The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic increase of network data and placed a high computation complexity across various connected devices. The IoT devices capture valuable information, which allows the industries or individual users to make critical live dependent decisions. Most of these IoT devices have resource constraints such as low CPU, limited memory, and low energy storage. Hence, these devices are vulnerable to cyber-attacks due to the lack of capacity to run existing general-purpose security software. It creates an inherent risk in IoT networks. The multi-access edge computing (MEC) platform has emerged to mitigate these constraints by relocating complex computing tasks from the IoT devices to the edge. Most of the existing related works are focusing on finding the optimized security solutions to protect the IoT devices. We believe distributed solutions leveraging MEC should draw more attention. This paper presents a comprehensive review of state-of-the-art network intrusion detection systems (NIDS) and security practices for IoT networks. We have analyzed the approaches based on MEC platforms and utilizing machine learning (ML) techniques. The paper also performs a comparative analysis on the public available datasets, evaluation metrics, and deployment strategies employed in the NIDS design. Finally, we propose an NIDS framework for IoT networks leveraging MEC.

Edge computing places the computational services and resources closer to the user proximity, to reduce latency, and ensure the quality of service and experience. Low latency, context awareness and mobility support are the major contributors to edge-enabled smart systems. Such systems require handling new situations and change on the fly and ensuring the quality of service while only having access to constrained computation and communication resources and operating in mobile, dynamic and ever-changing environments. Hence, adaptation and self-organisation are crucial for such systems to maintain their performance, and operability while accommodating new changes in their environment. This article reviews the current literature in the field of adaptive edge computing systems. We use a widely accepted taxonomy, which describes the important aspects of adaptive behaviour implementation in computing systems. This taxonomy discusses aspects such as adaptation reasons, the various levels an adaptation strategy can be implemented, the time of reaction to a change, categories of adaptation technique and control of the adaptive behaviour. In this article, we discuss how these aspects are addressed in the literature and identify the open research challenges and future direction in adaptive edge computing systems. The results of our analysis show that most of the identified approaches target adaptation at the application level, and only a few focus on middleware, communication infrastructure and context. Adaptations that are required to address the changes in the context, changes caused by users or in the system itself are also less explored. Furthermore, most of the literature has opted for reactive adaptation, although proactive adaptation is essential to maintain the edge computing systems’ performance and interoperability by anticipating the required adaptations on the fly. Additionally, most approaches apply a centralised adaptation control, which does not perfectly fit the mostly decentralised/distributed edge computing settings.

The Industrial Internet of Things (IIoT) should be equipped with computational resources to detect network intrusions, types of attacks, and update their models automatically in real time. The most challenging aspect of machine learning (ML)-based network intrusion detection system (NIDS) design to secure IIoT is the continuous need for up-to-date definitions of attack data records. Moreover, the approaches employed by cyber attackers are in a dynamic state with changing trends and techniques. Hence, conventional signature-based NIDS are not suitable since they cannot update obsolete detection models. Anomaly-based NIDS that contains an online learning technique is adopted in our proposed method. Since IIoTs face resource-constrained problems, such as low memory, low computing capacity, and limited energy supply, it is very challenging to implement the exiting general-purpose anomaly-based NIDS. This article proposes a lightweight NIDS based on an online incremental support vector data description (OI-SVDD) anomaly detection system on the IIoT devices and an adaptive sequential extreme learning machine (AS-ELM) on the multiaccess edge computing (MEC) server. Additionally, we utilize the MEC server, which provides computational resources to execute the AS-ELM model at the network’s edge. To avoid data saturation in the proposed model, we apply data filtering using the rate of convergence (ROC). We evaluated the proposed NIDS through experiments using two data sets, such as UNSW-NB15 (public data set) and our self-generated data set. Our results show that the proposed OI-SVDD and AS-ELM perform effectively and detect network intrusion in a realistic IIoT environment.

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. We leverage quantum phenomena such as superposition and entanglement to work on large-scale multi-dimensional data represented by quantum states. Under stochastic behaviors and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantum-empowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed Qe-DRL algorithm and its superior computational learning speed. Our proposed Qe-DRL algorithm outperforms other benchmarks in terms of energy efficiency performance.

With the rapid growth of the Internet of Things (IoT) applications in Maritime Transportation Systems (MTS), cyber-attacks and challenges in data safety have also increased extensively. Meanwhile, the IoT devices are resource-constrained and cannot implement the existing security systems, making them susceptible to various types of debilitating cyber-attacks. The dynamics in the attack processes in IoT-enabled MTS networks keep changing, which makes a traditional offline or batch ML-based attack detection systems intractable to apply. This paper provides a novel approach of using an adaptive incremental passive-aggressive machine learning (AI-PAML) method to create a network attack detection system (NADS) to protect the IoT devices in an MTS environment. In this paper, we propose an NADS that utilizes a multi-access edge computing (MEC) platform to provide computational resources to execute the proposed model at a network end. Since online learning models face data saturation problems, we present an improved approximate linear dependence and a modified hybrid forgetting mechanism to filter the inefficient data and keep the detection model up-to-date. The proposed data filtering ensures that the model does not experience a rapid increase in unwarranted data, which affects the model's attack detection rate. A Markov transition probability is applied to control the MEC selection and data offloading process by the IoT devices. The performance of the NADS is verified using selected benchmark datasets and a realistic IoT environment. Experimental results demonstrate that AI-PAML achieves remarkable performance in the NADS design for an MTS environment.

This paper investigates a computation resource optimization problem of mobile edge computing (MEC)- aided Internet-of-Things (IoT) devices with a reinforcement learning (RL) solution. Specifically, we leverage the stochastic optimization method and formulate the Lyapunov optimization technique to maximize the long-term energy efficiency, taking into account the transmission power, network stability, and transmission latency. Based on the Markov decision process and model-free deep RL (DRL) approach, we propose a double DRL-based online computation offloading method to implement a deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. Furthermore, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading tasks. The proposed real-time Lyapunov optimization and DRL algorithms achieve low computational complexity and optimal processing time. Simulation results demonstrate that the proposed algorithm can achieve near-optimal control performance with enhanced energy efficiency performance compared to the baseline policy control algorithms.