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Nowadays, with the explosive growth of sensor-based devices connected to Internet of Thing (IoT), massive amount of data are generated every day with potential tremendous value. We argue that the value of those data can be extracted through monetize data platform in IoT-Edge-Cloud ecosystems for many parts of the business. In such monetize data platform, the data can be computed and transformed into services in IoT-Edge-Cloud ecosystems and provide Data-As-A-Service (DAAS) for applications. The key to implement such a monetize data platform is to evenly distribute DAAS computing tasks to network devices to maximize the benefits of the system. So, in this paper, we study the Task Type-based Computation Offloading algorithm (TTCO) to implement such platform. We use the "IoT-Edge-Cloud" three-layer multi-hop model, which is closer to the complex scene in monetize data platform. We divide tasks into data-intensive tasks and CPU-intensive tasks, and then combine the cost model of computation offloading with task type to make data-intensive tasks prefer local computing and CPU-intensive tasks prefer offload computing, thereby reducing the monetize data platform transmission volume and improving the overall quality of computation offloading. We then use a hierarchical game model combined with fictitious play to solve the Nash Equilibrium (NE) of the system and obtain the mixed strategies of the devices. Finally, we propose a TTL-constrained flood strategy transmission mechanism to make the algorithm apply to practice. The experimental results prove that our algorithm has a large performance gain in various scenarios, which can be severed as a monetize data platform for IoT-Edge-Cloud ecosystems.

Previous theoretical studies have identified possible conditions for an undetectable false data injection attack (FDIA) vector that bypasses bad data detectors in power system state estimators (SE). The formulation of the FDIA vector has attracted many researchers to propose detection algorithms. However, neither the attack formulation nor the proposed detection algorithms have been experimentally proven or validated. In this paper, a framework for developing cybersecurity testbeds that evaluate FDIA vectors for utility-scale distributed energy resources (DERs) and SE in real-time is proposed and implemented. The testbed is a combination of: 1) simulation models of a utility-scale DER and distribution systems using OPAL-RT; 2) intelligent electronic devices (SEL-351S, SEL-311C and SEL-3355); 3) network devices; and 4) graphical user interface (LabVIEW). The combination of actual devices, modeled systems, and cyber-physical interfaces adds realistic constraints such as sampling error, network jitter, and data time alignment that exist during actual power system operations. Using the testbed framework, results show that the onset of the FDIA against a SE is experimentally detectable. Next, the testbed is used to expose an unknown vulnerability of existing phase locked loop (PLL) techniques by presenting a new class of attack that causes PLL reporting of false phase angle and frequency values to the DER controllers. This type of attack would then induce the DERs to perform an adverse control action. In this research, a new detection algorithm based on a Kalman filter is evaluated to detect this type of PLL attack. A detailed evaluation of various attack scenarios is presented. The results indicate the benefits of employing this type of testbed framework to improve the development of cybersecurity defenses for realistic inverter based DER environments.

Fingerprinting is a process of identifying the remote network devices and services running on the devices, including operating systems (OS) of the devices, and hosts running different OSs. Several research proposals and commercial products are available in the market to defeat fingerprinting. However, they have performance limitations and expose themselves to attackers. In this paper, we utilize some real-time fault-tolerance concepts (viz. real-time/dynamic, detection/locating, confinement/localizing and masking/decoy) to propose a plug-and-play adaptive middleware architecture called Secure Dynamic Middleware (SDMw) with a view to defeat attackers fingerprinting the network, without exposing itself to the attackers. We verify that the proposed scheme works seamlessly and requires zero-configuration at the client side.

Cloud computing applications have grown rapidly in the last decade, where many organizations are migrating their applications to cloud data center as they expected high performance, reliability, and the best quality of service. Data centers deploy a variety of technologies, such as software-defined networks (SDN), to effectively manage their resources. The SDN approach is a highly flexible network architecture that automates network configuration using a centralized controller to overcome traditional network limitations. This paper proposes an SDN-based monitoring algorithm to detect the performance anomaly and identify the bottleneck of the distributed application in the cloud data center using the support vector machine algorithm. It collects the data from the network devices and calculates the performance metrics for the distributed application components that are used to train the SVM algorithm and build a baseline model of the normal behavior of the distributed application. The SVM model detects performance anomaly behavior and identifies the root cause of bottlenecks using one-class support vector machine (OCSVM) and multi-class support vector machine (MCSVM) algorithms. The proposed method does not require any knowledge about the running applications or depends on static threshold values for performance measurements. Simulation results show that the proposed method can detect and locate the failure occurrences efficiently with high precision and low overhead compared to statistical methods, Naive Bayes Classifier and the decision tree machine learning method.

The global community networking movement promotes locally-managed network infrastructure as a strategy for affordable Internet connectivity. This case study investigates a group of collectively managed WiFi Internet networks in Argentina and the technologists who design the networking hardware and software. Members of these community networks collaborate on maintenance and repair and practice new forms of collective work. Drawing on Actor-Network Theory, we show that the networking technologies play a role in the social relations of their maintenance and that they are intentionally configured to do so. For technology designers and deployers, we suggest a path beyond designing for easy repair: since every breakdown is an opportunity to learn, we should design for accessible repair experiences that enable effective collaborative learning.

Software Defined Networking (SDN) is a new networking paradigm where forwarding hardware is decoupled from control decisions. It promises to dramatically simplify network management and enable innovation and evolution. In SDN, network intelligence is logically centralized in software-based controllers (the control plane), while network devices (OpenFlow Switches) become simple packet-forwarding devices (the data plane) that can be programmed via an open interface (OpenFlow protocol). Such decoupling of the control plane from the data plane introduces various challenges that include security, reliability, load balancing, and traffic engineering. Dreadful security challenges in SDNs are denial of service (DoS) and distributed denial of service (DDoS) attacks. For instance, in SDNs, DoS/DDoS attacks could flood the control plane, the data plane, or the communication channel. Attacking the control plane could result in failure of the entire network, while attacking the data plane or the communication channel results in packet drop and network unavailability. In this paper we deliver several contributions that shed light on the field of DoS/DDoS attacks in SDNs, providing a complete background about the area, including attacks and analysis of the existing solutions. In particular, our contributions can be summarized as follow: we review and systematize the state-of-the-art solutions that address both DoS and DDoS attacks in SDNs through the lenses of intrinsic and extrinsic approaches. Moreover, the discussed countermeasures are organized accordingly to their focus, be it on detection, mitigation, prevention, or graceful degradation. Further, we survey the different approaches and tools adopted to implement the revised solutions. Finally, we also highlight possible future research directions to address DoS/DDoS attacks in SDNs.

