Current - Issue
Year 2026 · Volume 5 · Issue 3
Review Article
A Comprehensive Survey of Malware Detection and Classification
Santosh Patil1
Vivekanand Koparde2
1 2 Department of CSE, Angadi Institute of Technology and Management, Belagavi, Affiliated to Visvesvaraya Technological University, Belagavi, Karnataka, India.
Published Online: September-December 2026
Pages: 79-83
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503010
References
1. Av-Test: Security Report 2019/2020. [Online]. Available:ttps://www.avtest.org/fileadmin/pdf/security_report/AV-TEST_
Security_Report_2019-2020.pdf
2. SonicWall: 2022 SonicwallCyber Threat Report. Accessed: 2022. [Online]. Available:
ttps://www.sonicwall.com/resources/whitepapers/2022-sonicwall-cyber-threat-report.
3. Stationx: malware staticts report. https://www.stationx.net/malware-statistics/
4. X. Yang, D. Lo, L. Li, X. Xia, T. F. Bissyandé, and J. Klein, ‘‘Characterizing malicious Android apps by mining topic-specific data flow
signatures,’’ Inf. Softw. Technol., vol. 90, pp. 27–39, Oct. 2017, doi: 10.1016/j.infsof.2017.04.007.
5. W. Zhang, H. Wang, H. He, and P. Liu, ‘‘DAMBA: Detecting Android malware by ORGB analysis,’’ IEEE Trans. Rel., vol. 69, no. 1, pp.
55–69, Mar. 2020, doi: 10.1109/TR.2019.2924677.
6. U. Baldangombo, N. Jambaljav, and S.-J. Horng, ‘‘A static malware detection system using data mining methods,’’ 2013, arXiv:1308.2831.
[Online]. Available: https://arxiv.org/abs/1308.2831.
7. A. Malhotra and K. Bajaj, ‘‘A hybrid pattern based text mining approach for malware detection using DBScan,’’ CSI Trans., vol . 4, nos. 2–
4, pp. 141–149, Dec. 2016.
8. N. A. Rosli, W. Yassin, F. M. A, and S. Rahayu, ‘‘Clustering analysis for malware behavior detection using registry data,’’ Int. J. Adv.
Comput. Sci. Appl., vol. 10, no. 12, 2019, doi: 10.14569/ijacsa.2019.0101213.
9. Y. Ding, W. Dai, S. Yan, and Y. Zhang, ‘‘Control flow-based opcode behavior analysis for malware detection,’’ Comput. Secur., vol. 44,
pp. 65–74, Jul. 2014, doi: 10.1016/j.cose.2014.04.003.10. S. Das, Y. Liu, W. Zhang, and M. Chandramohan, ‘‘Semantics– based online malware detection: Towards efficient real–time protection
against malware,’’ IEEE Trans. Inf. Forensics Security, svol. 11, no. 2, pp. 289– 302, Feb. 2016.
11. M. Norouzi, A. Souri, and S. M. Zamini, ‘‘A data mining classification approach for behavioral malware detection,’’ J. Comput . Netw.
Commun., vol. 2016, p. 1, Mar. 2016.
12. D. Kirat, L. Nataraj, G. Vigna, and B. S. Manjunath, ‘‘SigMal: A static signal processing based malware triage,’’ in Proc. 29 th Annu.
Comput. Secur. Appl. Conf., Dec. 2013, pp. 89–98, doi: 10.1145/2523649.2523682.
13. R. Islam, R. Tian, L. M. Batten, and S. Versteeg, ‘‘Classification of malware based on integrated static and dynamic features,’’ J. Netw.
Comput. Appl., vol. 36, no. 2, pp. 646–656, Mar. 2013.
14. S. Naval, V. Laxmi, M. Rajarajan, M. S. Gaur, and M. Conti, ‘‘Employing program semantics for malware detection,’’ IEEE Trans. Inf.
Forensics Security, vol. 10, no. 12, pp. 2591–2604, Dec. 2015.
15. Rahul Raj R, Naveen S, Subhikshan R, and Tarun S,” Malware Analysis Using Sandbox” http://dx.doi.org/10.2139/ssrn.4708146.
16. S. Liu, P. Feng, S. Wang, K. Sun, and J. Cao, ‘‘Enhancing malware analysis sandboxes with emulated user behavior,’’ Comput. Secur., vol.
115, Apr. 2022, Art. no. 102613, doi: 10.1016/j.cose.2022.102613.
17. V. Vouvoutsis, F. Casino, and C. Patsakis, ‘‘On the effectiveness of binary emulation in malware classification,’’ J. Inf. Secur. Appl., vol.
68, Aug. 2022, Art. no. 103258, doi: 10.1016/j.jisa.2022.103258.
18. E. Gandotra, D. Bansal, and S. Sofat, ‘‘Malware analysis and classification: A survey,’’ J. Inf. Secur., vol. 5, no. 2, pp. 56–64, 2014,
doi:10.4236/jis.2014.52006.
