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.

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.

Ubiquitous Internet of Things (IoT) devices have fueled plenty of innovations in the emerging network paradigms. Among them, IoT edge caching has emerged as a promising technique to cope with the explosive growth in network data traffic, with Quality of Service (QoS) improved and energy saved. However, the intrinsic storage limitations of the edge servers poses a critical challenge for the IoT edge caching system. Enabling edge servers to cooperate with each other can provide a potential perspective to improve the edge storage utilization widely discussed. Nevertheless, it also incurs an additional communication overhead, eventually making the caching system more complex. As a result, how to perform an efficient cooperative caching becomes a critical issue. Thus, in this paper, we propose a deep reinforcement learning-based cooperative edge caching approach, which allows the distributed edge servers to learn to cooperate with each other. Specifically, edge servers determine their cache actions based on the local caching state. After that, the centralized remote server evaluates these actions and feeds back the evaluation results to edge servers for subsequent caching actions optimization. We show that, by designing an appropriate reward function, our approach promotes cooperation between edge servers as well as improving the system hit rate. On this basis, we consider a practical and reasonable scenario with inconsistent data item size and propose a novel multi-agent actor-critic caching algorithm. Extensive simulation results demonstrate the performance improvement using our proposed solution over three other caching algorithms.

Benefitingfrom the rapid development of communication technology and Internet of Things (IoT) devices, crowdsensing is on the rise. Sensor data from IoT devices can be requested for data analysis and utilization, however, the collected data of an object from multiple devices are usually different. Therefore, how to extract the most reliable data from numerous data has become an important topic, and truth discovery receives great attention. These collected data often contain personal sensitive information, if users’ privacy cannot be protected, many users are unwilling to contribute their data, and the usability of the published data will be greatly reduced. In this article, a robust privacy-preserving truth discovery scheme is proposed to simultaneously achieve the reliability and privacy of data. Specifically, the data are collected and encrypted before it is sent from the user. Compared with the existing works, there are two additional benefits, trusted third party and noncolluding platforms are not necessary anymore, hence the robustness is improved and single-point failure bottlenecks are eliminated. Besides, the proposed RPPTD is secure against many known attacks in open wireless networks, and the human-factor-aware differential aggregation attack. Finally, the performance evaluation indicates that our scheme is efficient and suitable for the practical environment.

Federated Learning (FL) allows collaborative training of a Machine Learning (ML) model while preserving data privacy across participating clients. Most existing studies consider FL clients to be proactive and completely honest in their participation. However, in reality, clients might lack the motivation to participate, and malicious behavior among some clients could negatively impact the interests of others. For these reasons, ensuring trust and fairness among FL clients is paramount but remains challenging due to limitations in FL consensus mechanisms and incentive strategies. To address these challenges, we introduce a Trustworthy and Fair FL (TFFL) framework that develops a reputation-based consensus mechanism called Dynamic Reputation Consensus (DRC), where clients’ reputations are dynamically assessed based on subjective opinions by evaluating real-time client behavior. We also incorporate time decay and temporal discounting of TFFL interactions along with the weighted measures of clients’ data quality, performance, and reliability to accurately reflect the evolving nature of client behavior over time. By adaptively adjusting clients’ incentives based on reputations and a cooperative game theory, DRC incentivizes honest participation and discourages malicious intent. In addition, we utilize blockchain and smart contracts to provide decentralized, regularized, and secure reputation management that is resistant to tampering and non-repudiation. Theoretical analysis and empirical results on widely used datasets (MNIST, CIFAR-10, and CIFAR-100) demonstrate the effectiveness of DRC in enhancing trust and fairness, improving performance, and providing robust security in FL settings. Results further exhibit that DRC offers superior performance in local model validation, consensus decision, and convergence time compared to related research approaches across various experimental settings.

Software-defined Networks (SDN) offers unique and attractive solutions to solve challenging management issues in Internet of Things (IoT) based large scale multi-technological networks. SDN-IoT networks collaboration is innovative and attractive but expected to be extremely heterogeneous in future generation IoT systems. For example, multi technology network, network externality, and nodes heterogeneity in SDN-IoT may seriously affect the flow or application specific Quality of Service (QoS) requirements. Further, it highly influences security adoption in an network of interconnected IoT nodes. We observe that both QoS and security are inter-dependent and non-negligible factors, thus we emphasize that in order to alleviate heterogeneity it is inevitable to study both these factors hand-to-hand (or vice-versa). With this aim, firstly, we discuss significant and reasonable cases to encourage researchers to study QoS and security integrally in order to alleviate heterogeneity at SDN-IoT control plane. Secondly, we propose a framework which successfully transforms the m heterogeneous controllers to n homogeneous controller groups. The key metric of our observation and analysis is the SDN controller’s response time. Following this, to validate our approach, we use mathematical model as well as a Proof of Concept (PoC) in a virtual SDN ecosystem, is demonstrated. From performance evaluation, we observe that the proposed framework significantly alleviate heterogeneity which helps to maintain QoS and enhance security. This fundamental analysis will enable network security individuals to deal heterogeneity, QoS, and security, of SDN-IoT, in more successful and promising ways.