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The flexibility of 5G-NGNs makes them an ideal infrastructure for supporting mission-critical IoT applications that require low latency and high bandwidth. However, due to the rapid proliferation and the integration of IoTs with 5 G, the threat surface has considerably expanded. Hence the security of IoT devices is a big concern. Unfortunately, IoT devices have limited resources, and the traditional security approaches (authentication and intrusion detection approaches) of cryptography do not work effectively on 5G-IoT ecosystems. Motivated from this, we leverage the distinctive RF (Radio Frequency) fingerprinting signatures of IoT devices and used them to train a Deep learning model, Mahalanobis Distance theory in addition to the Chi-square distribution theory, to authenticate the IoT nodes. Under robust scenarios we have tested the approach shows detection accuracy (99.35%) as well as significant amount of reduction in model's training time as these two metrics are one of the primary key performance indicators (KPIs). In order to evaluate the effectiveness of the proposed method in real-time scenarios, we tested the proposed solution with a real RF dataset and the OSM-MANO 5 G platform. The model underwent formal verification using the Tamarin Prover tool, and the proposal was also compared with recent research works.

The booming of learning-based Artificial Intelligence (AI) enables the integration of Big Data and emerging computing architectures, which facilitate the Edge-AI-as-a-Service (EAaaS) in the edge-cloud continuum. To meet the emerging demands, such as privacy preservation and autonomy, blockchain-enabled federated learning (B-FL) is proposed, which further provides decentralized processing, data falsification avoidance, and learning model reliability. However, synchronous global aggregation, which is deployed in most existing B-FL paradigms, is dragging down the performances due to the data and computing resources heterogeneity of diverse edge devices. In addition, the restricted resources of edge devices pose further challenges in executing learning tasks and blockchain-based consensus simultaneously. To solve these issues, we propose a blockchained dual-asynchronous federated learning (BAFL-DT) service model for EAaaS in the digital twin empowered edge-cloud continuum. In BAFL-DT, federated learning services are run on local edge devices, while the global aggregation is achieved by the consensus process of digital twins implemented in the cloud. Besides, dual-asynchronous FL allows both local training and global aggregation to be performed in an asynchronous manner, which is uniquely enabled by the proposed paradigm. Extensive evaluations of real-world datasets testify to the superior performances of EAaaS by improving accuracy and efficiency.

Geo-Distributed Machine Leaning (Geo-DML) has been a promising technology, which performs collaborative learning across geographically dispersed data centers (DCs) with privacy-preserving over Wide Area Networks (WANs). Unfortunately, the limited and heterogeneous WAN bandwidth poses significant challenges to the performance of Geo-DML systems, leading to increased communication overhead and affecting the revenue of ISPs eventually. In particular, when multiple online jobs coexist in Geo-DML systems, the competition for bandwidth between training flows of different jobs aggravates this negative impact. To alleviate it, this paper investigates the problem of online training flow scheduling for Geo-DML jobs. We first formulate the studied problem as an Linear Programming (LP) model with the objective of maximizing the revenue of ISPs. Then, we propose an online traffic scheduling algorithm called Training Flow Adaptive Steering (TFAS), which exploits a primal-dual framework, tailored for efficient resource allocation of jobs to schedule training flows, such that system resources are maximally utilized and training procedures can be expedited and completed in a timely manner. Meanwhile, we conduct rigorous theoretical analysis to guarantee that the proposed algorithm can achieve a good competitive ratio. Extensive evaluation results demonstrate that our algorithm performs well and outperforms commonly adopted solutions 36.2%-49.4% in average.

Accurate load forecasting is critical for electricity production, transmission, and maintenance. Deep learning (DL) model has replaced other classical models as the most popular prediction models. However, the deep prediction model requires users to provide a large amount of private electricity consumption data, which has potential privacy risks. Edge nodes can federally train a global model through aggregation using federated learning (FL). As a novel distributed machine learning (ML) technique, it only exchanges model parameters without sharing raw data. However, existing forecasting methods based on FL still face challenges from data heterogeneity and privacy disclosure. Accordingly, we propose a user-level load forecasting system based on personalized federated learning (PFL) to address these issues. The obtained personalized model outperforms the global model on local data. Further, we introduce a novel differential privacy (DP) algorithm in the proposed system to provide an additional privacy guarantee. Based on the principle of generative adversarial network (GAN), the algorithm achieves the balance between privacy and prediction accuracy throughout the game. We perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

