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Open AccessArticle

Applications in Security and Evasions in Machine Learning: A Survey

Computer/IT Engineering Department, Gujarat Technological University, Ahmedabad 382424, India
Department of Computer Science & Engineering, Pandit Deendayal Petroleum University, Gandhinagar 382007, India
Department of Information and Communications Engineering, Autonomous University of Barcelona, 08193 Barcelona, Spain
Author to whom correspondence should be addressed.
Electronics 2020, 9(1), 97;
Received: 4 October 2019 / Revised: 4 December 2019 / Accepted: 11 December 2019 / Published: 3 January 2020
In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications’ perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers’ knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks. View Full-Text
Keywords: security; privacy; adversarial attack; machine learning; attackers’ knowledge security; privacy; adversarial attack; machine learning; attackers’ knowledge
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Sagar, R.; Jhaveri, R.; Borrego, C. Applications in Security and Evasions in Machine Learning: A Survey. Electronics 2020, 9, 97.

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