Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis

Autor: Akter, Mst Shapna, Shahriar, Hossain, Iqbal, Iysa, Hossain, MD, Karim, M. A., Clincy, Victor, Voicu, Razvan

The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying

Externí odkaz: http://arxiv.org/abs/2305.19593

APTracker: A Comprehensive and Analytical Malware Dataset Based on Attribution to APT Groups

Autor: Mohamad Erfan Mazaheri, Alireza Shameli-Sendi

Publikováno v: IEEE Access, Vol 12, Pp 145148-145158 (2024)

Malware poses a significant threat to organizations, necessitating robust countermeasures. One such measure involves attributing malware to its respective Advanced Persistent Threat (APT) group, which serves several purposes, two of the most importan

Externí odkaz: https://doaj.org/article/728706fed1d6438db17352aa78efa3a2

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Privacy Parameter Variation Using RAPPOR on a Malware Dataset

Autor: Aaby, Peter, De Acuna, Juanjo Mata, Macfarlane, Richard, Buchanan, William J

Publikováno v: 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)

Stricter data protection regulations and the poor application of privacy protection techniques have resulted in a requirement for data-driven companies to adopt new methods of analysing sensitive user data. The RAPPOR (Randomized Aggregatable Privacy

Externí odkaz: http://arxiv.org/abs/1907.10387

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