The widespread application of artificial intelligence technologies in metaverse introduces significant privacy concerns It is critical to study the training information leakage of AI models during the interaction of metaverse Model inversion attacks have revealed the privacy vulnerability of deep learning models through reconstructing their training data during predictions In this paper we reconstruct the training samples of AI models target model in metaverse under a more practical threat model where the adversary only has black box access to the target model and no side information besides auxiliary dataset We propose a contrastive supervised model inversion attack CSMI Specifically we modify contrastive learning to train a neural network projector for inferring semantical knowledge contained in target model s outputs Afterwards we design a supervised inversion model similar to the architecture of conditional GAN where the projected outputs of the target model are involved as conditional inputs to supervise the training process Finally to generate inversion samples we propose a bi level random search strategy to search proper inputs of the trained inversion model through an objective function which consists of the attacking success rate and the qualities of the reconstructed image We conduct extensive experiments to evaluate the performance of the proposed CSMI The experimental results show that samples reconstructed by CSMI are more visually plausible and reveal more features of the target than the state of the art methods under a black box setting

Due to millions of heterogeneous physical nodes, multiple-vendor and multi-tenant domains, and technologies etc., 5G has greatly expanded the threat landscape. Particularly from the high rate of traffic and ultra-low latency requirement of applications in 5G networks, the detection of the network traffic anomalies in real-time is critical. The conventional security approaches lack compatibility with modern network designs and are not much effective in 5G settings. We propose a two-stage network traffic anomaly detection system compatible with ETSI-NFV standard 5G architecture. Our architecture consists of two modules, i.e., (a) Dimensionality Reduction to compress the sample size at the edge of 5G networks and (b) Deep Neural Network classifier (DNN) that detects traffic anomalies. We have conducted our experiments using OMNET++ and ETSI-NFV (OSM MANO) 5G orchestration real platform deployed on AWS cloud systems. We have used the UNSW-NB15 data set and have shown that at dimensionality reduction factor of 81% the detection accuracy obtained is 98%. The proposal is compared with other recent approaches to show the overall merit of the architecture.

Although the next generation networks (5G-NGNs) provide a flexible infrastructure to support latency-sensitive and bandwidth-hungry mission-critical Internet of Things (IoT) applications, however, the 5G-IoT integration in NGNs has increased the threat surface. Unfortunately, IoT devices are resource constrained, and the traditional intrusion detection systems (IDS) approaches based on cryptography are not effective on 5G-IoT ecosystems. In this article, we propose an effective 5G-IoT node authentication approach that leverages unique radio frequency (RF) fingerprinting data to train the Deep learning model to detect legitimate and nonlegitimate IoT nodes. Our approach is based on Mahalanobis Distance theory and Chi-square distribution theories. The proposed approach achieves a higher detection accuracy (99.35%) as well as lower training time compared to other existing approaches which is a key benefit of our approach in NGNs. The experiments are conducted using ETSI-open source NFV management and orchestration (OSM-MANO) platform on Amazon Web Services (AWSs) cloud platform to verify how the proposed approach would fit in real-life scenarios. The method can be used as a standalone security system or as a part of multifactor authentication.

The integration of Internet of Things (IoT) with 5G simply creates additional threat landscape and any network infrastructure is more vulnerable. Severe attacks on networks potentially damage organization reputation, customers or tenants lose confidence, and impacts operational and maintenance cost. Intrusion detection systems (IDSs) are an effective approach to mitigate threats. We present a novel IDS mechanism in which the unique Radio Frequency (RF) features of IoT devices are used to create a learning model which is later used to identify the illegitimate devices in the network. Leveraging the Deep Autoencoder (DAE), the existing steady-state feature extraction is generalized. The performance evaluation is conducted using a real data set from different aspects including the mobility of the nodes. The proposed IDS is broken down into pluggable virtual network function (VNF) components and its evaluation is presented for its integration into the 5G network slicing ecosystem from the perspective of the European Telecommunications Standards Institute (ETSI) standards. A Proof of Concept (PoC) is presented using ETSI Open Source NFV Management and Orchestration (OSM-MANO) test bed, deployed on AWS cloud systems, to show how the proposed approach would fit in with a real-life MANO.

