Gradient Masking of Label Smoothing in Adversarial Robustness
IEEE Access, 2021 Deep neural networks (DNNs) have achieved impressive results in several image classification tasks. However, these architectures are unstable for adversarial examples (AEs) such as inputs crafted by a hardly perceptible perturbation with the intent of ...
Hyungyu Lee, Ho Bae, Sungroh Yoon
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Detecting Evasion Attacks in Deployed Tree Ensembles
, 2023 sponsorship: This research is supported by the Research Foundation - Flanders (LD: 1SB1322N; LP: 1166222N), the Flemish Government under the "Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen" program (JD), the European Union's Horizon Europe Research and Innovation program under the grant agreement TUPLES No.
Devos, Laurens +3 more
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AppCon: Mitigating Evasion Attacks to ML Cyber Detectors [PDF]
Symmetry, 2020 Adversarial attacks represent a critical issue that prevents the reliable integration of machine learning methods into cyber defense systems. Past work has shown that even proficient detectors are highly affected just by small perturbations to malicious samples, and that existing countermeasures are immature.
Apruzzese G. +4 more
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Malware Collusion Attack against SVM: Issues and Countermeasures
Applied Sciences, 2018 Android has become the most popular mobile platform, and a hot target for malware developers. At the same time, researchers have come up with numerous ways to deal with malware.
Hongyi Chen +3 more
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Research on game strategy of underwater attack and defense process in typical situation
Xibei Gongye Daxue Xuebao, 2023 Aiming at the problem of underwater attack and defense in typical situations, a mathematical model of three-party attack and defense problem composed of torpedo, submarines and anti-torpedo torpedo is established. Under the condition of considering three-
WANG Zhong +3 more
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An Evasion and Counter-Evasion Study in Malicious Websites Detection [PDF]
, 2014 Malicious websites are a major cyber attack vector, and effective detection of them is an important cyber defense task. The main defense paradigm in this regard is that the defender uses some kind of machine learning algorithms to train a detection model,
Xu, Li +3 more
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Improving rotorcraft survivability to RPG attack using inverse methods [PDF]
, 2009 This paper presents the results of a preliminary investigation of optimal threat evasion strategies for improving the survivability of rotorcraft under attack by rocket propelled grenades (RPGs).
Anderson, D., Thomson, D.G.
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Dual-Targeted Textfooler Attack on Text Classification Systems
IEEE Access, 2023 Deep neural networks provide good performance on classification tasks such as those for image, audio, and text classification. However, such neural networks are vulnerable to adversarial examples.
Hyun Kwon
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Deep learning-driven multi-layer intrusion detection and prevention framework for resilient defense against adaptive evasion techniques in modern networks [PDF]
International Journal of Data and Network ScienceCurrent network security technologies face new threats from determined attackers employing advanced evasion techniques such as IP spoofing, tiny fragment attacks, tunneling, and HTML smuggling. Conventional intrusion detection and prevention systems
Dena Abu Laila +5 more
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Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning [PDF]
, 2018 Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity.
Biggio, Battista, Roli, Fabio
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