Anti-malware engines under adversarial attacks

Abstract

Mobile phones have crawled into our lives with such rapidity and have reformed our lives in a short span. Malware is entangled with all forms of mobile applications causing havoc and distress. State of the art malware detection systems have exercised learning-based techniques successfully to discriminate benign contents from malware. But, Machine Learning (ML) models are vulnerable to adversarial samples and are not intrinsically robust against adversarial attacks. The adversarial samples generated against ML models degrade the model's performance. Adversarial attacks are utilized by malware authors to hinder the working of ML-based malware detection approaches. This article coheres into the effects of evasion attacks on an anti-malware engine utilizing a feed forward deep neural network model. Experiments on Android malware apps is explored by structuring a comprehensive feature engineering scheme for the Drebin dataset through static analysis. The results demonstrate the realistic threat and demand the need to develop adaptive defenses to foster a secure learning model which is immune to adversarial attacks.

Keywords:

• Evasion attacks
• Android
• malware detection
• adversarial attacks
• deep neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Shymalagowri Selvaganapathy

Shymalagowri Selvaganapathy is working as Assistant Professor in the department of Information Technology, PSG College of Technology, India since 2012. Her research interests include Malware detection, Adversarial machine learning, Attacks and Defense techniques and Information Security.

Sudha Sadasivam

Dr. Sudha Sadasivam is working as a Professor as is heading the Department of Computer Science and Engineering in PSG College of Technology, India. Her areas of interest include distributed systems, distributed object technology, and grid and cloud computing. She has published 75+ papers in refereed international and national journals, and at conferences. She has published five books in her areas of interest. She has coordinated two AICTE RPS projects in distributed and grid computing arena. She is also the coordinator for PSG-Yahoo research on grid and cloud computing.