Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Chen, Zhiyan; Liu, Jinxin; Shen, Yu; Şimşek, Murat; Kantarcı, Burak; Mouftah, Hussein T.; Djukic, Petar

doi:10.1145/3530812

Cited by 57 publications

(12 citation statements)

References 170 publications

(181 reference statements)

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“…Chen et al [81] proposed a survey of recent research on IoT assaults and associated defenses, with an emphasis on machine learning (ML)-based techniques for mitigate attacks on IoT networks. A review of IIoT security, specifically as it relates to Industrial Control Systems, comes after the introduction of IoT and Industrial IoT (IIoT).…”

Section: Blockchain In Iot Based Environmentmentioning

confidence: 99%

Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

Deepak,

Gulia,

Gill

et al. 2024

IEEE Access

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Internet of Things (IoT) plays an essential contribution in connecting devices and enabling seamless data exchange, leading to increased efficiency and convenience. However, security concerns in IoT systems are significant, as compromised devices can lead to data breaches and privacy violations. Blockchain technology can enhance IoT security by providing decentralized consensus, immutability, and transparent transaction records, ensuring secure and trustworthy communication and data integrity. This review article gives a succinct but thorough understanding of blockchain technology, covering architecture of blockchain, working principles, types, applications, platforms, and its role in the IoT environment. The study highlights potential benefits of blockchain like enhanced security and privacy, and explores its integration with IoT. Additionally, the study discusses various real-world applications, examines blockchain platforms, and addresses the limitations and challenges associated with blockchain technology. This review serves as a valuable resource for researchers and practitioners seeking a deeper understanding of blockchain's potential and its implications in the IoT landscape.INDEX TERMS Blockchain, Data Exchange, Internet of Things (IoT), Privacy, Security.

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Section: Blockchain In Iot Based Environmentmentioning

confidence: 99%

Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

Deepak,

Gulia,

Gill

et al. 2024

IEEE Access

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“…3) IoT Network: The top cited research papers discovered by this study are [43] and [44]. The first paper discussed Advanced Persistent Threats (APT) detection-related challenges and unsolved issues using ML.…”

Section: A Cluster Analysismentioning

confidence: 99%

Scientometric Analysis and Knowledge Mapping of Cybersecurity

Alqurashi,

Ahmad

2024

IJACSA

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Cybersecurity research includes several areas, such as authentication, software and hardware vulnerabilities, and defences against cyberattacks. However, only a limited number of cybersecurity experts have a comprehensive understanding of all aspects of this sector. Hence, it is vital to possess an impartial comprehension of the prevailing patterns in cybersecurity research. Scientometric analysis and knowledge mapping may effectively detect cybersecurity research trends, significant studies, and emerging technologies within this particular context. The main aim of this research is to comprehend the developmental trend of the academic literature about the concepts of "malware detection" and 'cybersecurity'. We collected 9,967 publications from January 2019 to December 2023 and used the Citespace tool for scientometric analysis. This study found six co-citation clusters,namely malware classification, evading malware classifier, android malware detection, IoT network, CNN, and ransomeware families. Additionally, this study discovered that the top contributing countries are the USA, China, and India based on the citation count, and the Chinese Academy of Science, the University of California, and the University of Texas are the top contributing institutions based on the frequency of the publications.

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“…Additionally, machine learning-based detection methods have been studied recently [19]. A study by Chen et al [19] discussed how to detect APT attacks in IoT environments.…”

Section: Detection Of Cyberattacks By Using Threat Detectorsmentioning

confidence: 99%

“…Additionally, machine learning-based detection methods have been studied recently [19]. A study by Chen et al [19] discussed how to detect APT attacks in IoT environments. This prior study raises the problem that it is challenging to apply ML-based detection methods due to the lack of datasets for APT attacks.…”

Section: Detection Of Cyberattacks By Using Threat Detectorsmentioning

confidence: 99%

An Effective Threat Detection Framework for Advanced Persistent Cyberattacks

Jeon¹,

Lee²,

Lee³

et al. 2023

Computers, Materials &Amp; Continua

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Recently, with the normalization of non-face-to-face online environments in response to the COVID-19 pandemic, the possibility of cyberattacks through endpoints has increased. Numerous endpoint devices are managed meticulously to prevent cyberattacks and ensure timely responses to potential security threats. In particular, because telecommuting, telemedicine, and teleeducation are implemented in uncontrolled environments, attackers typically target vulnerable endpoints to acquire administrator rights or steal authentication information, and reports of endpoint attacks have been increasing considerably. Advanced persistent threats (APTs) using various novel variant malicious codes are a form of a sophisticated attack. However, conventional commercial antivirus and anti-malware systems that use signature-based attack detection methods cannot satisfactorily respond to such attacks. In this paper, we propose a method that expands the detection coverage in APT attack environments. In this model, an open-source threat detector and log collector are used synergistically to improve threat detection performance. Extending the scope of attack log collection through interworking between highly accessible open-source tools can efficiently increase the detection coverage of tactics and techniques used to deal with APT attacks, as defined by MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK). We implemented an attack environment using an APT attack scenario emulator called Carbanak and analyzed the detection coverage of Google Rapid Response (GRR), an open-source threat detection tool, and Graylog, an open-source log collector. The proposed method expanded the detection coverage against MITRE ATT&CK by approximately 11% compared with that conventional methods.

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Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Cited by 57 publications

References 170 publications

Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

Exploring the Potential of Blockchain Technology in an IoT-Enabled Environment: A Review

Scientometric Analysis and Knowledge Mapping of Cybersecurity

An Effective Threat Detection Framework for Advanced Persistent Cyberattacks

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