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Software defined network (SDN) and network function virtualization (NFV) are the two key technologies that support a large scale 5G environment composed of bunches of users. In this paper, the intrusion detection system (IDS) is provisioned in each layer to completely drop the malicious packets that have escaped in prior layers. The four-layer architecture presented in this work is the perception layer, data plane layer, control plane layer, and application layer. Each layer mitigates particular attacks that peculiarly participate in the network. Classification of attacker packets is handled by analyzing flow features and packet features. Initially, the 5G users are authenticated using the Prince algorithm with identity, password, MAC address, location, and physically unclonable function. Then optimal switches are selected to overwhelm flow table overloading attack by sea lion optimization algorithm. The flow rules in switches are secured through blockchain that maintains PHOTON based hashed flow rules. Flow features from the packets are extracted and classified using recurrent neural network (RNN) in the control plane and then the attack packets are dropped, normal packets are processed and suspicious packets are further classified in the application layer. NFV enabled cloud on the application layer classifies the suspicious packets into normal and malicious from the extracted packet feature based on multinomial naive bayes (MNB) algorithm. The extensive simulation is performed in Network simulator and experimental results show the proposed attack mitigation is better in terms of detection rate, accuracy, precision, recall, and authentication time with respect to previous research.

Lightweight security provisioning is a recent topic in resource-constrained software-defined networking (SDN). Integration between SDN, internet of things (IoT), network function virtualization (NFV) provides massive application services for 5G/B5G communications. Current state-of-the-art security solutions were caused by higher resource consumption, weaker key distribution, and forged credentials. In this paper, we proposed a blockchain-based lightweight security architecture (Bloc-Sec) in SDN/NFV that enabled cloud of IoT networks with 5G/B5G communication. Firstly, we authenticate all IoT devices to the blockchain server using multiple factors Blake-256 hashing algorithm. Secondly, we select the optimal virtual network function (VNF) using the cuttlefish optimization algorithm. Thirdly, blockchain is invoked to keep store the hashed flow rules that are deployed in VNF. Fourthly, the controller is involved in the packet classification by proposing the packet header inspection and packet content inspection using spiking dual fuzzy neural networks. For testing, NS3.26 is implemented and the performance evaluated.

Number of networks and their sizes in terms of connected devices are increasing very rapidly. These devices are producing massive amount of raw data for different progressive purposes that need to be processed and stored somewhere. Local data storage and complete processing of massive data will not be possible by resource constrained Internet of Things (IoT) enabling devices anymore in the future. Therefore, there is a need of data storage and processing capabilities to meet the desired goals of applications. Cloud computing and edge computing are the possible solution to circumvent limited data storage and computation issues of IoT devices. This paper presents a comparative study of popular architectures in which IoT devices are integrated with cloud. For this purpose, various IoT and Cloud of Things (CoT) architectures have been studied. Strengths and weaknesses of the studied IoT and CoT architectures are comprehensively analyzed on the basis of five comparison parameters namely; resource based segregation of IoT enabling devices, range based segregation of the communication network, segregation of security solutions, cloud connectivity, and edge computing services.

There is a cardinal infrastructure of application software’s, protocols, and physical devices over different sort of wired and wireless networks, that need to communicate with each other, whether located not only on earth but also in the space. OSI seven-layers model has become an international standard to communicate securely and confidently among different kinds of the corporate networks while keeping the other OSI layers unfamiliar with the current layer of communication. This secrecy among the layers results in vulnerability to attack in a way that if one layer is compromised to attack, the other layers will not be able to detect it properly. This individuality of OSI layers makes the whole network severely vulnerable to attack particularly due to the data link layer as compared to other layers. Generally network security problems at layer 2 are not properly addressed as compared to other layers, rather the people focus on the device’s security for the whole management system instead. This paper encompasses network security problems faced due to lack of hardening the layer 2 and it also describes, how it makes a LAN or the system of networks more vulnerable to attacks, especially for the MAC flooding, ARP spoofing, VLAN hopping, DHCP attacks, Denial-of-Service (DoS) and Spanning Tree Protocol in a very concise manner.

Mutual Authentication is the initial and the most significant line of defense in any open and trusted network system. Weaknesses in mutual authentication protocol may lead to eavesdropping, unautho...