The evolution of quantum computers is considered a serious threat to public-key cryptosystems (e.g. RSA, ECDSA, ECDH, etc.). This is indeed a big concern for security of the Internet and other data communication and storage systems. The reason is that public-key schemes are the basis in the generation of shared symmetric keys that are used to perform data encryption/decryption in communication and data transfer protocols. One possible approach to address this issue is to use Quantum Key Distribution (QKD) (instead of public-key schemes) for the ultra-secure generation of symmetric keys. QKD is a physical layer technology that allows two parties (equipped with optical communication interfaces) to generate secure random keys over a quantum channel that is immune to eavesdropping threats. The keys are then used by symmetric encryption schemes (e.g. AES) to encrypt data over classical channels. This allows us to have data encryption/decryption without needing a public-key scheme. However, due to its inherent characteristics, the implementation of QKD has mostly been considered in particular contexts only (e.g. backhaul networks, point-to-point connections, optical networks, etc.). This indeed limits the utility of QKD technology to only some particular applications while it has the potential to be used in a wide range of used cases. Motivated by this (increasing the usability of QKD technology), in this study, the authors propose a model that enables SDN-based networks to utilise QKD technology and provide QKD security service (i.e., random key generation service) to network applications and security protocols in a practical and efficient way. In the proposed approach, secret keys are generated based on the distribution of quantum entanglement between QKD nodes deployed in the network. The significant characteristic of our proposed model is that it does not rely on quantum repeaters to operate. This also improves the efficiency of the employed QKD mechanisms in terms of

Edge caching has been regarded as a promising technique for low-latency, high-rate data delivery in future networks, and there is an increasing interest to leverage Machine Learning (ML) for better content placement instead of traditional optimization-based methods due to its self-adaptive ability under complex environments. Despite many efforts on ML-based cooperative caching, there are still several key issues that need to be addressed, especially to reduce computation complexity and communication costs under the optimization of cache efficiency. To this end, in this paper, we propose an efficient cooperative caching (FDDL) framework to address the issues in mobile edge networks. Particularly, we propose a DRL-CA algorithm for cache admission, which extracts a boarder set of attributes from massive requests to improve the cache efficiency. Then, we present an lightweight eviction algorithm for fine-grained replacements of unpopular contents. Moreover, we present a Federated Learning-based parameter sharing mechanism to reduce the signaling overheads in collaborations. We implement an emulation system and evaluate the caching performance of the proposed FDDL. Emulation results show that the proposed FDDL can achieve a higher cache hit ratio and traffic offloading rate than several conventional caching policies and DRL-based caching algorithms, and effectively reduce communication costs and training time.

In smart farming sector, Internet of Things (IoT) based smart sensing systems are vulnerable to failure, malfunction, and malicious attacks. Also, sensors are deployed often in an alien and harsh environment. Here, the conditions are not well supportive which either causes the sensor to fail prematurely or gives unusual and erroneous readings, known as outliers. This effects the smart networks performance and decision-making ability in many ways. Therefore, it is important to accurately detect the IoT sensor behaviour in legitimate, faulty, and compromised or attack scenarios. To distinguish the sensor behaviour in different scenarios we have proposed a feasible approach using spatial correlation theory which is validated using Morans I index tool. We have used Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) models to test our approach. For real-time anomaly detection we have used an edge computing technology. We have compared the proposed approach, using Forest Fire real dataset, with the three existing recent works. Our results are promising in terms of accurate detection of IoT sensor behaviours in real-time. This will assist the precision farming industry in making better decisions to securely manage IoT field network, increase productivity, and improves operational efficiency.

Network Function Virtualization (NFV) is an emerging approach to serve diverse demands of network services by decoupling network functions and dedicated network devices. Traffic needs to traverse through a sequence of software-based Virtual Network Functions (VNFs) in a preset order, which is named as Service Function Chain (SFC). Since network operators usually deploy the same type of VNFs in different locations in NFV-enabled networks. How to steer a SFC request to an appropriate path in substrate networks to meet service demands becomes an important issue, which is typically known as SFC mapping. However, the existing research works on SFC mapping often assume that service chain graphs are given in advance. They do not consider VNF interdependency and traffic volume change, which are both theoretically challenging for NFV Management and Orchestration (MANO) framework. To this end, we study the joint optimization of Service Chain Graph Design and Mapping (SCGDM) in NFV-enabled networks. Our objective is to minimize the maximum link load factor to improve the performance of network system. We first formulate the SCGDM problem as an Integer Linear Programming (ILP) model, and prove that it is an NP-hard problem by reduction from a classical Virtual Network Embedding (VNE) problem. Further, we develop an approximation algorithm using randomized rounding method and analyze the approximation performance. Extensive simulation results show that the proposed algorithm effectively reduce the maximum link load factor.

With the growing proliferation of intelligent end devices and data analytics techniques, real momentum towards the development of smart healthcare networks (SHN) has already been evident. Multiple parties in SHNs continuously exchange medical data in order to achieve a precise diagnosis and process optimization. Privacy issue emerges since medical data are susceptible, while the combination of a series of medical data may lead to further privacy leakage. Adversaries launch unceasingly launch poisoning attacks, a dominant attack to maliciously manipulate data, severely impact the authenticity of the data transmitting over the SHNs, leading to misdiagnosing or even physical damage. In this paper, we propose a personalized differential privacy model built upon blockchain, in which the community density is exploited to customize the degree of privacy protection and inject corresponding noise data. Besides using blockchain as the underlying network architecture to defeat poisoning attacks. The proposed model can guarantee the authentication of the differentially private data, traceability of data, and single-point failure avoidance in SHN. Evaluation and extensive results using real-world data sets demonstrate the superiority of the proposed model.

