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Keyword

evasion attacks

Description: Evasion attacks are adversarial techniques that manipulate inputs to deceive machine learning systems at prediction time, often causing incorrect classifications or decisions.
OpenAlex ID: keywords/evasion-attacks
Evasion attacks against machine learning at test time
2013 · Biggio, B., Corona, I., et al. · 1,621
Adversarial machine learning
2011 · Ling Huang, Anthony D. Joseph, et al. · 1,194
Evasion Attacks against Machine Learning at Test Time
2013 · Battista Biggio, Igino Corona, et al. · Lecture notes in computer science · 890
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Adversarial learning
2005 · Daniel Lowd, Christopher Meek · 754
Mimicry attacks on host-based intrusion detection systems
2002 · David Wagner, Paolo Soto · Proceedings of the ACM Conference on Computer and Communications Security · 714
Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection
2019 · Simen Thys, Wiebe Van Ranst, et al. · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · 712
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Insertion, Evasion, and Denial of Service: Eluding Network Intrusion Detection
1998 · Thomas H. Ptacek, Timothy N. Newsham · 653
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
2022 · Weiwei Hu, Ying Tan · Communications in computer and information science · 547
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Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
2017 · Carlini, Nicholas, Wagner, David · arXiv (Cornell University) · 357
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Intriguing Properties of Adversarial ML Attacks in the Problem Space
2020 · Fabio Pierazzi, Feargus Pendlebury, et al. · Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · 309
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Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
2017 · Ambra Demontis, Marco Melis, et al. · IEEE Transactions on Dependable and Secure Computing · 292
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Detecting AI Trojans Using Meta Neural Analysis
2021 · Xiaojun Xu, Qi Wang, et al. · Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · 271
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A taxonomy and survey of attacks against machine learning
2019 · Nikolaos Pitropakis, Emmanouil Panaousis, et al. · Computer Science Review · 266
Adversarial machine learning in Network Intrusion Detection Systems
2021 · Elie Alhajjar, Paul Maxwell, et al. · Expert Systems with Applications · 255
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Adversarial Feature Selection Against Evasion Attacks
2015 · Fei Zhang, Patrick P. K. Chan, et al. · IEEE Transactions on Cybernetics · 243
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Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems
2006 · Roberto Perdisci, Guofei Gu, et al. · Proceedings · 241
Realtime Robust Malicious Traffic Detection via Frequency Domain Analysis
2021 · Chuanpu Fu, Qi Li, et al. · ACM SIGSAC Conference on Computer and Communications Security (CCS) · 235
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Privacy Risks of Securing Machine Learning Models against Adversarial Examples
2019 · Liwei Song, Reza Shokri, et al. · ACM SIGSAC Conference on Computer and Communications Security (CCS) · 229
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Enhancing robustness of machine learning systems via data transformations
2018 · Arjun Nitin Bhagoji, Daniel Cullina, et al. · Conference on Information Sciences and Systems · 227
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Exploring Adversarial Examples in Malware Detection
2019 · Octavian Suciu, Scott E. Coull, et al. · IEEE Security and Privacy Workshops (SPW) · 226
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Efficient Cyber Attack Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA
2021 · Moshe Kravchik, Asaf Shabtai · IEEE Transactions on Dependable and Secure Computing · 221
Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers
2016 · Weilin Xu, Yanjun Qi, et al. · Network and Distributed System Security Symposium · 221
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Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
2018 · Demontis, Ambra, Melis, Marco, et al. · arXiv (Cornell University) · 218
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Generative Adversarial Networks For Launching and Thwarting Adversarial Attacks on Network Intrusion Detection Systems
2019 · Muhammad Usama, Muhammad Asim, et al. · 208
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors
2021 · Dongqi Han, Zhiliang Wang, et al. · IEEE Journal on Selected Areas in Communications · 193
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Works count: 1,928
Citation count: 28,480
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