The growth of the Internet of Things (IoT) has brought about security concerns owing to the massive global integration of diverse and constrained devices. Blockchain, Machine Learning (ML) and Deep Learning (DL) techniques have been proposed as effective ways to improve IoT security. This paper reviews the literature on the use of blockchain and smart learning techniques for security enhancement and threat detection in IoT networks. Blockchain offers a decentralized and immutable approach to secure data integrity, authenticating and controlling access to IoT devices, while ML and DL techniques allow intelligent monitoring of network data to detect anomalies and predict cyber-attacks. This study follows a systematic literature review approach, examining peer-reviewed journal articles and conference proceedings from 2019 to 2024. The analysis shows while blockchain, ML and DL technologies play a crucial role in enhancing IoT security, they each have limitations including scalability, computational overhead, data dependency and lack of flexibility against new cyber threats. Additionally, the majority of studies concentrate on either blockchain-based security or ML-based intrusion detection, with limited study on the integration of the two for real-time threat detection and mitigation. The study highlights this limitation and calls for the development of intelligent hybrid models that integrate blockchain technology with ML/DL to address scalability, adaptability and real-time security for IoT networks to ensure confidentiality, integrity and reliability.
Shamsudeen Mohammed S, Nwobodo-Nzeribe Nnenna Harmony, Aghaizu Herman Chijioke· International journal of re...· 0 citations
Many of the critical networks are now vulnerable to complex security threats, especially those launched by adversaries against the machine learning-driven security systems used by these networks. Such attacks take advantage of weaknesses in AI systems by perturbing the model with carefully designed perturbations, which result in misclassification of malicious content as benign, compromising the system's confidentiality, integrity, and availability. The adversarial threat is unlike traditional cyberattacks; it is dynamic, adaptive and can circumvent traditional intrusion detection capabilities. This paper provides an extensive literature review on the adversarial attack methods, detection and defence techniques of critical network infrastructures. This review includes peer-reviewed publications published between 2019 and 2024 from the leading academic databases such as IEEE Xplore, SpringerLink, ScienceDirect and Google Scholar. The total number of studies analyzed were 48, covering contributions in the fields of creating adversarial attack methods, machine learning and deep learning based detection methods, and mitigation techniques. The results indicate that adversarial attacks can be divided into the following categories: evasion attacks, poisoning attacks, and exploratory attacks, where some of the more sophisticated methods, including those based on gradient, optimization, and reinforcement learning, are very effective in evading security systems. Current solutions, however, suffer from limited real-time adaptability, cross-domain generalization ability, explainability and integration across the attack lifecycle. While there are several defence mechanisms proposed, such as adversarial training, anomaly detection, and input transformation, existing defences have difficulties in being adaptable in real time, cross-domain generalizable, explainable and suitable for certain phases of the attack lifecycle. The study highlights a number of critical research challenges such as the lack of a common defence framework, inadequate real-time detection capabilities, absence of a standardized data sets and poor ability to withstand adaptive adversaries. The paper suggests the creation of multi-strategic, adaptive, and real-time adversarial threat management systems that can sustain themselves in a heterogeneous network environment.
F. Okoye, Aghaizu Herman Chijioke, Shamsudeen Mohammed S.B· International journal of re...· 0 citations