Securing Internet of Things (IoT) with machine learning

Zeadally, Sherali; Tsikerdekis, Michail

doi:10.1002/dac.4169

Cited by 63 publications

(31 citation statements)

References 50 publications

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“…This shows that taxonomy perspectives are converging, making it less essential to define machine learning algorithms based on whether they are supervised or unsupervised [59]. Henceforth, we present an in-depth discussion of machine learning algorithms from a taxonomy perspective as described in [60], but in this section, we discuss the predominant machine learning techniques that are effective for cybersecurity solutions.…”

Section: A Machine Learningmentioning

confidence: 99%

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

et al. 2020

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Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.

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Section: A Machine Learningmentioning

confidence: 99%

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

et al. 2020

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“…Supervised vs. unsupervised machine learning: Machine learning models are also used for anomaly and intrusion detection in order to solve some of the shortcomings of supervised machine learning models [28]. Supervised classifier models require large datasets that contain examples of each class that they are classifying (e.g., malicious or legitimate accounts).…”

Section: Detection Methodsmentioning

confidence: 99%

Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

2020

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Identity deception in online social networks is a pervasive problem. Ongoing research is developing methods for identity deception detection. However, the real-world efficacy of these methods is currently unknown because they have been evaluated largely through laboratory experiments. We present a review of representative state-of-the-art results on identity deception detection. Based on this analysis, we identify common methodological weaknesses for these approaches, and we propose recommendations that can increase their effectiveness for when they are applied in real-world environments.

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“…The research examined various shallow ML algorithms along with the performance accuracy and expose the challenges to implementation. However, Zeadally and Tsikerdekis [69] reviewed IoT device properties and some common attacks. Authors also classified host-based and network-based security solutions using supervised, unsupervised learning techniques as well as addressed the necessity of existing ML methods improvement to adopt the constrained IoT environment.…”

Section: Related Review Workmentioning

confidence: 99%

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

et al. 2021

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The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resourceconstrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques can not be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms-branches of Artificial Intelligence (AI)-can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AIbased security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

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Securing Internet of Things (IoT) with machine learning

Cited by 63 publications

References 50 publications

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Vulnerabilities to Online Social Network Identity Deception Detection Research and Recommendations for Mitigation

Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

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