Black Box Analysis of Android Malware Detectors by Guruswamy Nellaivadivelu Code obfuscation can make it challenging to detect malware in Android devices. Malware writers obfuscate the code of their programs by employing various techniques that attempt to hide the true purpose of the program. Malware detectors can use a number of features to classify a program as a malware. If the malware detector uses a feature that is obfuscated, then the malware detector will likely fail to classify the malware as malicious software. In this research, we obfuscate selected features of known malware and determine whether the malware can still be detected by a given detector. Using this approach, we show that we can effectively perform black box analysis of various malware detectors. ACKNOWLEDGMENTS I would like to express my deep gratitude to Dr. Stamp for his valuable guidance and supervision throughout the project work. I am also extremely thankful to him for teaching me interesting new concepts and techniques in the field of security during the entire course of my graduate program. I am also grateful to my committee members for reviewing my work and providing constructive feedback. v C.25 Detection Rates after applying the Obfuscator Renaming, Reordering, Goto, and Arithmetic Branch. Average: 0.403457. .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.