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Articles

Malware detection based on visualization of recombined API instruction sequence

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Pages 2630-2651 | Received 29 Jul 2022, Accepted 18 Oct 2022, Published online: 04 Nov 2022

References

  • Cai, S., Han, D., Yin, X., Li, D., & Chang, C. C. (2022). A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning. Connection Science, 34(1), 551–577. https://doi.org/10.1080/09540091.2021.2024509
  • Chaganti, R., Ravi, V., & Pham, T. D. (2022). Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification. Journal of Information Security and Applications, 69(1), 103306:1–103306:19. https://doi.org/10.1016/j.jisa.2022.103306
  • Chai, Y., Qiu, J., Su, S., Zhu, C., Yin, L., & Tian, Z. (2020). LGMal: A joint framework based on local and global features for malware detection. 2020 International Wireless Communications and Mobile Computing (IWCMC) IEEE, 463–468.
  • Chen, J., Guo, S., Ma, X., Li, H., Guo, J., Chen, M., & Pan, Z. (2020). Slam: a malware detection method based on sliding local attention mechanism. Security and Communication Networks, 2020(1), 6724513:1–6724513:11. https://doi.org/10.1155/2020/6724513
  • Cui, Z., Xue, F., Cai, X., Cao, Y., Wang, G. G., & Chen, J. (2018). Detection of malicious code variants based on deep learning. IEEE Transactions on Industrial Informatics, 14(7), 3187–3196. https://doi.org/10.1109/TII.2018.2822680
  • D'Angelo, G., Palmieri, F., Robustelli, A., & Castiglione, A. (2021). Effective classification of android malware families through dynamic features and neural networks. Connection Science, 33(3), 786–801. https://doi.org/10.1080/09540091.2021.1889977
  • Gaurav, A., Gupta, B. B., & Panigrahi, P. K. (2022). A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system. Enterprise Information Systems, 1–25. https://doi.org/10.1080/17517575.2021.2023764
  • Han, W., Xue, J., Wang, Y., Liu, Z., & Kong, Z. (2019). MalInsight: A systematic profiling based malware detection framework. Journal of Network and Computer Applications, 125(1), 236–250. https://doi.org/10.1016/j.jnca.2018.10.022
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
  • Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2261–2269.
  • Jian, Y., Kuang, H., Ren, C., Ma, Z., & Wang, H. (2021). A novel framework for image-based malware detection with a deep neural network. Computers & Security, 109(1), 102400:1–102400:24. https://doi.org/10.1016/j.cose.2021.102400
  • Jian, Y., Kuang, H., Ren, C., Ma, Z, & Wang, H. (2021). A novel framework for image-based malware detection with a deep neural network. Computers and Security, 109, 102400.https://doi.org/10.1016/j.cose.2021.102400
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
  • Lin, G., Wen, S., Han, Q. L., Zhang, J., & Xiang, Y. (2020). Software vulnerability detection using deep neural networks: a survey. Proceedings of the IEEE, 108(10), 1825–1848. https://doi.org/10.1109/JPROC.2020.2993293
  • Ma, X., Guo, S., Bai, W., Chen, J., Xia, S., & Pan, Z. (2019). An API semantics-aware malware detection method based on deep learning. Security and Communication Networks, 2019(1), 1–9. https://doi.org/10.1155/2019/1315047
  • Miao, Y., Chen, C., Pan, L., Han, Q. L., Zhang, J., & Xiang, Y. (2021). Machine learning–based cyber attacks targeting on controlled information. ACM Computing Surveys, 54(7), 1–36. https://doi.org/10.1145/3465171
  • O’Shaughnessy, S., & Sheridan, S. (2022). Image-based malware classification hybrid framework based on space-filling curves. Computers & Security, 116(1), 102660:1–102660:14. https://doi.org/10.1016/j.cose.2022.102660
  • Pinhero, A., Anupama, M. L., Vinod, P., Visaggio, C. A., Aneesh, N., Abhijith, S., & AnanthaKrishnan, S. (2021). Malware detection employed by visualization and deep neural network. Computers & Security, 105(1), 102247:1–102247:30. https://doi.org/10.1016/j.cose.2021.102247
