Adversarial Attacks on Transformers-Based Malware Detectors

2022-10-01 · Yash Jakhotiya, Heramb Patil, Jugal Rawlani, Dr. Sunil B. Mane

Signature-based malware detectors have proven to be insufficient as even a small change in malignant executable code can bypass these signature-based detectors. Many machine learning-based models have been proposed to efficiently detect a wide variety of malware. Many of these models are found to be susceptible to adversarial attacks - attacks that work by generating intentionally designed inputs that can force these models to misclassify. Our work aims to explore vulnerabilities in the current state of the art malware detectors to adversarial attacks. We train a Transformers-based malware detector, carry out adversarial attacks resulting in a misclassification rate of 23.9% and propose defenses that reduce this misclassification rate to half. An implementation of our work can be found at https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers.

📄 PDF Abstract BibTeX arXiv:2210.00008

Code (1)

yashjakhotiya/adversarial-attacks-on-transformers 공식 구현 tf

PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks

2023-02-22 · Deqiang Li, Shicheng Cui, Yun Li, Jia Xu 외

Machine Learning (ML) techniques can facilitate the automation of malicious software (malware for short) detection, but suffer from evasion attacks. Many studies counter such attacks in heuristic manners, lacking theoret…

Malware Detection

Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model

2020-12-14 · MohammadReza Ebrahimi, Ning Zhang, James Hu, Muhammad Taqi Raza 외

Anti-malware engines are the first line of defense against malicious software. While widely used, feature engineering-based anti-malware engines are vulnerable to unseen (zero-day) attacks. Recently, deep learning-based …

Deep Learning Feature Engineering Language Modeling Language Modelling+1

Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors

2021-11-19 · Ruoxi Sun, Minhui Xue, Gareth Tyson, Tian Dong 외

Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection, e.g., by performing feature-space manipu…

Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection

2020-06-30 · Deqiang Li, Qianmu Li

Malware remains a big threat to cyber security, calling for machine learning based malware detection. While promising, such detectors are known to be vulnerable to evasion attacks. Ensemble learning typically facilitates…

Ensemble Learning Malware Detection

Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach

2021-12-03 · James Lee Hu, MohammadReza Ebrahimi, Hsinchun Chen

Deep Learning (DL)-based malware detectors are increasingly adopted for early detection of malicious behavior in cybersecurity. However, their sensitivity to adversarial malware variants has raised immense security conce…

Language Modeling Language Modelling