19. G. Gopinath and S. C. Sethuraman, ‘‘A comprehensive survey on deep learning based malware detection techniques,’’ Comput. Sci . Rev.,
vol. 47,Feb. 2023, Art. no. 100529, doi: 10.1016/j.cosrev.2022.100529.
20. Y.-T. Hou, Y. Chang, T. Chen, C.-S. Laih, and C.-M. Chen, ‘‘Malicious Web content detection by machine learning,’’ Expert Syst. Appl.,
vol. 37, no. 1, pp. 55–60, Jan. 2010, doi: 10.1016/j.eswa.2009.05.023
21. D. Vasan, M. Alazab, S. Wassan, B. Safaei, and Q. Zheng, ‘‘Imagebased malware classification using ensemble of CNN architectures
(IMCEC),’’ Comput. Secur., vol. 92, May 2020, Art. no. 101748, doi: 10.1016/j.cose.2020.101748.
22. T. Kim, B. Kang, M. Rho, S. Sezer, and E. G. Im, ‘‘A multimodal deep learning method for Android malware detection using various
features,’’ IEEE Trans. Inf. Forensics Security, vol. 14, no. 3, pp. 773–788, Mar. 2019, doi: 10.1109/TIFS.2018.2866319.
23. Z. Yuan, Y. Lu, and Y. Xue, ‘‘Droiddetector: Android malware characterization and detection using deep learning,’’ Tsinghua Sci. Technol.,
vol. 21, no. 1, pp. 114–123, Feb. 2016, doi: 10.1109/TST.2016.7399288.
24. X. Huang, L. Ma, W. Yang, and Y. Zhong, ‘‘A method for windows malware detection based on deep learning,’’ J. Signal Process. Syst.,
vol. 93, nos. 2–3, pp. 265–273, Mar. 2021, doi: 10.1007/s11265-020- 01588-1.
25. Z. Cui, L. Du, P. Wang, X. Cai, and W. Zhang, ‘‘Malicious code detection based on CNNs and multi-objective algorithm,’’ J. Parallel
Distrib. Comput., vol. 129, pp. 50–58, Jul. 2019, doi: 10.1016/j.jpdc.2019.03.010.
26. X. Xu, S. Jiang, J. Zhao, and X. Wang, ‘‘DCEL: Classifier fusion model for Android malware detection,’’ J. Syst. Eng. Electron., vol. 35,
no. 1, pp. 163–177, Feb. 2024, doi: 10.23919/jsee.2024.000018.
27. Santosh Patil, Prakash K Sonwalkar and Sagargouda Patil ,” SE-MADnet: A Balanced Deep Learning Framework for Robust Malware
Classification with Imbalanced Code Samples”, 2nd International Conference on Computational and Cognitive Systems Science (ICCCSS
2025)- Volume 1, pages 96-104 ISBN: 978-989-758-824-2.
Security_Report_2019-2020.pdf
2. SonicWall: 2022 SonicwallCyber Threat Report. Accessed: 2022. [Online]. Available:
ttps://www.sonicwall.com/resources/whitepapers/2022-sonicwall-cyber-threat-report.
3. Stationx: malware staticts report. https://www.stationx.net/malware-statistics/
4. X. Yang, D. Lo, L. Li, X. Xia, T. F. Bissyandé, and J. Klein, ‘‘Characterizing malicious Android apps by mining topic-specific data flow
signatures,’’ Inf. Softw. Technol., vol. 90, pp. 27–39, Oct. 2017, doi: 10.1016/j.infsof.2017.04.007.
5. W. Zhang, H. Wang, H. He, and P. Liu, ‘‘DAMBA: Detecting Android malware by ORGB analysis,’’ IEEE Trans. Rel., vol. 69, no. 1, pp.
55–69, Mar. 2020, doi: 10.1109/TR.2019.2924677.
6. U. Baldangombo, N. Jambaljav, and S.-J. Horng, ‘‘A static malware detection system using data mining methods,’’ 2013, arXiv:1308.2831.
[Online]. Available: https://arxiv.org/abs/1308.2831.
7. A. Malhotra and K. Bajaj, ‘‘A hybrid pattern based text mining approach for malware detection using DBScan,’’ CSI Trans., vol . 4, nos. 2–
4, pp. 141–149, Dec. 2016.
8. N. A. Rosli, W. Yassin, F. M. A, and S. Rahayu, ‘‘Clustering analysis for malware behavior detection using registry data,’’ Int. J. Adv.
Comput. Sci. Appl., vol. 10, no. 12, 2019, doi: 10.14569/ijacsa.2019.0101213.
9. Y. Ding, W. Dai, S. Yan, and Y. Zhang, ‘‘Control flow-based opcode behavior analysis for malware detection,’’ Comput. Secur., vol. 44,
pp. 65–74, Jul. 2014, doi: 10.1016/j.cose.2014.04.003.10. S. Das, Y. Liu, W. Zhang, and M. Chandramohan, ‘‘Semantics– based online malware detection: Towards efficient real–time protection
against malware,’’ IEEE Trans. Inf. Forensics Security, svol. 11, no. 2, pp. 289– 302, Feb. 2016.
