On the Security Risks of ML-based Malware Detection Systems: A Survey

16 May 2025

Papers citing "On the Security Risks of ML-based Malware Detection Systems: A Survey"

39 / 39 papers shown

Title
Adapting Novelty towards Generating Antigens for Antivirus systems Ritwik Murali C Shunmuga Velayutham 44 7 0 24 May 2025
CTBENCH: A Library and Benchmark for Certified Training Yuhao Mao Stefan Balauca Martin Vechev OOD 100 5 0 07 Jun 2024
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks Yuyang Zhou Guang Cheng Zongyao Chen Shui Yu AAML 91 5 0 11 Dec 2023
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance Savino Dambra Yufei Han Simone Aonzo Platon Kotzias Antonino Vitale Juan Caballero Davide Balzarotti Leyla Bilge 78 26 0 27 Jul 2023
Expressive Losses for Verified Robustness via Convex Combinations Alessandro De Palma Rudy Bunel Krishnamurthy Dvijotham M. P. Kumar Robert Stanforth A. Lomuscio AAML 95 14 0 23 May 2023
MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection Aqib Rashid Jose Such AAML 98 10 0 21 Feb 2023
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion Zhuoqun Huang Neil G. Marchant Keane Lucas Lujo Bauer O. Ohrimenko Benjamin I. P. Rubinstein AAML 85 17 0 31 Jan 2023
Towards A Proactive ML Approach for Detecting Backdoor Poison Samples Xiangyu Qi Tinghao Xie Jiachen T. Wang Tong Wu Saeed Mahloujifar Prateek Mittal AAML 80 52 0 26 May 2022
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers Limin Yang Zhi Chen Jacopo Cortellazzi Feargus Pendlebury Kevin Tu Fabio Pierazzi Lorenzo Cavallaro Gang Wang AAML 101 37 0 11 Feb 2022
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art Xiang Ling Lingfei Wu Jiangyu Zhang Zhenqing Qu Wei Deng ... Chunming Wu S. Ji Tianyue Luo Jingzheng Wu Yanjun Wu AAML 95 81 0 23 Dec 2021
Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors Ruoxi Sun Minhui Xue Gareth Tyson Tian Dong Shaofeng Li Shuo Wang Haojin Zhu S. Çamtepe Surya Nepal AAML 116 15 0 19 Nov 2021
SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection Richard E. Harang Ethan M. Rudd 66 103 0 14 Dec 2020
Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift Federico Barbero Feargus Pendlebury Fabio Pierazzi Lorenzo Cavallaro 70 74 0 08 Oct 2020
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection Lan Zhang Peng Liu Yoon-Ho Choi Ping Chen AAML 90 38 0 11 Sep 2020
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection Deqiang Li Qianmu Li AAML 63 126 0 30 Jun 2020
A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices Ruitao Feng Sen Chen Xiaofei Xie Guozhu Meng Shang-Wei Lin Yang Liu 89 104 0 11 May 2020
Blind Backdoors in Deep Learning Models Eugene Bagdasaryan Vitaly Shmatikov AAML FedML SILM 142 305 0 08 May 2020
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation Bozhi Wu Sen Chen Cuiyun Gao Lingling Fan Yang Liu W. Wen Michael R. Lyu 83 58 0 24 Apr 2020
Functionality-preserving Black-box Optimization of Adversarial Windows Malware Christian Scano Battista Biggio Giovanni Lagorio Fabio Roli A. Armando AAML 82 145 0 30 Mar 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 241 1,861 0 03 Mar 2020
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes Keane Lucas Mahmood Sharif Lujo Bauer Michael K. Reiter S. Shintre AAML 83 68 0 19 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 580 42,677 0 03 Dec 2019
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries Christian Scano Battista Biggio Giovanni Lagorio Fabio Roli A. Armando AAML 64 131 0 11 Jan 2019
Poisoning Behavioral Malware Clustering Battista Biggio Konrad Rieck Andrea Valenza Christian Wressnegger Igino Corona Giorgio Giacinto Fabio Roli 71 152 0 25 Nov 2018
Exploring Adversarial Examples in Malware Detection Octavian Suciu Scott E. Coull Jeffrey Johns AAML 92 193 0 18 Oct 2018
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples Deqiang Li Ramesh Baral Tao Li Han Wang Qianmu Li Shouhuai Xu AAML 63 21 0 18 Sep 2018
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection Xiao Chen Chaoran Li Derui Wang S. Wen Jun Zhang Surya Nepal Yang Xiang K. Ren AAML 71 246 0 10 Aug 2018
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time Feargus Pendlebury Fabio Pierazzi Roberto Jordaney Johannes Kinder Lorenzo Cavallaro 88 359 0 20 Jul 2018
Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning Hyrum S. Anderson Anant Kharkar Bobby Filar David Evans P. Roth AAML 90 210 0 26 Jan 2018
Malware Detection by Eating a Whole EXE Edward Raff Jon Barker Jared Sylvester Robert Brandon Bryan Catanzaro Charles K. Nicholas 84 546 0 25 Oct 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 163 2,160 0 21 Aug 2017
Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach Sen Chen Minhui Xue Lingling Fan S. Hao Lihua Xu Haojin Zhu Yue Liu AAML 82 221 0 13 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,135 0 22 May 2017
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection Ambra Demontis Marco Melis Battista Biggio Davide Maiorca Dan Arp Konrad Rieck Igino Corona Giorgio Giacinto Fabio Roli AAML 66 284 0 28 Apr 2017
Adversary Resistant Deep Neural Networks with an Application to Malware Detection Qinglong Wang Wenbo Guo Kaixuan Zhang Alexander Ororbia Masashi Sugiyama C. Lee Giles Xue Liu AAML 91 175 0 05 Oct 2016
Stealing Machine Learning Models via Prediction APIs Florian Tramèr Fan Zhang Ari Juels Michael K. Reiter Thomas Ristenpart SILM MLAU 109 1,813 0 09 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,593 0 16 Aug 2016
Adversarial Perturbations Against Deep Neural Networks for Malware Classification Kathrin Grosse Nicolas Papernot Praveen Manoharan Michael Backes Patrick McDaniel AAML 90 418 0 14 Jun 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,145 0 20 Dec 2014