Software defined networking (SDN), network function virtualization (NFV), and cloud computing are receiving significant attention in 5G networks. However, this attention creates a new challenge for security provisioning in these integrated technologies. Research in the field of SDN, NFV, cloud computing, and 5G has recently focused on the intrusion detection and prevention system (IDPS). Existing IDPS solutions are inadequate, which could cause large resource wastage and several security threats. To alleviate security issues, timely detection of an attacker is important. Thus, in this paper, we propose a novel approach that is referred to as multilayered intrusion detection and prevention (ML-IDP) in an SDN/NFV-enabled cloud of 5G networks. The proposed approach defends against security attacks using artificial intelligence (AI). In this paper, we employed five layers: data acquisition layer, switches layer, domain controllers (DC) layer, smart controller (SC) layer, and virtualization layer (NFV infrastructure). User authentication is held in the first layer using the Four-Q-Curve algorithm. To address the flow table overloading attack in the switches layer, the game theory approach, which is executed in the IDP agent, is proposed. The involvement of the IDP agent is to completely avoid a flow table overloading attack by a deep reinforcement learning algorithm, and thus, it updates the current state of all switches. In the DC layer, packets are processed and classified into two classes (normal and suspicious) by a Shannon Entropy function. Normal packets are forwarded to the cloud via the SC. Suspicious packets are sent to the VNF using a growing multiple self-organization map (GM-SOM). The proposed ML-IDP system is evaluated using NS3.26 for different security attacks, including IP Spoofing, flow table overloading, DDoS, Control Plane Saturation, and host location hijacking. From the experiment results, we proved that the ML-IDP with AI-based defense mechanisms effectively detects and prevents attacks.

This article presents the optimised planning of RDS and proposes the artificial intelligence technique using hybrid optimisation combined with phasor particle swarm optimisation and a gravitational algorithm, called PPSO/GSA for optimal planning of RDS considering photovoltaic distributed generators in RDSs. The main objective is to maximise the RDS performance by optimally allocating the PV generators. The proposed PPSO/GSA is implemented and validated on 94-bus practical RDS located in Portuguese considering single and multiple scenarios of PV generators installation along with various loading conditions. The results reveal that the optimised planning of RDS enhance the system reliability in term of a substantial reduction in active power loss and yearly economic loss as well as improving system voltage profile. Moreover, the convergence characteristics, computational efficiency, and applicability of the proposed artificial intelligence technique are evaluated by comparative analysis and comparison with other optimisation techniques.

Failure occurrence in large-scale systems is inevitable, which makes the resilience a key challenge for modern systems. Checkpoints with rollback recovery is a well-known approach to provide fault tolerance in distributed systems. The checkpoint based fault tolerance approach periodically persists the application state to reliable storage, which serves as a recovery point in case of failure. These periodic checkpoints are not inline with the failure rate of the systems as many studies conclude that failure occurrence is not periodic. The optimal size of checkpoint interval is a crucial decision, which directly determines the checkpoint overheads. To minimize the checkpoint overheads, we propose to reduce the number of checkpoints during the application execution. We suggest reducing the number of checkpoints by successively increasing the checkpoint intervals. We consider the failure probability of the underlying infrastructure and iteratively increase the checkpoint intervals. The proposed checkpoint approach tailors the checkpoint initializing based on the failure probability. If failure probability is low, it increases the checkpoint interval, and eventually reduces the total number of checkpoints triggered during application timespan. Reducing the total number of checkpoints during application execution results in decreasing the checkpoint overheads. The experiment results show that the proposed checkpoint policy considerably reduces the checkpoint overheads as compared to periodic checkpoints.

Software Defined Networking (SDN) is a promising network architecture constructed by a decoupled data plane and control plane. Deployment of key entities i.e. controllers and switches is vital for achieving high performance environment. In this paper, we concentrate on classification, load balancing in switches and controllers (local and global). User requests arrive from WiFi Access Points (APs) (i.e.) WiFi network support. The novel SDN architecture involves 2-metric classifier in an AP and dynamic 2-level load balancing scheme (D2LLB) is applied over the global controller. Three significant features of classified flows are extracted and applied on Analytical Hierarchical Process (AHP), to allocate to controllers. Global Controller manages information of local controllers in form of clusters. Clustering is done in accordance to their processing information which is periodically updated. Clusters are un-loaded clusters, overloaded cluster and idle clusters. Hereby load is balanced in two ways; one is by migrating controllers in overloaded condition and second is allocating higher prioritized flows to idle controllers. Finally, our extensive simulation performed using OMNeT++ simulator, shows better performance in delay and file transfer as compared to weighted round robin technique and switch based load balancing.

Processing semantics ensure tuple processing guarantees during system recovery from failures. It determines whether particular tuples are re-processed or not upon system recovery. Processing semantics facilitate to maintain real-time response as well as correctness of results in stream data processing. Existing literature is in real deficit to provide sufficient resources exclusively on processing semantics. This work focuses on reviewing processing semantics in stream data processing systems. This article aims at exploring the impact of processing guarantees on performance of stream data processing. We summarize current research directions to guarantee processing semantics, based on prominent stream processing engines. Furthermore, we compare throughput for a couple of representative stream processing engines under all possible processing guarantees. Our experiments show that at-most once processing guarantee provides the highest throughput among other guarantees. Lastly, challenges for further research are also discussed in this paper.

With the emergence of the smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In this work, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a home energy management controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the utility with the load profile of the home. The smart meter is connected to a power grid having an advanced metering infrastructure which is responsible for two-way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising user comfort. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and real-time pricing information. Simulation results show that proposed algorithms reduce the peak-to-average ratio by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.