With the construction and improvement of 5G infrastructure, more devices choose to access the Internet to achieve some functions. People are paying more attention to information security in the use of network devices. This makes lightweight block ciphers become a hotspot. A lightweight block cipher with superior performance can ensure the security of information while reducing the consumption of device resources. Traditional optimization tools, such as brute force or random search, are often used to solve the design of Symmetric-Key primitives. The metaheuristic algorithm was first used to solve the design of Symmetric-Key primitives of SKINNY. The genetic algorithm and the simulated annealing algorithm are used to increase the number of active S-boxes in SKINNY, thus improving the security of SKINNY. Based on this, to improve search efficiency and optimize search results, we design a novel metaheuristic algorithm, named particle swarm-like normal optimization algorithm (PSNO) to design the Symmetric-Key primitives of SKINNY. With our algorithm, one or better algorithm components can be obtained more quickly. The results in the experiments show that our search results are better than those of the genetic algorithm and the simulated annealing algorithm. The search efficiency is significantly improved. The algorithm we proposed can be generalized to the design of Symmetric-Key primitives of other lightweight block ciphers with clear evaluation indicators, where the corresponding indicators can be used as the objective functions.

High level, network programming languages, like P4, enable students to gain hands-on experience in the structure of a switch or router. Students can implement the packet processing pipeline themselves, without prior knowledge of circuit design. However, when choosing a P4 programmable target for use in the classroom, instructors face a lack of options. On the one hand, software solutions, such as the behavioral model (BMv2) switch, are overly simplified and offer low performance. On the other hand, existing hardware solutions are closed source and expensive. In this paper, we present P4Pi, a new, low-cost, open-source hardware platform intended for networking education. P4Pi allows students to design and deploy P4-based network devices using the Raspberry Pi board, which has a price tag of less than many academic textbooks. We describe the high-level design of the P4Pi platform, offer some suggestions for how P4Pi could be used in the classroom, and present some additional use-cases for applications and functionality that could be developed using P4Pi.

Since the advent of network function virtualization (NFV), cloud service providers (CSPs) can implement traditional dedicated network devices as software and flexibly instantiate network functions (NFs) on common off-the-shelf servers. NFV technology enables CSPs to deploy their NFs to a cloud data center in the form of virtual network functions (VNFs) without costly capital expenditures and operating expenses. However, it is an essential but intractable issue for CSPs to devise a suitable VNF placement scheme to optimize network resource consumption and improve network performance. In this article, we focus on the VNF placement problem for mapping users’ service function chain requests (SFCRs) in cloud networks. To enhance network resource utilization, we consider the fundamental resource overheads and implementation method of VNFs. The VNF placement problem is formulated as an integer linear programming model with the aim of minimizing the total network resource consumption while guaranteeing the delay requirements of SFCRs. We devise a two-phase optimization solution (TPOS) to solve the problem. TPOS contains a mapping phase to map SFCRs on servers and an adjustment phase to optimize the placement of VNFs and VNF requests. Evaluation results demonstrate that TPOS can derive near-optimal server resource consumption and significantly enhance network resource utilization. TPOS can guarantee the delay requirements of SFCRs and outperform contrastive schemes in terms of activated servers, SFCR acceptance ratio, and average VNF utilization.

For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred. Considering a large number of optical components in an optical network and many digital signal processing modules in each optical transceiver, massive real-time data can be collected. However, for a traditional monitoring structure, collecting, storing and processing a large size of data are challenging tasks. Moreover, strong correlations and similarities between data from different sources and regions are not properly considered, which may limit function extension and accuracy improvement. To address abovementioned issues, a data-fusion-assisted telemetry layer between the physical layer and control layer is proposed in this paper. The data fusion methodologies are elaborated on three different levels: Source Level , Space Level and Model Level . For each level, various data fusion algorithms are introduced and relevant works are reviewed. In addition, proof-of-concept use cases for each level are provided through simulations, where the benefits of the data-fusion-assisted telemetry layer are shown.

With the elevation of terminal devices, network traffic has also grown with rapid speed. To relieve cloud computing constraints on timely delivery, energy consumption, and congestion, fog computing is introduced to provide proximate and spot-on services to network devices. In fog environment, resource-deprived user nodes can offload their multi-task to network periphery situated fog nodes. However, due to the decentralized and untrusted behavior of fog nodes; trade, and pricing related sensitive information can be tampered by an unauthorized entity for their benefits. In this paper, we design a joint resource allocation and pricing scheme using blockchain employed smart contract. Blockchain is used to overcome untrusted behavior by preventing the malicious node from tampering with trade information. However, a smart contract employed fair and secure payment among user nodes and fog nodes. Particularly, we propose descending combinatorial auction-based dynamic pricing schemes, in which any user node can request bundle of resources and fog nodes compete with each other to serve their request. Experimental results show that the proposed pricing scheme helps fog nodes to earn significant profit by providing their resource efficiently.

Software-defined networks have many benefits such as more control over the control plane and reduced operating costs through separating the control plane from the data plane in network equipment. One of the most critical problems in software-defined networks is a controller placement problem, which significantly influences its overall performance. The purpose of this problem is to determine the number of controllers required and how to assign switches. This paper attempts to solve this problem, aiming to reduce the network's operational cost and to improve their survivability and load balancing. Hence, we have tried to divide the network into several subdomains using segmentation. Then, we used multi-criteria decision-making methods to solve the controller placement in each subdomain. For this purpose, we considered criteria such as reliability rate, cost, delay, and processing capacity. To assign switches to controllers, we used the proposed mathematical model to minimize the objective function while observing the defined constraints. Furthermore, according to the proposed architecture, we used distance to the central controller, adjacent neighborhood, and controller memory to select the cluster head controller for each subdomain to communicate with the central controller. Finally, we performed experiments on topologies Uran, SwitchL3, and Sinet of the Internet Topology Zoo to evaluate the proposed method. In these experiments, we compared the results of the proposed method with our work-related methods, including Controller Placement Analytic Hierarchy Process (CPAHP) and Controller Placement Modified Density Peak (CPMDP), in terms of criteria such as cost, survivability, number of controllers, connection failure probability, average delay, and controller load-balancing rate. The results show that the proposed method outperforms CPMDP and CPAHP. Thus, the proposed method has a 24.97% and 19.76% improvement in reducing network implementation costs than CPMDP and CPAHP, respectively.

Intrusion detection is an important technique that can provide solid protection for the network equipment against the security attacks. However, the attacks are usually unbalanced in different types and the attacks of unknown classes may also occur with the growth of Internet construction. In this case, the traditional machine learning-based intrusion detection methods usually have inferior detection accuracy and high false-positive rates. To tackle this problem, in this article, we propose a novel deep learning-based intrusion detection method named log-cosh conditional variational autoencoder (LCVAE). It inherits the capability of the conditional variational autoencoder (CVAE) that can capture the complex distribution of observed data and generate new data with prespecified classes. Different from the traditional CVAE, to better model the discrete property in the intrusion data, we design an effective loss term using the log hyperbolic cosine (log-cosh) function in the proposed LCVAE method. It can well balance the generation and reconstruction procedures and is more effective to generate diverse intrusion data for the imbalanced classes. To improve the detection accuracy, we utilize the classification based on convolutional neural network to perform feature extraction and classification based on the observed and generated intrusion data. We conduct extensive experiments on the challenging data set NSL-KDD with large-scale intrusion data. The results show that the superior detection performance of the proposed LCVAE method comparing with several state-of-the-art intrusion detection methods, and also demonstrate the potentiality of generating new intrusion data with promising diversity.

