arXiv:1807.08265
공식 코드 · Papers with Code
Deep learning at the shallow end: Malware classification for non-domain experts
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Orthrus: A Bimodal Learning Architecture for Malware Classification
A Hierarchical Convolutional Neural Network for Malware Classification
Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion
Malware families discovery via Open-Set Recognition on Android manifest permissions
토론
이 논문과 연결된 포럼 글이 아직 없습니다.