A Review on Attack Graph Analysis for IoT Vulnerability Assessment: Challenges, Open Issues, and Future Directions

Abstract

Vulnerability assessment in industrial IoT networks is critical due to the evolving nature of the domain and the increasing complexity of security threats. This study aims to address the existing gaps in the literature by conducting a comprehensive survey on the use of attack graphs for vulnerability assessment in IoT networks. Attack graphs serve as a valuable cybersecurity tool for modeling and analyzing potential attack scenarios on systems, networks, or applications. The survey covers the research conducted between 2016 and 2021(34 peer-reviewed journal articles and 28 conference papers), identifying and categorizing the main methodologies and technologies employed in generating and analyzing attack graphs. In this review, core modeling techniques for IoT vulnerability assessment are highlighted, such as Markov Decision Processes (MDP), Feature Pyramid Networks (FPN), K-means clustering, and logistic regression models, along with other techniques involving genetic algorithms like fast-forward (FF), contingent fast-forwards (CFF), advanced reinforcement-learning algorithms, and HARMs models. The evaluation of the performance of these attack graph models using IoT networks or devices as case studies is also emphasized. This survey provides valuable insights into the state-of-the-art attack graph techniques for IoT network vulnerability assessment, identifying various applications, performances, research opportunities, and challenges. As a reference source, it serves to inform academicians and practitioners interested in leveraging attack graphs for IoT network vulnerability assessment and guides future research directions in this area.INDEX TERMS Attack graph, the Internet of Things, network vulnerabilities, vulnerability assessment.

A Survey on Key Agreement and Authentication Protocol for Internet of Things Application

“…Various methods are available for assessing and detecting attacks to improve the security of the IoT. Attack graphs combined with other methods, such as game theory and machine learning, help assess the vulnerability of IoT networks [103]. Recent research focuses on using deep learning methods to design malware detection systems [104].…”

Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection

“…GAs are metaheuristics that mimic the Darwinian evolutionary process, wherein the fittest individuals are more likely to transmit their traits to the next generation. GAs have been applied successfully to a variety of problems such as vulnerability assessment [32,33], protection coordination [34,35], expansion planning [36,37], etc. Furthermore, it has been demonstrated that GAs are effective in addressing the problem of UFLS scheme dimen-sioning, as indicated in [38][39][40].…”

Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

“…It reveals all potential vulnerability combinations and their relationships and lists all potential attack paths from the perspective of the attacker to reflect the security state of the network, such as the number of attack paths, the length of the shortest attack path, and the key vulnerability. Multi-step attacks can be effectively prevented based on the attack graph [11,12]. To enhance the relevance between vulnerability assessment and network systems, many studies have conducted network vulnerability analysis based on attack graphs.…”