Proliferation of large number of cloud users steered the exponential increase in number and size of the data centers. These data centers are energy hungry and put burden for cloud service provider in terms of electricity bills. There is environmental concern too, due to large carbon foot print. A lot of work has been done on reducing the energy requirement of data centers using optimal use of CPUs. Virtualization has been used as the core technology for optimal use of computing resources using VM migration. However, networking devices also contribute significantly to the responsible for the energy dissipation. We have proposed a two level energy optimization method for the data center to reduce energy consumption by keeping SLA. VM migration has been performed for optimal use of physical machines as well as switches used to connect physical machines in data center. Results of experiments conducted in CloudSim on PlanetLab data confirm superiority of the proposed method over existing methods using only single level optimization.

Software-Defined Vehicular Networks (SDVNs) have been a vital component to various radio access technologies that supports massive data loads in various security and infotainment applications. SDVNs elevate the constraints of static hardware network devices to programmable units and provide a global view of the network's status and a common interface between varied radio access technologies. However, having a logically centralized control unit comes with several challenges, including bottleneck issues and densification issues. The distributed control plane appears as a possible alternative to the centralized one yet raises questions concerning where to deploy control units, how many SDN controllers are needed in a given network structure, and when to migrate switches or change controllers. Therefore, the development of an efficient topology management entity is an important aspect that needs to be addressed. In this article, we define the main components necessary to develop and design an efficient SDVN topology management architecture. We evaluate the current solutions and methods of controllers' deployment and assignment for software-defined vehicle networks. We then discuss several challenges, design guidelines, open issues, and future directions for research related to the development of future adaptive software-defined vehicular networks.

Modeling network traffic is an endeavor actively carried on since early digital communications, supporting a number of practical applications, that range from network planning and provisioning to security. Accordingly, many theoretical and empirical approaches have been proposed in this long-standing research, most notably, Machine Learning (ML) ones. Indeed, recent interest from network equipment vendors is sparking around the evaluation of solid information-theoretical modeling approaches complementary to ML ones, especially applied to new network traffic profiles stemming from the massive diffusion of mobile apps. To cater to these needs, we analyze mobile-app traffic available in the public dataset MIRAGE-2019 adopting two related modeling approaches based on the well-known methodological toolset of Markov models (namely, Markov Chains and Hidden Markov Models ). We propose a novel heuristic to reconstruct application-layer messages in the common case of encrypted traffic. We discuss and experimentally evaluate the suitability of the provided modeling approaches for different tasks: characterization of network traffic (at different granularities, such as application, application category, and application version), and prediction of network traffic at both packet and message level. We also compare the results with several ML approaches, showing performance comparable to a state-of-the-art ML predictor (Random Forest Regressor). Also, with this work we provide a viable and theoretically sound traffic-analysis toolset to help improving ML evaluation (and possibly its design), and a sensible and interpretable baseline.

Recent technology evolution allows network equipment to continuously stream a wealth of “telemetry” information, which pertains to multiple protocols and layers of the stack, at a very fine spatial-grain and high-frequency. This deluge of telemetry data clearly offers new opportunities for network control and troubleshooting, but also poses a serious challenge for what concerns its real-time processing. We tackle this challenge by applying streaming machine-learning techniques to the continuous flow of control and data-plane telemetry data, with the purpose of real-time detection of anomalies. In particular, we implement an anomaly detection engine that leverages DenStream, an unsupervised clustering technique, and apply it to features collected from a large-scale testbed comprising tens of routers traversed up to 3Terabit/s worth of real application traffic. We contrast DenStream with offline algorithms such as DBScan and Local Outlier Factor (LOF), as well as online algorithms such as the windowed version of DBScan, ExactSTORM, Continuous Outlier Detection (COD) and Robust Random Cut Forest (RRCF). Our experimental campaign compares these seven algorithms under both accuracy and computational complexity viewpoints: results testify that DenStream (i) achieves detection results on par with RRCF, the best performing algorithm and (ii) is significantly faster than other approaches, notably over two orders of magnitude faster than RRCF. In spirit with the recent trend toward reproducibility of results, we make our code available as open source to the scientific community.

The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. Meanwhile, with the explosion of IoT devices and various applications, the expectations for the performance, reliability, and security of networks are greater than ever. The current end-host-based or centralized control framework incurs too much communication and computation overhead, therefore exhibiting tardiness and clumsiness in responding to network dynamics. Recently, with the advancement of programmable network hardware, it is possible to implement network functions inside the network. However, current in-network schemes are largely dependent on the manual process, which presents poor scalability and robustness. Therefore, in this article, we present a new intelligent network control architecture, in-network intelligence control. We design intelligent in-network devices that can automatically adapt to network dynamics by leveraging powerful machine learning adaptive abilities. In addition, to enhance the collaboration among distributed in-network devices, a centralized management plane is introduced to ease the training process of distributed switches. To demonstrate the technical feasibility and performance advantage of our architecture, we present three use cases: in-network load balance, in-network congestion control, and in-network DDoS detection.

Recently, smart cities, healthcare system, and smart vehicles have raised challenges on the capability and connectivity of state-of-the-art Internet-of-Things (IoT) devices, especially for the devices in hotspots area. Multiaccess edge computing (MEC) can enhance the ability of emerging resource-intensive IoT applications and has attracted much attention. However, due to the time-varying network environments, as well as the heterogeneous resources of network devices, it is hard to achieve stable, reliable, and real-time interactions between edge devices and their serving edge servers, especially in the 5G ultradense network (UDN) scenarios. Ultradense edge computing (UDEC) has the potential to fill this gap, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: 1) efficient utilization of multiple 5G resources (e.g., computation, communication, storage, and service resources); 2) low overhead offloading decision making and resource allocation strategies; and 3) privacy and security protection schemes. Thus, we first propose an intelligent UDEC (I-UDEC) framework, which integrates blockchain and artificial intelligence (AI) into 5G UDEC networks. Then, in order to achieve real-time and low overhead computation offloading decisions and resource allocation strategies, we design a novel two-timescale deep reinforcement learning (2Ts-DRL) approach, consisting of a fast-timescale and a slow-timescale learning process, respectively. The primary objective is to minimize the total offloading delay and network resource usage by jointly optimizing computation offloading, resource allocation, and service caching placement. We also leverage federated learning (FL) to train the 2Ts-DRL model in a distributed manner, aiming to protect the edge devices’ data privacy. Simulation results corroborate the effectiveness of both the 2Ts-DRL and FL in the I-UDEC framework and prove that our proposed algorithm can reduce task execution time up to 31.87%.