With rising temperatures and events contributing climate change, the world is facing extreme weather patterns. Recently, Australia was hit hard by bushfires, the most devastating fires ever faced by the country. The economic damage reported was nearly one billion Australian dollars and an estimated 3 billion native animals killed or adversely affected. Given the extent and intensity of this damage, researchers are seeking effective solutions to enable the prediction of fire before it starts to increase the time available for firefighters to protect lives and assets and prepare to mitigate the fires. This motivated us to investigate an approach to address this critical problem. In this paper, we propose a Machine Learning (ML)–based approach that detects anomalies in spatiotemporal measurements of environmental parameters (e.g., temperature, relative humidity, etc.). In the proposed approach, an ML–based model learns the normal spatiotemporal behaviour of the environmental data (collected over a period of one year). This is carried out during a one-time training phase. Then, during the detection phase, any spatiotemporal pattern in the real–time data (received from the field sensors) that is different than the normal pattern will be identified by the model as anomaly which indicates a possible bushfire situation. Following this, we propose a supplementary classification model based on Moran’s I index to ensure that the detected anomalies are not due to either a sensor failure or a security attack (which are common in IoTs). We developed three different ML models for performance evaluation and comparison and used the Forest Fire dataset to train them. The results of our experiments confirm the effectiveness of the proposed approach in the early detection of fire symptoms.

The potential vulnerability to wireless spoofing attacks is still a critical concern for Next Generation Internet of Things (NGIoT) networks which may result in catastrophic consequences in mission-critical applications. Conventional solutions may impose additional signal processing, protocol, and latency overheads which are inappropriate for NGIoT networks designed to provide high-speed and low-latency connections for a large number of resource-constrained IoT devices. In this paper, we utilize the uniqueness of beam pattern features in mmWave-enabled devices and propose a scalable security mechanism for the detection of wireless spoofing attacks in NGIoT networks. This uniqueness is proven to exist due to the non-ideal manufacturing of antenna arrays used in mmWave-enabled devices. In our approach, when legitimate mmWave-enabled IoT devices enrol into the network, their unique beam features are learned by a learning model developed at the network server. Then, during data transmission, network base stations (gNBs)/Access Points (APs) measure the beam features from the received RF signals and send them to the network server for the detection of anomalies. We develop our learning model based on Deep Autoencoders (DAEs) that are an effective tool for anomaly detection. Fortunately, the beam feature extraction can be performed using the beam searching mechanism that is already provided in mmWave standards (5G-NR and IEEE 802.11ad). Thus, feature extraction does not introduce any signal processing overheads to the system. Moreover, the proposed mechanism imposes zero computation/communication overhead to the resource - constrained IoT nodes. In our experiments, we reached 98.6% accuracy in the detection of illegitimate devices which confirms the effectiveness of the proposed approach.

In cryptocurrencies, privacy of users is preserved using pseudonymity. However, it has been shown that pseudonymity does not result in anonymity if a users transactions are linkable. This makes cryptocurrencies vulnerable to deanonymization attacks. The current solutions proposed in the literature suffer from at least one of the following issues: (1) requiring a trusted thirdparty entity, (2) poor performance, and (3) incompatible with the standard structure of cryptocurrencies. In this paper, we propose Unlinkable Coin (UCoin), a secure mixbased approach to address these issues. In UCoin, the link between the input (payer) and output (payee) addresses in a transaction is broken. This is done by mixing the transactions of multiple users into a single aggregated transaction in which the output addresses have been secretly shuffled. In our protocol design, we first develop HDCnet, a secure shuffling protocol that enables a group of users to anonymously publish their data. Then, we deploy the proposed HDCnet protocol in the UCoin architecture (as a mixing unit) to generate the aggregate transactions. We show that UCoin (1) does not rely on a trusted thirdparty, (2) can mix 50 transactions in 6.3 seconds that is 18% faster than the current solutions, and (3) is fully compatible with the architecture of cryptocurrencies.

