| Title | DeepMal: A CNN-LSTM Model for Malware Detection Based on Dynamic Semantic Behaviours |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Zhang, J. |
| Conference Name | 2020 International Conference on Computer Information and Big Data Applications (CIBDA) |
| Date Published | apr |
| Keywords | CNN-LSTM model, component-CNN, compositionality, convolution, convolutional neural nets, Cyber Dependencies, cyber-criminals, Data models, deep learning framework, DeepMal, dynamic semantic behaviours, evil intentions, feature extraction, high-level abstractions, human factors, invasive software, learning (artificial intelligence), locally spatial correlations, LSTM, machine learning, malicious programs, Malware, malware classification task, malware detection, Metrics, natural language processing, Neurons, NLP techniques, pattern classification, pubcrawl, recurrent neural nets, Resiliency, Scalability, sequential longterm dependency, Training |
| Abstract | Malware refers to any software accessing or being installed in a system without the authorisation of administrators. Various malware has been widely used for cyber-criminals to accomplish their evil intentions and goals. To combat the increasing amount and reduce the threat of malicious programs, a novel deep learning framework, which uses NLP techniques for reference, combines CNN and LSTM neurones to capture the locally spatial correlations and learn from sequential longterm dependency is proposed. Hence, high-level abstractions and representations are automatically extracted for the malware classification task. The classification accuracy improves from 0.81 (best one by Random Forest) to approximately 1.0. |
| DOI | 10.1109/CIBDA50819.2020.00077 |
| Citation Key | zhang_deepmal_2020 |
DeepMal: A CNN-LSTM Model for Malware Detection Based on Dynamic Semantic Behaviours