BINARY IMAGE BASED STATIC ANDROID MALWARE DETECTION USING DEEP LEARNING

Abstract

Android is an immensely popular operating system, used by the majority of people worldwide. Its widespread adoption, however, makes it a prime target for attackers. As technology advances, these attackers are becoming increasingly sophisticated, devising new techniques to exploit Android users. They create numerous malicious apps designed to steal data, commit identity theft, monitor users, and sometimes encrypt data to demand ransom for its recovery. This malicious activity creates a fearful environment for Android users, who become hesitant to install new applications due to the risk of critical data being compromised. To combat this, various methods have been developed to identify malicious apps. Traditional approaches include signature-based detection, which relies on recognizing known malware signatures, and pattern-based analysis, which looks for suspicious patterns in-app behaviour. While these methods have their merits, they also have limitations. Signature-based detection can be easily bypassed by new malware variants that do not match existing signatures. Pattern-based analysis may struggle with novel threats that do not conform to known patterns. The dynamic analysis approach is impractical for screening large numbers of applications, especially given the rapid growth of the Android app ecosystem. As the population increases and the number of apps continues to surge, relying solely on dynamic analysis will become increasingly challenging. To address these limitations, there is an urgent need for new approaches that are both fast and efficient. Advanced methodologies such as machine learning and deep learning show great promise in this regard. The goal is to develop a robust and efficient security framework that not only detects existing threats but also adapts to emerging ones. By integrating advanced technological solutions and refining detection strategies, we can create a more resilient Android ecosystem. This will ensure that users can enjoy the benefits of their devices without compromising on security, restoring their confidence in installing and using new applications.