Security and privacy concerns have emerged as critical challenges in the Internet-of-Things (IoT) era. These issues need to be carefully addressed due to the sensitive data within IoT systems. However, some IoT devices have various limitations in terms of energy, memory capacity, and computational resources, which makes them extremely vulnerable to security attacks. Data integrity with source authentication are essential security services for protecting IoT data value and utility. Existing message authentication algorithms (MAAs), which are either based on block ciphers or keyed hash functions, require multiple rounds and complex operations, which leads to unacceptable overhead for resource-limited devices and delay-sensitive applications. Moreover, the high number of IoT connected devices generates a huge amount of data, which challenges even the capacity of powerful network devices to handle the security of such Big Data. As such, the protection of such amounts of generated data calls for lightweight security solutions. In this paper, we propose a lightweight MAA that provides data integrity and source authentication. The proposed solution is based on a dynamic key structure with a single round and simple operations. The used cryptographic primitives (substitution and permutation tables) are dynamic and get updated for each new input message by using specific update primitives. The dynamic structure of the proposed MAA allows for decreasing the required number of rounds to just one, while maintaining a high degree of security. The security tests results show that the proposed keyed hash functions (1) achieve the desired cryptographic properties, (2) are immune against existing attacks and (3) require low overhead in terms of computational and storage resources.

One of the most critical challenges of the Internet of Things (IoT) is to provide real-time services. Therefore, to provide a secure, efficient, and stable communication platform in the Internet of Things, emerging architectures such as software-defined networks (SDN) being significant. This paper proposes a comprehensive SDN based fault-tolerant architecture in IoT environments. In the proposed scheme, a mathematical model called Shared Risk Link Group (SRLG) calculates redundant paths as the main and backup non-overlapping paths between network equipment. In addition to the fault tolerance (FT) discussion in the proposed scheme, service quality is considered in the proposed scheme. Putting the percentage of link usage and the rate of link delay in calculating link costs makes it possible to calculate two completely non-overlapping paths. The end-to-end delay and the degree of link congestion are minimal. We compare our proposed scheme with two policies for building routes from source to destination. The simulation results indicate that, while reducing the error recovery time, the proposed method leads to improved services quality parameters such as packet loss, delay, and packet jitter. The results show that in case of a link failure in the network, the recovery time in large scenarios is a maximum of 16 ms, which improved by 20%, compared to the disjoint paths (DP) method. Our Approach can decrease packet loss by approximately 30% compared to the Dijkstra’s algorithm and roughly 24% compared to the DP method. Also, SLRG reduces latency by approximately 36% compared to the Dijkstra’s algorithm and roughly 19% compared to the DP method. Last, the proposed scheme reduces Jitter by approximately 49% compared to the Dijkstra’s algorithm, and roughly 26% compared to the DP method.

Software defined network (SDN) has attracted great interests as an emergent paradigm which aims to centralize the configuration of network devices by decoupling control layer and data layer. One considerable challenge in SDN is to protect against multiple attacks generated by distributed denial of service (DDoS) bots which attempt to make SDN controllers unavailable. The goal of this research is to propose a novel detect and mitigate DDoS attack in SDN controllers using traffic monitoring. Besides the advantages of queueing theory based model is exploited to evaluate the arrival flows and leveraging robust features and entropy, a distance-based classification is designed accurately to detect malicious packets from legitimate packets. The experimental results vividly demonstrate that our proposed detection scheme effectively yields high accuracy as well as high-efficiency controller utilization.

SDN enables a new networking paradigm probable to improve system efficiency where complex networks are easily managed and controlled. SDN allows network virtualization and advance programmability for customizing the behaviour of networking devices with user defined features even at run time. SDN separates network control and data planes. Intelligently controlled network management and operation, such that routing is eliminated from forwarding elements (switches) while shifting the routing logic in a centralized module named SDN Controller. Mininet is Linux based network emulator which is cost effective for implementing SDN having in built support of OpenFlow switches. This paper presents practical implementation of Mininet with ns-3 using Wi-Fi. Previous results reported in literature were limited upto 512 nodes in Mininet. Tests are conducted in Mininet by varying number of nodes in two distinct scenarios based on scalability and resource capabilities of the host system. We presented a low cost and reliable method allowing scalability with authenticity of results in real time environment. Simulation results show a marked improvement in time required for creating a topology designed for 3 nodes with powerful resources i.e. only 0.077 sec and 4.512 sec with limited resources, however with 2047 nodes required time is 1623.547 sec for powerful resources and 4615.115 sec with less capable resources respectively.

The industrial scenario is undergoing exponential changes, mainly due to the different technologies that emerge quickly and the ever increasing demand. As a consequence, the number of processing devices and systems in the industries’ architectures is also increasing. Entities connectivity, physical/virtual joint functioning, interactivity, interoperability, self-organization, smart decision making, among other factors are fundamental to foster Industry 4.0 (I4.0) potential. We believe that Cyber Physical System (CPS) and Industrial Internet of Things (IIoT) will have a major role in the emerging I4.0. In this context, researchers and experts from major factories are exploring these technologies in order to keep up with this digital transformation, developing IIoT systems and CPS architectures capable of connecting network devices from different information and communications technologies (ICT) systems, virtualizing the companies’ assets and integrating them with other manufacturing sectors and companies. This article performs a survey covering the main CPS architecture models available in the industrial environment, emphasizing their key characteristics and technologies, as well as the correlations among them, pointing objectives, advantages and contribution for the IIoT introduction in I4.0. It also provides a literature review covering projects from CPSs and IIoT point-of-view, identifying main technologies employed in current state-of-the-art and how they can meet the I4.0 key features of vertical and horizontal industrial integration. Finally, the article points requirements for current and future challenges, limitations, gaps and necessary changes in the CPS architectures in order to improve and introduce them in the I4.0 scenario.

The increasing adoption of the Industry 4.0 paradigm encompasses digitally interconnected factories which enables many advantages. However, it is still necessary to dedicate effort towards investigating protection mechanisms against cyberattacks in these scenarios. Despite the power demonstrated by Anomaly Detection-based Intrusion Detection Systems in industrial scenarios, their vulnerabilities to adversarial attacks, especially to evasion attacks, make Machine Learning and Deep Learning models ineffective for real scenarios. These type of attacks craft samples misclassified by the Intrusion Detection System and potentially reach industrial devices, causing potentially damaging impacts to factory workers and industry resources. Adversarial attacks linked to industrial scenarios are currently in early stages of development, hence most of them have the capability to craft samples misclassified by the IDS but not reach industrial devices. In this work, we present a new adversarial attack named Selective and Iterative Gradient Sign Method that overcomes the limitation of the adversarial attacks present in the literature. To complement this work we also detail a study of how the detection rate of an Intrusion Detection System is degraded and the time required by each technique to generate adversarial samples. The experiments were carried out using a dataset named Electra, collected from an Electric Traction Substation, and showed that adversarial attacks evaluated crafted samples misclassified by the IDS. However, only the method we proposed generated samples that can be understood by intermediate network devices and, therefore, reach their destination. Our experiment outputs demonstrate a lower period of time to achieve and craft adversarial samples using out our iterative based process method as opposed to other current iterative methods currently available.

