How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey

Bout, Émilie; Loscrì, Valeria; Gallais, Antoine

doi:10.1109/comst.2021.3127267

Cited by 59 publications

(20 citation statements)

References 175 publications

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“…Namely, adversarial machine learning can decrease integrity of the ML models. These attacks often occur during the learning phase and can lead to missclassifications [177], [178]. Further, the dynamic and complex nature of IoT can lead to vulnerabilities [179].…”

Section: Cyber Security In Precision Agriculturementioning

confidence: 99%

Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades

et al. 2022

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Precision agriculture represents the new age of conventional agriculture. This is made possible by the advancement of various modern technologies such as the internet of things. The unparalleled potential for data collection and analytics has resulted in an increase in multi-disciplinary research within machine learning and agriculture. However, the application of machine learning techniques to agriculture seems to be out of step with core machine learning research. This gap is further exacerbated by the inherent challenges associated with agricultural data. In this work, we conduct a systematic review of a large body of academic literature published between 2000 and 2022, on the application of machine learning techniques to agriculture. We identify and discuss some of the key data issues such as class imbalance, data sparsity and high dimensionality. Further, we study the impact of these data issues on various machine learning approaches within the context of agriculture. Finally, we identify some of the common pitfalls in the machine learning and agriculture research including the misapplication of machine learning evaluation techniques. To this end, this survey presents a holistic view on the state of affairs in the cross-domain of machine learning and agriculture and proposes some suitable mitigation strategies to address these challenges.

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Section: Cyber Security In Precision Agriculturementioning

confidence: 99%

Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades

et al. 2022

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“…In parallel, ML-based smart attacks have emerged, to drastically change the threat landscape [4]. Thanks to tools allowing the rapid creation of ML algorithms, this technology has become accessible to all.…”

Section: Smart Attack In the Literaturementioning

confidence: 99%

Evolution of IoT Security: The Era of Smart Attacks

Bout

Loscrì

Gallais

2022

IEEE Internet Things M.

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“…Such abundance inspired many literature surveys that aggregate or summarize the state-of-the-art. However, most of such studies may provide a detailed analysis but on a single application, such as cyber risk assessment [137] or IoT security [45]. Others may focus on a specific cyber detection problem, e.g., malware [13,71,160], spam [70,89] or intrusion detection [46,102].…”

Section: Related Workmentioning

confidence: 99%

The Role of Machine Learning in Cybersecurity

Apruzzese,

Laskov,

de Oca

et al. 2022

Preprint

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Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such discrepancy has its root cause in the current state-of-the-art, which does not allow to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience.This paper is the first attempt to provide a holistic understanding of the role of ML in the entire cybersecurity domain-to any potential reader with an interest in this topic. We highlight the advantages of ML with respect to human-driven detection methods, as well as the additional tasks that can be addressed by ML in cybersecurity. Moreover, we elucidate various intrinsic problems affecting real ML deployments in cybersecurity. Finally, we present how various stakeholders can contribute to future developments of ML in cybersecurity, which is essential for further progress in this field. Our contributions are complemented with two real case studies describing industrial applications of ML as defense against cyber-threats.

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How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey

Cited by 59 publications

References 175 publications

Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades

Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades

Evolution of IoT Security: The Era of Smart Attacks

The Role of Machine Learning in Cybersecurity

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