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Self-Attentive Models for Real-Time Malware Classification
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AI
DOI：10.1109/ACCESS.2022.3202952.png)
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Abstract
En 中文
Malware classification is a critical task in cybersecurity, as it offers insights into the threats that malware poses to the victim device and helps in the design of countermeasures. For real-time malware classification, due to the high network throughputs of modern networks, there is a challenge of achieving high classification accuracy while maintaining low inference latency. We first introduce two self-attention transformer-based classifiers, SeqConvAttn and ImgConvAttn, to replace the currently predominant Convolutional Neural Network (CNN) classifiers. We then devise a file-size-aware two-stage framework to combine the two proposed models, thereby controlling the tradeoff between accuracy and latency for real-time classification. To assess our proposed designs, we conduct experiments on three malware datasets: the Microsoft Malware Classification Challenge (BIG 2015) and two selected subsets from the BODMAS PE malware dataset, BODMAS-11 and BODMAS-49. We show that our transformer-based designs can achieve better classification accuracy than traditional CNN-based designs. Furthermore, we show that the proposed two-stage framework reduces the average model inference latency while maintaining superior accuracy, thereby fulfilling the requirements of real-time classification.
Keywords:
Malware
Transformers
Residual neural networks
Convolutional neural networks
Markov processes
Real-time systems
Feature extraction
Computer security
Malware classification
self-attention networks
multi-stage classification
cybersecurity