Applications of Machine Learning Algorithms for Countermeasures to Cyber Attacks

Chachra, Anjali; Sharma, Deepak

doi:10.2139/ssrn.3370181

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…In 2019, Chachra et al [45] discussed machine learning strategies for identifying the relationship of such strategies to cybersecurity. Machine learning is based on algorithms that learn from previous experiences to understand reoccurring patterns instead of programming the patterns themselves.…”

Section: Ai-based Cyberattacksmentioning

confidence: 99%

“…Computing devices and the internet have become an integral part of our society, and security professionals must adopt comprehensive strategies to counter the threats posed by AI-powered attacks. From this perspective, the researchers in [45] presented methods used to counter cyber security attacks and their relationship with machine learning models. The researchers have identified pitfalls and characterize challenges regarding machine learning algorithms in cybersecurity attacks.The researchers employed three basic network security systems for countering external and internal security breaches-namely, signature-based, anomaly-based, and hybrid.…”

Section: Mitigation Strategiesmentioning

confidence: 99%

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Weaponized AI for cyber attacks

Yamin

Ullah

et al. 2021

Journal of Information Security and Applications

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Section: Ai-based Cyberattacksmentioning

confidence: 99%

Section: Mitigation Strategiesmentioning

confidence: 99%

Weaponized AI for cyber attacks

Yamin

Ullah

et al. 2021

Journal of Information Security and Applications

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“…Machine learning has a proven ability to forecast future risks and threats, recognize threat patterns, and detect anomalies [14]. These capabilities have led to the development of several ML-based cybersecurity pipelines [15,16] in various industrial sectors.…”

Section: Introductionmentioning

confidence: 99%

Integrating MLSecOps in the Biotechnology Industry 5.0

Pervez,

J. Titus

2024

The Role of Cybersecurity in the Industry 5.0 Era [Working Title]

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Biotechnology Industry 5.0 is advancing with the integration of cutting-edge technologies such as Machine Learning (ML), the Internet of Things (IoT), and cloud computing. It is no surprise that an industry that utilizes data from customers and can alter their lives is a target of a variety of attacks. This chapter provides a perspective on how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices. This chapter explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting how crucial it is to comply with current regulatory frameworks. With biotechnology Industry 5.0 developing innovative solutions in healthcare, supply chain management, biomanufacturing, pharmaceutical sectors, and more, the chapter also discusses the MLSecOps best practices that industry and enterprises should follow while also considering ethical responsibilities. Overall, the chapter provides a discussion of how to integrate MLSecOps into the design, deployment, and regulation of the processes in the biotechnology Industry 5.0.

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