With massive sorts of terminals, devices, and machines connecting to 5G, a tremendous surge of data makes cyber-security a pressing issue, and conventional countermeasures are facing unprecedented challenges. Recently, with the rise of ML (Machine Learning) and SDN/NFV-based (Software-Defined Networks/Network Functions Virtualization) SFC (Service Function Chaining) techniques, how to leverage them for security enhancement in MEC (Multi-Access/Mobile Edge Computing) has received much attention. Hence, in this article, we first propose an elastic framework to integrate ML with virtualized SFC, aiming at smart and efficient provision of different services at MEC. Then, we propose an ML-based anomaly detection algorithm used as a kind of service policy for SFC classifiers, which guides the latter for quick traffic classification and subsequent redirections of attack flows. Finally, we build a corresponding prototype system and evaluate the performance of the proposed algorithm through extensive experiments. Related results have confirmed the feasibility and advantages of the proposed framework and algorithm.

With the cool upcoming wave of 5G, currently, the networking and telecommunication industries are facing various digital transformations, which are changing the very fundamental nature of the existing network management infrastructure. Besides the Internet of Things (IoT) domain, we also notice that the 5G network in itself is composed of millions of heterogeneous physical entities and nodes, multiple domains, complex protocols and technologies, different gateways, and so on. This heterogeneity imposes critical impacts on the application specific quality of service (QoS) requirements, performance and utilization of network resources, and data and user security. In order to alleviate the above impacts, researchers propose to use different technologies such as software-defined networking, network function virtualization, blockchain, and artificial intelligence in 5G-enabled IoT networking. We notice that the layers over layers (of protocols and technologies) act like a plug-in over plug-in (PoP) in the network in order to accomplish various aims, including meeting QoS demands, enhancing security, load balancing, and so on. On one hand, we agree that this integration of different technologies in 5G networks bring numerous advantages, but on the other hand, we realize that this has posed a lot of unique critical issues in modern 5G network management. In this article, we point out that this straightforward approach of PoP is eventually not a healthy approach for network transformation. In this regard, using open source MANO (OSM), we provide a proof of concept (PoC) to show that at varying degrees of heterogeneity, PoP adds the delay in the VNF deployment process and further impacts the VIM CPU performance. This eventually affects the QoS requirements of IoT nodes or applications. Following this, we propose a high-level holistic approach that helps to alleviate the PoP issue. Finally, in this context, we also discuss the associated challenges and research opportunities.

© 2020 Network Function Virtualization (NFV) is a promising technology that implements Virtual Network Function (VNF) with software on general servers. Traffic needs to go through a set of ordered VNFs, which is called a Service Function Chain (SFC). Rational deployment of VNFs can reduce costs and increase profits for network operators. However, during the deployment of the VNFs, how to guarantee the reliability of SFC requirements while optimizing network resource cost is still an open problem. To this end, we study the problem of reliability-aware VNF placement in carrier networks. In this paper, we firstly redefine the reliability of SFC, which is the product of the reliability of all nodes and physical links in SFC. On this basis, we propose two reliability protection mechanisms: the All-Nodes Protection Mechanism (ANPM) and the Single-Node Protection Mechanism (SNPM). Following this, for each protection mechanism, we formulate the problem as an Integer Linear Programming (ILP) model. Due to the problem complexity, we propose a heuristic algorithm based on Dynamic Programming and Lagrangian Relaxation for each protection mechanism. With extensive simulations using real world topologies, our results show that compared with the benchmark algorithm and ANPM, SNPM can save up to 33.34% and 26.76% network resource cost on average respectively while guaranteeing the reliability requirement of SFC requests, indicating that SNPM performs better than ANPM and has better application potential in carrier networks.

© 2014 IEEE. Recently, there has been a rapid growth in location-based systems and applications in which users submit their location information to service providers in order to gain access to a service, resource, or reward. We have seen that in these applications, dishonest users have an incentive to cheat on their location. Unfortunately, no effective protection mechanism has been adopted by service providers against these fake location submissions. This is a critical issue that causes severe consequences for these applications. Motivated by this, we propose the Privacy-Aware and Secure Proof Of pRoximiTy (PASPORT) scheme in this article to address the problem. Using PASPORT, users submit a location proof (LP) to service providers to prove that their submitted location is true. PASPORT has a decentralized architecture designed for ad hoc scenarios in which mobile users can act as witnesses and generate LPs for each other. It provides user privacy protection as well as security properties, such as unforgeability and nontransferability of LPs. Furthermore, the PASPORT scheme is resilient to prover-prover collusions and significantly reduces the success probability of Prover-Witness collusion attacks. To further make the proximity checking process private, we propose P-TREAD, a privacy-aware distance bounding protocol and integrate it into PASPORT. To validate our model, we implement a prototype of the proposed scheme on the Android platform. Extensive experiments indicate that the proposed method can efficiently protect location-based applications against fake submissions.