  • Pinhero, A., Anupama, M. L., Vinod, P., Visaggio, C. A., Aneesh, N., Abhijith, S, & AnanthaKrishnan, S. (2021). Malware detection employed by visualization and deep neural network. Computers and Security, 105, 102247.https://doi.org/10.1016/j.cose.2021.102247
  • Potha, N., Kouliaridis, V., & Kambourakis, G. (2021). An extrinsic random-based ensemble approach for android malware detection. Connection Science, 33(4), 1077–1093. https://doi.org/10.1080/0954-0091.2020.1853056
  • Qiu, J., Zhang, J., Luo, W., Pan, L., Nepal, S., & Xiang, Y. (2020). A survey of android malware detection with deep neural models. ACM Computing Surveys, 53(6), 1–36. https://doi.org/10.1145/3417978
  • Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 4510–4520.
  • Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. the 3rd International Conference on Learning Representations(ICLR), 1–14.
  • Tekerek, A., & Yapici, M. (2022). A novel malware classification and augmentation model based on convolutional neural network. Computers & Security, 112(1), 102515:1–102515:17. https://doi.org/10.1016/j.cose.2021.102515
  • Vasan, D., Alazab, M., Wassan, S., Naeem, H., Safaei, B., & Zheng, Q. (2020). IMCFN: Image-based malware classification using fine-tuned convolutional neural network architecture. Computer Networks, 171(1), 107138:1–107138:19. https://doi.org/10.1016/j.comnet.2020.107138
  • Wang, J., Zhang, C., Qi, X., & Rong, Y. (2021). A survey of Intelligent malware detection on windows platform. Journal of Computer Research and Development, 58(5), 977–994. https://doi.org/10.7544/issn1000-1239.2021.20200964
  • Xiao, M., Guo, C., Shen, G., Cui, Y., & Jiang, C. (2021). Image-based malware classification using section distribution information. Computers & Security, 110(1), 102420:1–102420:14. https://doi.org/10.1016/j.cose.2021.102420
  • Xu, A., Chen, L., Kuang, X., Lv, H., Yang, H., Jiang, Y., & Li, B. (2020). A hybrid deep learning model for malicious behavior detection. 2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC), and IEEE Intl Conference on Intelligent Data and Security (IDS) IEEE, 55–59.
  • Yang, H., & Feng, Y. (2021). A Pythagorean fuzzy Petri net based security assessment model for civil aviation airport security inspection information system. International Journal of Intelligent Systems, 36(5), 2122–2143. https://doi.org/10.1002/int.22373
  • Yang, H., Wang, Z., Zhang, L., & Cheng, X. (2022a). IoT botnet detection with feature reconstruction and interval optimization. International Journal of Intelligent Systems, https://doi.org/10.1002/int.23074
  • Yang, H., Zeng, R., Xu, G., & Zhang, L. (2021). A network security situation assessment method based on adversarial deep learning. Applied Soft Computing, 102(1), 107096:1–107096:9. https://doi.org/10.1016/j.asoc.2021.107096
  • Yang, H., Zhang, Z., Xie, L., & Zhang, L. (2022b). Network security situation assessment with network attack behavior classification. International Journal of Intelligent Systems, 37(3), 6909–6927. https://doi.org/10.1002/int.22867
  • Zhang, J., Pan, L., Han, Q. L., Chen, C., Wen, S., & Xiang, Y. (2021). Deep learning based attack detection for cyber-physical system cybersecurity: A survey. IEEE/CAA Journal of Automatica Sinica, 9(3), 377–391. https://doi.org/10.1109/JAS.2021.1004261
  • Zhang, Z., Li, Y., Dong, H., Gao, H., Jin, Y., & Wang, W. (2020). Spectral-based directed graph network for malware detection. IEEE Transactions on Network Science and Engineering, 8(2), 957–970. https://doi.org/10.1109/TNSE.2020.3024557
  • Zhu, X., Huang, J., Wang, B., & Qi, C. (2021). Malware homology determination using visualized images and feature fusion. PeerJ Computer Science, 7(4), 494–513. doi:10.7717/peerj-cs.494