In the last decade, many ransomware attacks had the ability to spread within local networks or even outside them. At the same time, software defined networking (SDN) has provided a major boost to networks by transferring intelligence from network devices to a programmable logically centralised controller. The latter can be programmed to be compatible with the requirements of a wide range of networks and environments in a straightforward manner. This has motivated researchers to design SDN-based security solutions against threats targeting traditional networks and systems. This article investigates the use of SDN to detect and mitigate the risk of self-propagating ransomware. The infamous BadRabbit ransomware has been used for the proof of concept. To achieve this, an extensive analysis of BadRabbit was performed to identify its characteristics and understand its behaviour at both the infected device level and at the network level. As a result, several unique artifacts were extracted from BadRabbit, which could facilitate its detection. These artifacts were relied upon to design an SDN-based intrusion detection and prevention system. Our system comprises five modules, namely deep packet inspection, ARP scanning detection, packet header inspection, honeypot, and SMB checker. The first two modules have been inspired by other works and have been included for comparison with the existing solutions. Three other modules rely on novel SDN-based methods for ransomware detection. We have also evaluated the efficiency and the performance of our system in terms of detection time, CPU utilisation, as well as TCP and ping latency. Finally, the proposed approach has also been tested for other ransomware families, such as WannaCry and NotPetya. Our experimental results show that the system is effective in terms of detecting self-propagating ransomware and outperforms other proposed approaches.

As networks grow larger in size and complexity, their monitoring is becoming an increasing challenge because of the required polling performance and also due to heterogeneity of devices. As it turns out, SNMP (Simple Network Management Protocol) is by far the most popular monitoring protocol. However, due to the increase in the number of network devices, it becomes necessary to employ multiple SNMP managers, which is not cost-effective due to the hardware requirements. Additionally, the different proprietary SNMP implementations require custom configuration very often, as new devices are being incorporated into the network. Therefore, current SNMP managers not only require capabilities for large-scale monitoring but also a high degree of flexibility and programmability. In response, we propose an SNMP manager architecture with a flexible multi-threaded architecture, which effectively reduces the hardware resources necessary to poll the increasing number of SNMP agents. In addition, it features a scripting component to deal with the different data representations caused by proprietary implementations. Our experience has shown that SNMP agents can have high variability in their response times. Actually, our findings show a strong correlation between high response times and CPU load. As a solution, we propose and analyze novel adaptive polling algorithms that decrease the load on agents' CPUs while keeping the desired polling rate for fast agents. Finally, we present several real-world use cases where we show the benefits of the polling algorithms and the scripting component, by means of extensive measurement campaigns, This work was supported by Ayudas para la formación de doctores en empresas, Doctorados Industriales, under Grant DI-16-08979

Applications and technologies of the Internet of Things are in high demand with the increase of network devices. With the development of technologies such as 5G, machine learning, edge computing, and Industry 4.0, the Internet of Things has evolved. This survey article discusses the evolution of the Internet of Things and presents the vision for Internet of Things 2.0. The Internet of Things 2.0 development is discussed across seven major fields. These fields are machine learning intelligence, mission critical communication, scalability, energy harvesting-based energy sustainability, interoperability, user friendly IoT, and security. Other than these major fields, the architectural development of the Internet of Things and major types of applications are also reviewed. Finally, this article ends with the vision and current limitations of the Internet of Things in future network environments.

The power consumed by the Internet industry has been growing sharply in recent years, which accounts for a considerable proportion of the overall power consumption. To reduce high power consumption in the network, numerous proposals on power-saving are proposed. However, they usually reduce power consumption by dynamically sleeping and awakening the network device according to the current traffic fluctuation. When the traffic fluctuate sharply, the network device will swing between sleep and active state frequently, which consumes a considerable power and causes a series of side effects, such as packet losses, short sustainable device service life and synchronization among devices. In this paper, based on Software Defined Networking (SDN), we propose a Power Optimization with Less State Transition (POLST) framework, which mainly achieves the following two functions: (1) Real-time network monitoring with high accuracy and low overhead; (2) Power optimization with less device state transition. Then, we formulate optimization problem of minimizing the network power consumption, while considering power consumed by device state transition. To solve it, we propose a Power Aware Routing with Less State Transition (PARLST) algorithm. Finally, simulation results show that compared with the benchmarks, POLST has excellent performance in both network monitoring and network power optimization. More specifically, in the network monitoring, POLST can achieve up to 97.2% measurement accuracy with low monitoring overhead. In the power optimization, in comparison to the benchmarks, POLST can achieve up to 72.2% energy efficiency, 56.6% decrease in Switch State Transition (SST), 53.5% decrease in Link State Transition (LST).

The Internet of Everything (IoE) is the intelligent connection of people, process, data and things. In addition to machine-to-machine communications as facilitated in traditional networks and the Internet of Things, IoE enables machine-to-people and technology-assisted people-to-people interactions. An IoE local network may comprise various devices, including resource-constrained devices such as sensors, actuators, and embedded and wearable devices. The protection of such a network (and the data collected by and transmitted between network devices) against security attacks is highly challenging. First, the devices may be deployed in open environments without physical protection. More severely, the security protocols developed for traditional computer networks are ineffective or even inapplicable due to the resource-constrained nature of the end devices in IoE. In this article, we discuss the security challenges and requirements for data communications between IoE devices. We show that a suite of security protocols developed recently may provide an adaptive and resource-ef-ficient solution to IoE security.

Wireless Local Area Networks (WLANs) are one of the most popular networks for the Internet-of-Things (IoT) applications. Among various WLAN technologies, the Long-Range WAN (LoRaWAN) has gained a high demand in recent years because of its low power consumption and long-range communication. However, the Long-Range (LoRa) network suffers from interference problem among LoRa Devices (LDs) that are connected to the LoRa gateway by using the same Spreading Factors (SFs). In this article, we propose a game theory-based approach for estimating the time duration of transmission of data on suitable SFs such that interference problem is reduced and network devices maximize their utilities. We next propose a scheduling algorithm that schedules the allocated time duration on the SFs such that the waiting time of the network can be minimized. We finally use the network simulator-3 for validating the propose work. Various experiments are performed which demonstrate the improvement in the network performance.

International audience; In order to improve locality aspects, new Cloud-related architectures such as Edge Computing have been proposed. Despite the growing popularity of these new architectures, their energy consumption has not been well investigated yet. To move forward on such a critical question, we first introduce a taxonomy of different Cloud-related architectures. From this taxonomy, we then present an energy model to evaluate their consumption. Unlike previous proposals, our model comprises the full energy consumption of the computing facilities, including cooling systems, and the energy consumption of network devices linking end users to Cloud resources. Finally, we instantiate our model on different Cloud-related architectures, ranging from fully centralized to completely distributed ones, and compare their energy consumption. The results show that a completely distributed architecture, because of not using intra-data center network and large-size cooling systems, consumes between 14% and 25% less energy than fully centralized and partly distributed architectures respectively. To the best of our knowledge, our work is the first one to propose a model that enables researchers to analyze and compare energy consumption of different Cloud-related architectures.

