Comprehensive Analysis of the Current State of Cyber Security Measures for IoT Devices

A new era of connectivity has been brought about by the Internet of Things (IoT), which presents unheard-of chances for innovation in a wide range of sectors and applications. IoT device proliferation does, however, also bring with it serious cybersecurity challenges. This systematic review examines the state of cyber security risk in the Internet of Things (IoT) space today, providing a thorough analysis of the research that has already been done and the approaches that have been used to assess and mitigate these risks. We study different aspects of cyber security risk within the IoT context, such as threat modeling, risk assessment techniques, vulnerability analysis, and mitigation strategies, drawing from a wide range of peer-reviewed articles, industry reports, and white papers. Additionally, the review draws attention to the unique qualities of IoT systems—such as heterogeneity, scalability, and resource constraints—that increase the risks associated with cyber security. The current status of cyber security protocols for Internet of Things devices is thoroughly examined in this paper. This paper will examine the distinct difficulties presented by the Internet of Things environment, the security flaws that these gadgets frequently display, and the possible risks they may encounter. Furthermore, the paper will examine the diverse approaches, tools, and methods presently utilized to enhance the security of Internet of Things devices. We will also talk about the regulatory environment that oversees IoT security and the ongoing research and development initiatives aimed at improving it. In order to help direct future efforts in safeguarding our digital, interconnected world, we hope to offer a comprehensive, perceptive analysis

Evil Twin Attacks on Smart Home IoT Devices for Visually Impaired Users

Securing the Internet of Things (IoT) devices in a smart home has become inevitable due to the recent surge in the use of smart devices by the visually impaired. The visually impaired users rely heavily on these IoT devices and assistive technologies for guidance, medical usage, mobility help, voice recognition, news feeds and emergency communications. However, cyber attackers are deploying Evil Twin and Man-in-the-middle (MITM) attacks, among others, to penetrate the network, establish rogue Wi-Fi access points and trick victims into connecting to it, leading to interceptions, manipulation, exploitation, compromising the smart devices and taking command and control. The paper aims to explore the Evil Twin attack on smart devices and provide mitigating techniques to improve privacy and trust. The novelty contribution of the paper is three-fold: First, we identify the various IoT device vulnerabilities and attacks. We consider the state-of-the-art IoT cyberattacks on Smart TVs, Smart Door Lock, and cameras. Secondly, we created a virtual environment using Kali Linux (Raspberry Pi) and NetGear r7000 as the home router for our testbed. We deployed an Evil Twin attack to penetrate the network to identify the vulnerable spots on the IoT devices. We consider the Kill Chain attack approach for the attack pattern. Finally, we recommend a security mechanism in a table to improve security, privacy and trust. Our results show how vulnerabilities in smart home appliances are susceptible to attacks. We have recommended mitigation techniques to enhance the security for visually impaired users

Sensitivity analysis for vulnerability mitigation in hybrid networks

The development of cyber‐assured systems is a challenging task, particularly due to the cost and complexities associated with the modern hybrid networks architectures, as well as the recent advancements in cloud computing. For this reason, the early detection of vulnerabilities and threat strategies are vital for minimising the risks for enterprise networks configured with a variety of node types, which are called hybrid networks. Existing vulnerability assessment techniques are unable to exhaustively analyse all vulnerabilities in modern dynamic IT networks, which utilise a wide range of IoT and industrial control devices (ICS). This could lead to having a less optimal risk evaluation. In this paper, we present a novel framework to analyse the mitigation strategies for a variety of nodes, including traditional IT systems and their dependability on IoT devices, as well as industrial control systems. The framework adopts avoid, reduce, and manage as its core principles in characterising mitigation strategies. Our results confirmed the effectiveness of our mitigation strategy framework, which took node types, their criticality, and the network topology into account. Our results showed that our proposed framework was highly effective at reducing the risks in dynamic and resource constraint environments, in contrast to the existing techniques in the literature. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Vulnerability modelling and mitigation strategies for hybrid networks

Hybrid networks nowadays consist of traditional IT components, Internet of Things (IoT) and industrial control systems (ICS) nodes with varying characteristics, making them genuinely heterogeneous in nature. Historically evolving from traditional internet-enabled IT servers, hybrid networks allow organisations to strengthen cybersecurity, increase flexibility, improve efficiency, enhance reliability, boost remote connectivity and easy management. Though hybrid networks offer significant benefits from business and operational perspectives, this integration has increased the complexity and security challenges to all connected nodes. The IT servers of these hybrid networks are high-budget devices with tremendous processing power and significant storage capacity. In contrast, IoT nodes are low-cost devices with limited processing power and capacity. In addition, the ICS nodes are programmed for dedicated functions with the least interference. The available cybersecurity solutions for hybrid networks are either for specific node types or address particular weaknesses. Due to these distinct characteristics, these solutions may place other nodes in vulnerable positions. This study addresses this gap by proposing a comprehensive vulnerability modelling and mitigation strategy. This proposed solution equally applies to each node type of hybrid network while considering their unique characteristics. For this purpose, the industry-wide adoption of the Common Vulnerability Scoring System (CVSS) has been extended to embed the distinct characteristics of each node type in a hybrid network. To embed IoT features, the ‘attack vectors’ and ‘attack complexity vectors’ are modified and another metric “human safety index”, is integrated in the ‘Base metric group’ of CVSS. In addition, the ICS related characteristics are included in the ‘Environmental metric group’ of CVSS. This metric group is further enhanced to reflect the node resilience capabilities when evaluating the vulnerability score. The resilience of a node is evaluated by analysing the complex relationship of numerous contributing cyber security factors and practices. The evolved CVSSR-IoT-ICS framework proposed in the thesis measures the given vulnerabilities by adopting the unique dynamics of each node. These vulnerability scores are then mapped in the attack tree to reveal the critical nodes and shortest path to the target node. The mitigating strategy framework suggests the most efficient mitigation strategy to counter vulnerabilities by examining the node’s functionality, its locality, centrality, criticality, cascading impacts, available resources, and performance thresholds. Various case studies were conducted to analyse and evaluate our proposed vulnerability modelling and mitigation strategies on realistic supply chain systems. These analyses and evaluations confirm that the proposed solutions are highly effective for modelling the vulnerabilities while the mitigation strategies reduce the risks in dynamic and resource-constrained environments. The unified vulnerability modelling of hybrid networks minimises ambiguities, reduces complexities and identifies hidden deficiencies. It also improves system reliability and performance of heterogeneous networks while at the same time gaining acceptance for a universal vulnerability modelling framework across the cyber industry. The contributions have been published in reputable journals and conferences.Doctor of Philosoph

Generating datasets for anomaly-based intrusion detection systems in IoT and industrial IoT networks

Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin “powertrace” and the Cooja tool “Radio messages”