Deep learning at the shallow end: Malware classification for non-domain experts

Abstract

Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification. Moreover, these signatures are often limited to local, contiguous sequences within the data whilst ignoring their context in relation to each other and throughout the malware file as a whole. We present a Deep Learning based malware classification approach that requires no expert domain knowledge and is based on a purely data driven approach for complex pattern and feature identification.

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Cited by in corpus (12)

• Deep Learning for Anomaly Detection: A Survey
• SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation
• Fusing Feature Engineering and Deep Learning: A Case Study for Malware Classification
• DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining
• Methodology for the Automated Metadata-Based Classification of Incriminating Digital Forensic Artefacts
• Scratch that! An Evolution-based Adversarial Attack against Neural Networks
• Automated Artefact Relevancy Determination from Artefact Metadata and Associated Timeline Events
• Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning
• Feature-level Malware Obfuscation in Deep Learning
• Malware Classification Using Deep Boosted Learning
• A Review of Computer Vision Methods in Network Security
• Bytes to Threats: Byte-Sequence Transformer For Malware Detection