Different versions of Bluetooth Low Energy (BLE) have been standardized with point-to-point and broadcast topologies. Recently Bluetooth mesh specification provides BLE with mesh topology, highly demanded by Internet of Things (IoT) and numerous applications such as smart cities or Industry 4.0. Bluetooth mesh emerges as one of the most promising low-power IoT standards, enabling many-to-many device communications and optimising the deployment of large-scale device networks. Our research focuses on addressing the significant real-life problem of interoperability, the integration of devices with different topologies and versions of BLE to permit mesh transmissions. This integration will permit to increase the coverage area, to diminish important drawbacks in communication and power consumption and to save money by reducing the number of new devices. We have implemented and evaluated the Bluetooth mesh and the provisioning procedure, which provides network devices with all the necessary data to communicate with the rest of devices in the mesh network. Moreover, the Lightweight Provisioning, our proposal to improve the provisioning procedure, is presented. Our implementation allows Bluetooth non-mesh devices to be provisioned and to take part in a Bluetooth mesh network, making possible to continue using current devices. Performance was evaluated by a set of experiments across two steps: First, the provisioning time was measured for different configurations, including our proposal. Second, we measured the end-to-end delay and the packet reception rate in the lowest compatible BLE version. This work demonstrates real interoperability among devices with different BLE versions, a low end-to-end delay in a small and medium-scale mesh networks and a high packet reception rate for advertising intervals greater than 100 ms.

Large-scale datacenters are the key infrastructures of cloud computing. Inside a datacenter, a large number of servers are interconnected using a specific datacenter network to deliver the infrastructure as a service (IaaS) for tenants. To realize novel cloud applications like the network virtualization and network isolation among tenants, the principle of software-defined network (SDN) has been applied to datacenters. In the setting, multiple distributed controllers are deployed to offer a control plane over the entire datacenter to efficiently manage the network usage. Despite such efforts, cloud datacenters, however, still lack a scalable and resilient control plane. Consequently, this paper systematically studies the coverage problem of controllers, which means to cover all network devices using the least number of controllers. More precisely, we tackle this essential problem from three aspects, including the minimal coverage, the minimal fault-tolerant coverage, and the minimal communication overhead among controllers. After modelling and analyzing such three problems, we design efficient approaches to approximate the optimal solution, respectively. Extensive evaluation results indicate that our approaches can significantly save the number of required controllers, improve the fault-tolerant capability of the control plane and reduce the communication overhead of state synchronization among controllers. The design methodologies proposed in this paper can be applied to cloud datacenters with other networking structures after minimal modifications.

In the context of wide area networks (WANs) and software defined networking (SDN), reducing the communication delays experienced by the network devices is an important challenge whose solution requires a careful placement of controllers to decrease the end-to-end latencies. Although the majority of studies focusing on the controller placement problem (CPP) only consider the switch–controller propagation delays and inter-controller latencies, the controllers’ capacity needs to be addressed for lowering the end-to-end delays. While the controllers with a variety of processing rates, number of ports, and costs are available in the market, Internet Service Providers (ISPs) need to consider the affordability and capability of compensating their needs by maximizing the use of their networks’ resources. To propose a solution for this important problem, we consider the Knapsack 0–1problem and formulate the Garter Snake Optimization Capacitated Controller Placement Problem (GSOCCPP), a meta-heuristic algorithm, with new iterations and temperate mating conditions to solve the CPP. This algorithm uses a reasonable amount of computation time to obtain the minimum delays. A number of Topology-Zoo datasets are analyzed with a variety of small to large scale data plane nodes to evaluate the GSOCCPP algorithm based on different controllers’ capacities. The simulation results demonstrate that, in addition to outperforming similar meta-heuristic and clustering algorithms such as the Firefly Algorithm, Particle Swarm Optimization, and the k-means++, our newly proposed GSOCCPP algorithm is successful in achieving the lowest execution time among the analyzed algorithms. Furthermore, this proposed solution has a more efficient memory consumption compared to other algorithms for controller placement in different network topologies.

In this study, a new non-local active compensation method is developed for a multi-microgrid (MMG) system. The current industry practice is to utilise local harmonic current and reactive power compensation methods, however, local compensation methods are not practical for large MMG system with widely dispersed non-linear loads, because each non-linear load would require its own compensator. To overcome this problem, a novel compensating technology called a series–shunt network device (SSND) is installed between a pair of microgrids in the proposed MMG system. The SSND reduces the number of local compensators required while also performing additional functions in comparison with conventional devices. For effective control of the SSND, an improved model predictive control (MPC) algorithm, which gives better tracking accuracy and faster set-point change than that of a conventional proportional–integral controller, is also presented. Analysis and simulations verify the capability of SSND in performing both local and non-local active compensation of harmonic current and reactive power in the proposed MMG system under various test scenarios. Simulation results show that the MPC-controlled SSND can achieve effective compensation, thereby resulting in a very low current total harmonic distortion value of 2.4% and a unity power factor at the distribution grid side.

With the increasing number of emerging robust networks, the challenges to design new security protocols and techniques are never ending. With the enlargement of 5G paradigm, there is a remarkable makeshift in how the distributed devices perform to achieve a common goal. The 5G technology has been significantly revolutionized by the advent of the Internet of Things, and its quick and pervasive evolution. However, it remains imperative that all these devices work in a sequential manner to execute a collective and secure operation. Despite computing being focused around cloud infrastructures in the last two decades, the 5G networks and the huge amount of data it generates is presently inverting this trend, shifting computing power back to where data is generated from. To encompass different autonomous and collaborative systems with functions of data sensing and authentication between such systems in a predictive and adaptive manner is a challenging task. Wireless network design in 5G is expected to emphasize broadly on three facets: security, privacy and energy efficiency. Therefore, this paper investigates the role of various data security and privacy techniques for different generation networks, but emphasis is on 5G. It addresses encryption schemes with the focus on the challenges faced such as side channel and ciphertext attacks, inherent key generation, sharing and distribution, key escrow, analysis of various overheads, and potential techniques, and solutions to address such challenges by leveraging 5G network devices. With the proliferation of ubiquitous computing and wide adoption of hybrid encryption techniques, various attacks will be circumvent in an intelligent manner.

