2017/12/16 by Jack W. Stokes, Stokes, Jack W., De Wang +7 · 18 citations
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Adversarial machine learning #Adversarial system #Artificial intelligence #Classifier (UML) #Computer science #Computer security #Cryptography and Security (cs.CR) #Cryptovirology #Data mining #Deep learning #FOS: Computer and information sciences #Machine learning #Malware #Malware analysis #Physical Unclonable Functions (PUFs) and Hardware Security #Robustness (evolution) #Static analysis #Unpacking #cs.CR
paper · pdf · doi:10.48550/arxiv.1712.05919
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/12/16 · openalex publication_date 2017/12/16 · arxiv updated 2017/12/19 · openalex created_date 2018/01/05 · openalex updated_date 2026/07/28
Recently researchers have proposed using deep learning-based systems for malware detection. Unfortunately, all deep learning classification systems are vulnerable to adversarial attacks. Previous work has studied adversarial attacks against static analysis-based malware classifiers which only classify the content of the unknown file without execution. However, since the majority of malware is either packed or encrypted, malware classification based on static analysis often fails to detect these types of files. To overcome this limitation, anti-malware companies typically perform dynamic analysis by emulating each file in the anti-malware engine or performing in-depth scanning in a virtual machine. These strategies allow the analysis of the malware after unpacking or decryption. In this work, we study different strategies of crafting adversarial samples for dynamic analysis. These strategies operate on sparse, binary inputs in contrast to continuous inputs such as pixels in images. We then study the effects of two, previously proposed defensive mechanisms against crafted adversarial samples including the distillation and ensemble defenses. We also propose and evaluate the weight decay defense. Experiments show that with these three defensive strategies, the number of successfully crafted adversarial samples is reduced compared to a standard baseline system without any defenses. In particular, the ensemble defense is the most resilient to adversarial attacks. Importantly, none of the defenses significantly reduce the classification accuracy for detecting malware. Finally, we demonstrate that while adding additional hidden layers to neural models does not significantly improve the malware classification accuracy, it does significantly increase the classifier's robustness to adversarial attacks.