Problem Statement
The increasing complexity and sophistication of malware pose a significant threat to
digital security. Traditional antivirus solutions often struggle to keep up with evolving
malware patterns, leading to a pressing need for more advanced detection
mechanisms.
Abstract
The Deep Learning-based Malware Detection project addresses the limitations of
traditional antivirus approaches by leveraging deep learning techniques. Through neural
network models, the system aims to detect and classify malware based on intricate
patterns and behaviors, providing a more robust defense against evolving cyber threats.
Outcome
The outcome of this project is an advanced malware detection system capable of
identifying and categorizing previously unseen malware variants. By utilizing deep
learning, the system adapts and learns from new threats, improving its accuracy over
time. This results in a more proactive and effective defense against malware, enhancing
overall cybersecurity for individuals and organizations.
Reference
As the predominant mobile operating system world-wide, Android suffers from various types of malware, which could cause severe security and privacy issues. To cope with them, many research efforts have been made to develop effective mal ware detectors. However, malware authors tend to evade detection by launching adversarial example attacks, in which Android applications could be mutated and thus causing confusion to malware detectors. In this paper, we propose a deep learning based approach to identify malware for the Android system in the presence of adversarial example attacks. To validate the proposed approach, we have conducted experimental study on real-world datasets.
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https://ieeexplore.ieee.org/document/9701615/references#references