Zobrazeno 1 - 10
of 119
pro vyhledávání: '"Grobler, Marthie"'
Software vulnerabilities (SVs) have become a common, serious, and crucial concern to safety-critical security systems. That leads to significant progress in the use of AI-based methods for software vulnerability detection (SVD). In practice, although
Externí odkaz:
http://arxiv.org/abs/2404.05964
Phishing attacks have become a serious and challenging issue for detection, explanation, and defense. Despite more than a decade of research on phishing, encompassing both technical and non-technical remedies, phishing continues to be a serious probl
Externí odkaz:
http://arxiv.org/abs/2402.17092
Autor:
Whitty, Monica Therese, Ruddy, Christopher, Keatley, David, Butavicius, Marcus, Grobler, Marthie
Publikováno v:
Information & Computer Security, 2024, Vol. 32, Issue 4, pp. 509-522.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ICS-11-2023-0210
Autor:
Zelenkova, Reena, Swallow, Jack, Chamikara, M. A. P., Liu, Dongxi, Chhetri, Mohan Baruwal, Camtepe, Seyit, Grobler, Marthie, Almashor, Mahathir
Biometric data, such as face images, are often associated with sensitive information (e.g medical, financial, personal government records). Hence, a data breach in a system storing such information can have devastating consequences. Deep learning is
Externí odkaz:
http://arxiv.org/abs/2202.10320
Autor:
Chamikara, M. A. P., Liu, Dongxi, Camtepe, Seyit, Nepal, Surya, Grobler, Marthie, Bertok, Peter, Khalil, Ibrahim
Advanced adversarial attacks such as membership inference and model memorization can make federated learning (FL) vulnerable and potentially leak sensitive private data. Local differentially private (LDP) approaches are gaining more popularity due to
Externí odkaz:
http://arxiv.org/abs/2202.06053
Autor:
Jiang, Liuyue, Jayatilaka, Asangi, Nasim, Mehwish, Grobler, Marthie, Zahedi, Mansooreh, Babar, M. Ali
The dynamics of cyber threats are increasingly complex, making it more challenging than ever for organizations to obtain in-depth insights into their cyber security status. Therefore, organizations rely on Cyber Situational Awareness (CSA) to support
Externí odkaz:
http://arxiv.org/abs/2112.10354
Previous robustness approaches for deep learning models such as data augmentation techniques via data transformation or adversarial training cannot capture real-world variations that preserve the semantics of the input, such as a change in lighting c
Externí odkaz:
http://arxiv.org/abs/2105.04070
Autor:
Wang, Shuo, Nepal, Surya, Moore, Kristen, Grobler, Marthie, Rudolph, Carsten, Abuadbba, Alsharif
The diversity and quantity of data warehouses, gathering data from distributed devices such as mobile devices, can enhance the success and robustness of machine learning algorithms. Federated learning enables distributed participants to collaborative
Externí odkaz:
http://arxiv.org/abs/2105.00602
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we investigate an attack-agnostic defense against adversarial attacks on high-resolution image
Externí odkaz:
http://arxiv.org/abs/2006.09701
Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purif
Externí odkaz:
http://arxiv.org/abs/2002.02007