Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes

19 December 2019

Papers citing "Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes"

27 / 27 papers shown

Title
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems Ping He Lorenzo Cavallaro Shouling Ji AAML 39 0 0 23 Jan 2025
CaFA: Cost-aware, Feasible Attacks With Database Constraints Against Neural Tabular Classifiers Matan Ben-Tov Daniel Deutch Nave Frost Mahmood Sharif AAML 107 0 0 20 Jan 2025
On the Robustness of Malware Detectors to Adversarial Samples Muhammad Salman B. Zhao H. Asghar Muhammad Ikram Sidharth Kaushik M. Kâafar AAML 29 0 0 05 Aug 2024
A Wolf in Sheep's Clothing: Practical Black-box Adversarial Attacks for Evading Learning-based Windows Malware Detection in the Wild Xiang Ling Zhiyu Wu Bin Wang Wei Deng Jingzheng Wu Shouling Ji Tianyue Luo Yanjun Wu AAML 36 1 0 03 Jul 2024
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection Marco Rando Luca Demetrio Lorenzo Rosasco Fabio Roli AAML 26 1 0 23 May 2024
SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines Andrea Ponte Dmitrijs Trizna Luca Demetrio Battista Biggio Ivan Tesfai Ogbu Fabio Roli 41 0 0 23 May 2024
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing Daniel Gibert Luca Demetrio Giulio Zizzo Quan Le Jordi Planes Battista Biggio AAML 33 2 0 01 May 2024
Machine Learning for Windows Malware Detection and Classification: Methods, Challenges and Ongoing Research Daniel Gibert AAML 34 1 0 29 Apr 2024
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing Daniel Gibert Giulio Zizzo Quan Le Jordi Planes AAML 31 4 0 23 Feb 2024
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors T. To Danhee Kim Do Thi Thu Hien Nghi Hoang Khoa Hien Do Hoang Phan The Duy V. Pham AAML 14 0 0 25 Sep 2023
Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing Daniel Gibert Giulio Zizzo Quan Le AAML 21 5 0 17 Aug 2023
URET: Universal Robustness Evaluation Toolkit (for Evasion) Kevin Eykholt Taesung Lee D. Schales Jiyong Jang Ian Molloy Masha Zorin AAML 33 6 0 03 Aug 2023
Hyper-parameter Tuning for Adversarially Robust Models Pedro Mendes Paolo Romano David Garlan AAML 11 2 0 05 Apr 2023
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness Shoumik Saha Wenxiao Wang Yigitcan Kaya S. Feizi Tudor Dumitras AAML 11 1 0 20 Mar 2023
Adversarial Attacks against Binary Similarity Systems Gianluca Capozzi Daniele Cono DÉlia Giuseppe Antonio Di Luna Leonardo Querzoni AAML 24 0 0 20 Mar 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 22 14 0 31 Jan 2023
ML-FEED: Machine Learning Framework for Efficient Exploit Detection Tanujay Saha Tamjid Al-Rahat N. Aaraj Yuan Tian N. Jha 20 3 0 11 Jan 2023
Instance Attack:An Explanation-based Vulnerability Analysis Framework Against DNNs for Malware Detection Ruijin Sun Shize Guo Jinhong Guo Changyou Xing Luming Yang Xi Guo Zhisong Pan AAML 19 1 0 06 Sep 2022
Black-box Attacks Against Neural Binary Function Detection Josh Bundt Michael Davinroy Ioannis Agadakos Alina Oprea William K. Robertson AAML 21 1 0 24 Aug 2022
On deceiving malware classification with section injection Adeilson Antonio da Silva Maurício Pamplona Segundo 23 4 0 12 Aug 2022
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware Luca Demetrio Battista Biggio Fabio Roli AAML 11 8 0 12 Jul 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 26 72 0 23 Dec 2021
MALIGN: Explainable Static Raw-byte Based Malware Family Classification using Sequence Alignment Shoumik Saha Sadia Afroz A. Rahman 17 4 0 28 Nov 2021
A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries P. Dasgupta Zachary Osman AAML 22 2 0 22 Nov 2021
Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights Omid Kargarnovin A. M. Sadeghzadeh R. Jalili AAML 13 7 0 27 Aug 2021
Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version] Jacopo Cortellazzi Feargus Pendlebury Daniel Arp Erwin Quiring Fabio Pierazzi Lorenzo Cavallaro AAML 19 0 0 05 Nov 2019
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 258 3,109 0 04 Nov 2016