Web of Science: 10 cites, Scopus: 12 cites, Google Scholar: cites
Applications in security and evasions in machine learning : a survey
Sagar, Ramani (Gujarat Technological University)
Jhaveri, Rutvij (Pandit Deendayal Petroleum University)
Borrego Iglesias, Carlos (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)

Data: 2020
Resum: 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.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Security ; Privacy ; Adversarial attack ; Machine learning ; Attackers' knowledge
Publicat a: Electronics, Vol. 9 (2020) , p. 1-42, ISSN 2079-9292

DOI: 10.3390/electronics9010097


42 p, 2.1 MB

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