Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation).

Area of Science:

• Computer Science
• Cybersecurity
• Software Engineering

Background:

• Android's open-source nature and widespread adoption have led to increased mobile device usage.
• The proliferation of Android devices has attracted malicious actors developing malware for various illicit purposes.
• Existing intrusion detection methods, like static analysis, require more comprehensive review articles.

Purpose of the Study:

• To systematically review and analyze research on static analysis for Android malware detection.
• To provide a taxonomy of static analysis steps, including reverse engineering, feature extraction, and classification.
• To identify research gaps and challenges in static analysis for Android malware.

Main Methods:

• A comprehensive literature review of articles published between 2009 and 2019.
• Analysis of static analysis methodologies, focusing on reverse engineering, feature engineering, and classification algorithms.
• Taxonomic classification of identified static analysis techniques.

Main Results:

• The study categorizes various static analysis techniques used in Android malware detection.
• Key challenges and limitations in current static analysis approaches are identified.
• A structured overview of research trends in Android malware static analysis over a decade is presented.

Conclusions:

• Static analysis remains a critical technique for understanding and detecting Android malware.
• Further research is needed to address identified challenges and improve the efficacy of static analysis.
• This review serves as a valuable resource for researchers and practitioners in mobile security.