Unprecedented levels of device connectivity and the emergence of futuristic digital services are driving fundamental changes to underlying fixed and wireless data transport networks. Projected bandwidth requirements, coupled with increased network centralization and virtualization, will lead to the convergence of data-center, fixed and wireless systems, and a greater onus being placed on the optical routing/transport portion of these networks. Such converged networks will require the development of optical technologies capable of servicing a multitude of network user types. In this work, we propose the use of a Silicon Photonic (SiP) space-and-wavelength selective switch fabric as a flexible wavelength provisioning platform for converged optical networks. The envisaged converged network is presented and an experimental test-bed which demonstrates flexible Cloud Radio Access Networking (C-RAN), using a 4 × 4 micro-ring resonator based switch, is described. Successful provisioning and transmission of Wavelength Division Multiplexed (WDM) based analog Radio-over-Fiber (RoF) services, over 10 km of fiber, in the converged test-bed is demonstrated and evaluated in terms of the received Bit Error Rate (BER) and Error Vector Magnitude (EVM). Furthermore, the wavelength multi-casting capabilities of the SiP switch is shown to enable dynamic resource allocation in the optical domain, and this is highlighted through the experimental implementation and evaluation of two C-RAN use cases - representing high and low mobile traffic demand scenarios.

To ensure the quality of service of an end-to-end connection, current network solutions are mostly dependable on the differentiation between different classes of traffic. The Software-defined networking (SDN) architecture has emerged to offer network programmability, giving to network operators a programmatic control over their network. In SDN, network devices are programmed in many ways, having a standard, open, and vendor-agnostic interface, e.g., OpenFlow, enabling the control plane to instruct the forwarding behavior of network devices from different vendors. In this paper, we introduce the Programmable Labels (ProgLab) approach to support traffic differentiation with QoS guarantees as a low-cost alternative built over an SDN architecture. The idea relies on the simplification of a packet-forwarding operation which relies on the remainder of a division, instead of classical table lookup method. ProgLab computes programmable label at the control plane by solving a congruence system from Residue Number System and the co-prime numbers assigned to the switches in the path of an end-to-end connection. Such label has a meaning within this network that expresses the entire route, addressing the respective traffic class at each switch’s logical queue along the path. ProgLab approach has been implemented through the P4 language and evaluated through an emulation-based evaluation. The experiments demonstrated the feasibility of ProgLab and showed its ability in providing QoS differentiation on demand.

By decoupling network functions from dedicated, proprietary hardware network devices, Network Function Virtualization (NFV) allows building Virtual Network Functions (VNFs) that can run on standard, commodity servers to reduce cost and gain flexibility in network deployment, operation, and management. However, building VNFs with high-throughput and low-latency is a big challenge. In this paper, we propose eVNF - a hybrid fast-slow path architecture to build and accelerate VNFs with eXpress Data Path (XDP), which is a Linux kernel framework that enables high performance and programmable network processing. The programmability of XDP is limited to ensure kernel safety, thus causing difficulties when using XDP to accelerate VNFs. eVNF solves this problem by taking a hybrid approach: leave the simple but critical tasks inside the kernel with XDP, and let complex tasks be processed outside XDP, e.g., in user-space. With the hybrid architecture, eVNF allows building fast and flexible VNFs. We applied eVNF to build four prototype VNFs: Flow Monitoring (eFM), Firewall (eFW), Deep Packet Inspection (eDPI), and Load Balancer (eLB). These VNFs are evaluated individually and in service function chains (SFCs) using OpenStack. Our experiments showed that eVNF can significantly improve service throughput as well as reduce latency and CPU usage. eVNF-based VNFs also can scale out with the number of CPU cores and can combine with Open vSwitch - Data Plane Development Kit (OvS-DPDK) for better performance.

Self-driving networks represent the next step of network management techniques in the close future. A fundamental point for such an evolution is the use of Machine Learning based solutions to extract information from data coming from network devices during their activity. In this work we focus on a new type of data, available thanks to the definition of the novel SRv6 paradigm, referred to as SRv6 Traffic Counters ( SRTCs ). SRTCs provide aggregated measurements related to forwarding operations performed by SRv6 routers. In this work a detailed description of different SRTCs types (SR.INT, PISD, PSID.TM and POL) is provided and their relationships is formalized. The theoretical framework deployed is used to identify, on the basis of network configuration parameters of both SRv6 and IGP protocols, the minimum set of independent SRTCs to characterize the Network Status : we show that about the 80% of counters can be neglected with no information loss. We also apply our framework to two use cases: i) Traffic Matrix (TM) Assessment and ii) Traffic Anomaly Detection. For the TM assessment, we show that in a partially deployed SRv6 scenario a specific type of SRTCs , i.e., PSID, is more reliable than other ones; on the contrary, in a fully deployed scenario POL and PSID.TM counters provide the full TM knowledge. For the Traffic Anomaly Detection case, we show that known solutions based on link load measurements can be improved when integrating SRTCs information.

The unprecedented growth in data volume results in an urgent need for a dramatic increase in the size of data center networks. Accommodating millions to even billions of servers in the structure of data center networks, while providing highly-efficient interconnecting architectures, is of prominent importance nowadays. Generally, high scalability, noticeable fault-tolerance, great network capacity, limited delay, reasonable power consumption, and justifiable construction cost have to be listed as the principal design goals of well-organized cloud data center networks. In this paper, Platonica, a novel dual-centric and recursively defined architecture, is proposed. Platonica is constructed by employing fault-tolerant building blocks inspired by the edge connection pattern of five varieties of Platonic solids that turns the proposed architecture to a flexible scheme. It also escapes the adoption of any leveling policy for the network equipment, which leads to the efficient usage of all the employed resources. Moreover, to ameliorate the routing efficiency and the network fault-tolerance, two exclusively designed routing algorithms are presented. Both theoretical analysis and simulation results demonstrate that Platonica is highly fault-tolerant and can provide notable network capacity, good average path length, and low latency to support delay-sensitive and data-intensive applications. It can also be considered as a cost-effective and power-efficient network architecture. Altogether, Platonica achieves a good balance among all the major goals of designing cloud data center networks.

Mobile Edge Computing (MEC) provides different storage and computing capabilities within the access range of mobile devices. This moderates the burden of offloading compute/storage- intensive processes of the mobile devices to the centralized cloud data centers. As a result, the network latency is reduced and the quality of service provided for the mobile end users is improved. Different applications benefit from the large-scale deployments of MEC servers. However, the considerable complexity of managing large scale deployments of the sheer number of applications for the millions of mobile devices is a challenge. Recently, Software Defined Networking (SDN) is leveraged to resolve the problem by providing unified and programmable interfaces for managing network devices. Most of the current SDN packet processing services are tightly dependent on the packet classification service. This primary service classifies any incoming packet based on matching a set of specific fields of its header against a flow table. Acceleration of this basic process considerably increases the performance of the SDN-based MEC. In this paper, the hierarchical tree algorithm, which is a packet classification method, is parallelized using popular platforms on a cluster of Graphics Processing Units (GPUs), a cluster of Central Processing Units (CPUs), and a hybrid cluster. The best scenario for the parallel implementation of this algorithm on the CPU cluster is that which combines OpenMP and MPI. In this case, the throughput of the classifier is 4.2 million packets per second (MPPS). On the GPU cluster, two different scenarios have been used. In the first scenario, the global memory is used to store the rules and the Hierarchical-trie of the classifier while in the second scenario we break the filter set in a way that the resulting Hierarchical-trie of each subset could be stored in the shared memory of GPU. According to the results, although the first GPU cluster scenario achieves a throughput of 29.19 MPPS and a speedup 58 times as great as the serial mode, the second scenario is 12 times faster due to using the shared memory. The best performance, however, belongs to the hybrid cluster mode. The hybrid cluster achieves a throughput of 30.59 which is 1.4 MPPS more than the GPU cluster.

