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Advances in Electrical and Computer Engineering, 2023 In recent years, machine learning (ML) has had a significant influence on the discipline of computer security. In network security, intrusion detection systems increasingly employ machine learning techniques.
NATHANIEL, D., SOOSAI, A.
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Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019 Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case.
Tramèr, Florian +4 more
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Sensors, 2023 Intrusion detection and prevention are two of the most important issues to solve in network security infrastructure. Intrusion detection systems (IDSs) protect networks by using patterns to detect malicious traffic. As attackers have tried to dissimulate
Andrei-Grigore Mari +2 more
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Machine learning uncertainties with adversarial neural networks [PDF]
The European Physical Journal C, 2019 10 pages, 6 figures, v2: published ...
Englert, Christoph +3 more
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A Distributed Biased Boundary Attack Method in Black-Box Attack
Applied Sciences, 2021 The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios.
Fengtao Xiang +3 more
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Adversarial Machine Learning: Bayesian Perspectives
Journal of the American Statistical Association, 2023 Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting machine learning (ML) systems against security threats: in certain scenarios there may be adversaries that actively manipulate input data to fool learning systems.
Rios Insua, David +3 more
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Politics of Adversarial Machine Learning [PDF]
SSRN Electronic Journal, 2020 In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights.
Albert, Kendra +3 more
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SSQLi: A Black-Box Adversarial Attack Method for SQL Injection Based on Reinforcement Learning
Future Internet, 2023 SQL injection is a highly detrimental web attack technique that can result in significant data leakage and compromise system integrity. To counteract the harm caused by such attacks, researchers have devoted much attention to the examination of SQL ...
Yuting Guan +4 more
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Are Accuracy and Robustness Correlated?
, 2016 Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors.
Boult, Terrance E. +2 more
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Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains
, 2017 While modern day web applications aim to create impact at the civilization level, they have become vulnerable to adversarial activity, where the next cyber-attack can take any shape and can originate from anywhere. The increasing scale and sophistication
Kantardzic, Mehmed, Sethi, Tegjyot Singh
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