11. M. Norouzi, A. Souri, and S. M. Zamini, ‘‘A data mining classification approach for behavioral malware detection,’’ J. Comput . Netw.
Commun., vol. 2016, p. 1, Mar. 2016.
12. D. Kirat, L. Nataraj, G. Vigna, and B. S. Manjunath, ‘‘SigMal: A static signal processing based malware triage,’’ in Proc. 29 th Annu.
Comput. Secur. Appl. Conf., Dec. 2013, pp. 89–98, doi: 10.1145/2523649.2523682.
13. R. Islam, R. Tian, L. M. Batten, and S. Versteeg, ‘‘Classification of malware based on integrated static and dynamic features,’’ J. Netw.
Comput. Appl., vol. 36, no. 2, pp. 646–656, Mar. 2013.
14. S. Naval, V. Laxmi, M. Rajarajan, M. S. Gaur, and M. Conti, ‘‘Employing program semantics for malware detection,’’ IEEE Trans. Inf.
Forensics Security, vol. 10, no. 12, pp. 2591–2604, Dec. 2015.
15. Rahul Raj R, Naveen S, Subhikshan R, and Tarun S,” Malware Analysis Using Sandbox” http://dx.doi.org/10.2139/ssrn.4708146.
16. S. Liu, P. Feng, S. Wang, K. Sun, and J. Cao, ‘‘Enhancing malware analysis sandboxes with emulated user behavior,’’ Comput. Secur., vol.
115, Apr. 2022, Art. no. 102613, doi: 10.1016/j.cose.2022.102613.
17. V. Vouvoutsis, F. Casino, and C. Patsakis, ‘‘On the effectiveness of binary emulation in malware classification,’’ J. Inf. Secur. Appl., vol.
68, Aug. 2022, Art. no. 103258, doi: 10.1016/j.jisa.2022.103258.
18. E. Gandotra, D. Bansal, and S. Sofat, ‘‘Malware analysis and classification: A survey,’’ J. Inf. Secur., vol. 5, no. 2, pp. 56–64, 2014,
doi:10.4236/jis.2014.52006.
19. G. Gopinath and S. C. Sethuraman, ‘‘A comprehensive survey on deep learning based malware detection techniques,’’ Comput. Sci . Rev.,
vol. 47,Feb. 2023, Art. no. 100529, doi: 10.1016/j.cosrev.2022.100529.
20. Y.-T. Hou, Y. Chang, T. Chen, C.-S. Laih, and C.-M. Chen, ‘‘Malicious Web content detection by machine learning,’’ Expert Syst. Appl.,
vol. 37, no. 1, pp. 55–60, Jan. 2010, doi: 10.1016/j.eswa.2009.05.023
21. D. Vasan, M. Alazab, S. Wassan, B. Safaei, and Q. Zheng, ‘‘Imagebased malware classification using ensemble of CNN architectures
(IMCEC),’’ Comput. Secur., vol. 92, May 2020, Art. no. 101748, doi: 10.1016/j.cose.2020.101748.
22. T. Kim, B. Kang, M. Rho, S. Sezer, and E. G. Im, ‘‘A multimodal deep learning method for Android malware detection using various
features,’’ IEEE Trans. Inf. Forensics Security, vol. 14, no. 3, pp. 773–788, Mar. 2019, doi: 10.1109/TIFS.2018.2866319.
23. Z. Yuan, Y. Lu, and Y. Xue, ‘‘Droiddetector: Android malware characterization and detection using deep learning,’’ Tsinghua Sci. Technol.,
vol. 21, no. 1, pp. 114–123, Feb. 2016, doi: 10.1109/TST.2016.7399288.
24. X. Huang, L. Ma, W. Yang, and Y. Zhong, ‘‘A method for windows malware detection based on deep learning,’’ J. Signal Process. Syst.,
vol. 93, nos. 2–3, pp. 265–273, Mar. 2021, doi: 10.1007/s11265-020- 01588-1.
25. Z. Cui, L. Du, P. Wang, X. Cai, and W. Zhang, ‘‘Malicious code detection based on CNNs and multi-objective algorithm,’’ J. Parallel
Distrib. Comput., vol. 129, pp. 50–58, Jul. 2019, doi: 10.1016/j.jpdc.2019.03.010.
26. X. Xu, S. Jiang, J. Zhao, and X. Wang, ‘‘DCEL: Classifier fusion model for Android malware detection,’’ J. Syst. Eng. Electron., vol. 35,
no. 1, pp. 163–177, Feb. 2024, doi: 10.23919/jsee.2024.000018.
27. Santosh Patil, Prakash K Sonwalkar and Sagargouda Patil ,” SE-MADnet: A Balanced Deep Learning Framework for Robust Malware
Classification with Imbalanced Code Samples”, 2nd International Conference on Computational and Cognitive Systems Science (ICCCSS
2025)- Volume 1, pages 96-104 ISBN: 978-989-758-824-2.
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