Software defined networking (SDN) is a networking paradigm that allows for the easy programmability of network devices by decoupling the data plane and the control plane. On the other hand, Distributed Denial of Service (DDoS) attacks remains one of the major concerns for organizational network infrastructures and Cloud providers. In this article, the authors propose a Neural Network based Traffic Flow Classifier (TFC-NN) for live DDoS detection in SDN environments. This study provides a live traffic analysis method with a neural network. The training of the TFC-NN model is performed by a labelled dataset constructed from SDN normal traffic and an-under DDoS traffic. The study also provides a live mitigation process combined with the live TFC-NN-based DDoS detection. The approach is deployed and evaluated on an SDN architecture based on different performance metrics with different under-DDoS attack scenarios.

The rapid advancement of network, communication and Internet technology resulted with always-on, always-connected, device-independent and remote online working, business, education and entertainment environment. Consequently, users are searching for solutions and technologies that enable fast and reliable wide area network connection and the typical solution is through using personal computers connected with ethernet cable to network equipment and infrastructure that supports gigabit ethernet connection. Besides the complex network infrastructure that can influence performance, the bottleneck can also be caused by insufficient hardware, operating system and software resources on clients’ machines. Therefore, in this paper a networking performance evaluation of three globally most common and most used versions of Windows operating systems; namely Windows 7TM, Windows 8.1TM and Windows 10TM, on two identical computer systems, is conducted. Networking performance measurements are performed with three different benchmarks: namely iPerf, D-ITG and NetStress. Performance evaluation results showed that a newer versions of an operating system bring certain networking performance improvements but by sacrificing other performances.

With the move from traditional hardware appliance based network functions to Network Function Virtualization, software development is decoupled from the hardware. However, as a network function is no longer optimized for hardware, beneficial features of networking hardware may not be used any more. Solutions such as SDN or NIC offloading aim to overcome this antipodes by integrating networking hardware into the packet processing pipelines. On the one hand, offloading traffic of network functions to hardware can increase throughput and reduce resource consumption. On the other hand, the number of parallel flows in a network can be very high, exhausting the capacity of the tables of the networking hardware and force the system to fall back to software processing. Fortunately, it is known that a large portion of the flows in the Internet are mice flows, whereas the majority of the traffic is constituted by elephant flows. If the elephant flows can be detected efficiently, the hardware tables can be used more efficiently as a larger share of the traffic can be offloaded. We introduce a machine learning based approach that takes its decision with the first packet of a flow. A fundamentally different approach is using packet sampling for the offloading decision. We are evaluating both approaches in terms of complexity, offloaded share of the traffic and table occupation. The results show that a machine learning based offloading decision is possible with the first packet. The sampling approach only reaches a comparable performance at very high sampling rates.

Recent years have seen the development of successful internet of things (IoT) technologies based on IP-enabled data networks. Despite the growing demand for security of resource-constrained data service networks, a majority of nodes in them typically lack memory space and computational ability compared to Internet nodes. This restricts constrained nodes from implementing and operating burdensome security protocols and authentication methods. To overcome this problem, we propose a security offloading network system which expands the security coverage to constrained nodes in IPv6-based heterogeneous networks. The proposed system supports constrained nodes by offloading the handshake and data transmission of a security protocol to offloading agents. Constrained nodes can also utilize the ciphersuites of offloading agents without the burden of deploying them. The offloading server controls the registration, the authentication, and the load-balanced allocation of offloading-agents. The implementation and evaluation show the feasibility of the proposed system by presenting practical network performance analyses in respect of availability, responsiveness, protocol overhead, and validation. This research would be advantageous for constrained nodes to achieve a high level of security, lowering the cost of network devices at the same time.

Over the decades, as the technology of Internet thrives rapidly, more and more kinds of cyber-attacks are blasting out around the world. Among them, botnet is one of the most noxious attacks which has always been challenging to overcome. The difficulties of botnet detection stem from the various forms of attack since the viruses keep evolving to avoid themselves from being found. Rule-based botnet detection has its shortcoming of detecting dynamically changing features. On the other hand, the more the Internet functionalities are developed, the severer the impacts botnets may cause. In recent years, many network devices have suffered from botnet attacks as the Internet of things technology prospers, which caused great damage in many industries. Consequently, botnet detection has always been a critical issue in computer security field. In this paper, we introduce a method to detect potential botnets by inspecting the behaviors of network traffics from network packets. In the beginning, we sample the given packets by a period of time and extract the behavioral features from a series of packets. By analyzing these features with proposed deep learning models, we can detect the threat of botnets and classify them into different categories.

This article presents a coarse-grained reconfigurable cryptographic logic array named PVHArray and an intelligent mapping algorithm for cryptographic algorithms. We propose three techniques to improve energy efficiency without affecting performance. First, the coarse-grained pipeline variable reconfigurable operation units balance the system critical path delay and number of algorithm operations to ensure the best performance. Second, the hierarchical interconnect network overcomes the shortcomings of a single network, providing PVHArray with good interconnectivity and scalability while managing the network hardware resource overhead. Third, the distributed control network supports accurate period-oriented control with a lightweight hardware structure, preserving hardware resources for other performance enhancements. We combine these advances with deep learning to propose a type of smart ant colony optimization mapping algorithm to improve algorithm mapping performance. We implemented our PVHArray on a 12.25 mm2 silicon square with 55-nm CMOS technology, with each algorithm working at its optimum frequency. Experiments show that PVHArray improved performance by about 12.9% per unit area and 13.9% per unit power compared with the reconfigurable cryptographic logic array REMUS_LPP and other state-of-the-art cryptographic structures. For cryptographic algorithm mapping, our smart ant colony optimization (SACO) algorithm reduced compilation time by nearly 38%. Finally, PVHArray supports a variety of types of cryptographic algorithms.

The rapid development of communications networks facilitate our lives but bring severe energy consumption problems. As an emerging network structure, software-defined networking can realize flexible scheduling of network traffic demands. In this paper, we exploit the above feature and adopt the idea that is powering down unnecessary links and improving links bandwidth’s utilization to formulate an optimization problem. It can realize reducing network costs while satisfying traffic demands and network resources. We propose a heuristic algorithm based on the maximum bandwidth utilization of activated links to solve this optimization problem. It aims to minimize energy costs by improving link utilization and turning on network devices as few as possible. Besides, we propose to divide all networks in a library of test instances for Survivable fixed telecommunication Network Design into four categories according to the network complexity. Finally, we choose eight different sizes of networks to verify our algorithm. The results show that the proposed algorithm can realize energy-efficient and improve the energy-saving rate by 0.34–19.69% on average by improving the utilization of active links.