An Investigation on Fragility of Machine Learning Classifiers in Android Malware Detection

Husnain Rafiq, Nauman Aslam, Biju Issac, Rizwan Hamid Randhawa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)
157 Downloads (Pure)

Abstract

Machine learning (ML) classifiers have been increasingly used in Android malware detection and countermeasures for the past decade. However, ML-based solutions are vulnerable to adversarial evasion attacks. An attacker can craft a malicious sample carefully to fool an underlying pre-trained classifier. In this paper, we highlight the fragility of the ML classifiers against adversarial evasion attacks. We perform mimicry attacks based on Oracle and Generative Adversarial Network (GAN) against these classifiers using our proposed methodology. We use static analysis on Android applications to extract API-based features from a balanced excerpt of a well-known public dataset. The empirical results demonstrate that among ML classifiers, the detection capability of linear classifiers can be reduced as low as 0% by perturbing only up to 4 out of 315 extracted API features. As a countermeasure, we propose TrickDroid, a cumulative adversarial training scheme based on Oracle and GAN-based adversarial data to improve evasion detection. The experimental results of cumulative adversarial training achieves a remarkable detection accuracy of up to 99.46% against adversarial samples.
Original languageEnglish
Title of host publicationIEEE INFOCOM 2022
Subtitle of host publicationIEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Place of PublicationPiscataway, US
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9781665409261
ISBN (Print)9781665409278
DOIs
Publication statusPublished - 2 May 2022
EventThe Sixth IEEE International Workshop on the Security, Privacy, and Digital Forensics of Mobile Systems and Networks (MobiSec 2022): in conjunction with IEEE International Conference on Computer Communications, INFOCOM 2022 - Virtual
Duration: 2 May 20225 May 2022
https://infocom2022.ieee-infocom.org/sixth-ieee-international-workshop-security-privacy-and-digital-forensics-mobile-systems-and-networks

Conference

ConferenceThe Sixth IEEE International Workshop on the Security, Privacy, and Digital Forensics of Mobile Systems and Networks (MobiSec 2022)
CityVirtual
Period2/05/225/05/22
Internet address

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