Malware Detection Based on API Call Sequence Analysis: A Gated Recurrent Unit–Generative Adversarial Network Model Approach
Malware remains a major threat to computer systems, with a vast number of new samples being identified and documented regularly. Windows systems are particularly vulnerable to malicious programs like viruses, worms, and trojans. Dynamic analysis, which involves observing malware behavior during exec...
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| Published in: | Future internet Vol. 16; no. 10; p. 369 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.10.2024
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| ISSN: | 1999-5903, 1999-5903 |
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| Abstract | Malware remains a major threat to computer systems, with a vast number of new samples being identified and documented regularly. Windows systems are particularly vulnerable to malicious programs like viruses, worms, and trojans. Dynamic analysis, which involves observing malware behavior during execution in a controlled environment, has emerged as a powerful technique for detection. This approach often focuses on analyzing Application Programming Interface (API) calls, which represent the interactions between the malware and the operating system. Recent advances in deep learning have shown promise in improving malware detection accuracy using API call sequence data. However, the potential of Generative Adversarial Networks (GANs) for this purpose remains largely unexplored. This paper proposes a novel hybrid deep learning model combining Gated Recurrent Units (GRUs) and GANs to enhance malware detection based on API call sequences from Windows portable executable files. We evaluate our GRU–GAN model against other approaches like Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) on multiple datasets. Results demonstrated the superior performance of our hybrid model, achieving 98.9% accuracy on the most challenging dataset. It outperformed existing models in resource utilization, with faster training and testing times and low memory usage. |
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| AbstractList | Malware remains a major threat to computer systems, with a vast number of new samples being identified and documented regularly. Windows systems are particularly vulnerable to malicious programs like viruses, worms, and trojans. Dynamic analysis, which involves observing malware behavior during execution in a controlled environment, has emerged as a powerful technique for detection. This approach often focuses on analyzing Application Programming Interface (API) calls, which represent the interactions between the malware and the operating system. Recent advances in deep learning have shown promise in improving malware detection accuracy using API call sequence data. However, the potential of Generative Adversarial Networks (GANs) for this purpose remains largely unexplored. This paper proposes a novel hybrid deep learning model combining Gated Recurrent Units (GRUs) and GANs to enhance malware detection based on API call sequences from Windows portable executable files. We evaluate our GRU–GAN model against other approaches like Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) on multiple datasets. Results demonstrated the superior performance of our hybrid model, achieving 98.9% accuracy on the most challenging dataset. It outperformed existing models in resource utilization, with faster training and testing times and low memory usage. |
| Author | Ashawa, Moses Osamor, Jude Hosseinzadeh, Salaheddin Qureshi, Ayyaz Adejoh, John Owoh, Nsikak |
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| Cites_doi | 10.7717/peerj-cs.285 10.1109/ACCESS.2024.3358454 10.1109/ITNEC.2019.8728992 10.1109/JCSSE58229.2023.10202128 10.1007/s11416-021-00383-1 10.1371/journal.pone.0298809 10.1016/j.cose.2021.102247 10.1016/j.gltp.2021.01.004 10.3390/app12199403 10.1155/2015/659101 10.1016/j.jnca.2019.102526 10.1016/j.jnca.2023.103704 10.3390/app13074097 10.1007/978-3-031-25891-6_4 10.1109/ACCESS.2019.2963724 10.1007/978-3-031-37963-5_53 10.1145/3559540 10.1016/j.cose.2020.101773 10.1016/j.cose.2021.102221 10.1145/3073559 10.1016/j.cosrev.2022.100529 10.7717/peerj-cs.1319 10.18517/ijaseit.8.4-2.6827 10.1109/UBMK55850.2022.9919580 10.3390/app13095439 10.1016/j.jnca.2018.09.013 10.1145/3571070 10.1016/j.cose.2023.103582 10.1007/s11277-020-07166-9 |
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| SubjectTerms | Accuracy Algorithms API call sequence Application programming interface Computer worms Cybersecurity Datasets Deep learning dynamic malware analysis Efficiency Gated Recurrent Unit Generative Adversarial Network Generative adversarial networks Machine learning Malware malware detection Performance evaluation Resource utilization Sequences Software Windows (computer programs) |
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