DeepAI AI Chat
Log In Sign Up

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

by   Quan Le, et al.

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.


page 1

page 2

page 3

page 4

page 6

page 7

page 8

page 9


Learning the PE Header, Malware Detection with Minimal Domain Knowledge

Many efforts have been made to use various forms of domain knowledge in ...

An investigation of a deep learning based malware detection system

We investigate a Deep Learning based system for malware detection. In th...

Fusing Feature Engineering and Deep Learning: A Case Study for Malware Classification

Machine learning has become an appealing signature-less approach to dete...

Bio-inspired data mining: Treating malware signatures as biosequences

The application of machine learning to bioinformatics problems is well e...

KiloGrams: Very Large N-Grams for Malware Classification

N-grams have been a common tool for information retrieval and machine le...

Network-based Analysis and Classification of Malware using Behavioral Artifacts Ordering

Using runtime execution artifacts to identify malware and its associated...

A Comparison of Graph Neural Networks for Malware Classification

Managing the threat posed by malware requires accurate detection and cla...