Research Article
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Year 2024, Volume: 12 Issue: 4, 337 - 348
https://doi.org/10.17694/bajece.1506554

Abstract

References

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  • [32] “CNSSI 4009: Committee on national security systems (cnss) glossary,” Committee on National Security Systems (CNSS), 2015, accessed: 2024-10-28. [Online]. Available: https://rmf.org/wp-content/uploads/2017/10/CNSSI-4009.pdf
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  • [48] R. Sihwail, K. Omar, and K. Z. Ariffin, “A survey on malware analysis techniques: Static, dynamic, hybrid and memory analysis,” Int. J. Adv. Sci. Eng. Inf. Technol, vol. 8, no. 4-2, pp. 1662–1671, 2018.
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  • [51] A. Salem, S. Banescu, and A. Pretschner, “Maat: Automatically analyzing virustotal for accurate labeling and effective malware detection,” ACM Transactions on Privacy and Security (TOPS), vol. 24, no. 4, pp. 1–35, 2021.

Analysis of Malicious Files Gathering via Honeypot Trap System and Benchmark of Anti-Virus Software

Year 2024, Volume: 12 Issue: 4, 337 - 348
https://doi.org/10.17694/bajece.1506554

Abstract

In the age of widespread digital integration, the rise in cyber threats is evident. Cyber attackers use malicious software (malware) to compromise data and exploit system resources, employing tactics such as remote control or ransom through data encryption. Despite the common use of antivirus software with signature-based detection, this study reveals its limitations. Using a honeypot trap system on Google Cloud, suspicious files uploaded by attackers were analyzed. Results from evaluating these files with 64 antivirus programs show that relying solely on signature-based methods is insufficient. Only three programs had success rates exceeding 90\%, while the majority had success rates predominantly below 70\%. This underscores the need for diverse detection techniques alongside signature-based methods to enhance cybersecurity. The repository containing collected malicious files and the Python script is available on Github, serving as a valuable research resource for further exploration.

References

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  • [2] M. Amal and P. Venkadesh, “Review of cyber attack detection: Honeypot system,” Webology, vol. 19, no. 1, pp. 5497–5514, 2022.
  • [3] S. COOK, “Malware statistics in 2022: Frequency, impact, cost & more,” Feb 2022. [Online]. Available: https: //www.comparitech.com/antivirus/malware-statistics-facts/
  • [4] S. S. Chakkaravarthy, D. Sangeetha, and V. Vaidehi, “A survey on malware analysis and mitigation techniques,” Computer Science Review, vol. 32, pp. 1–23, 2019.
  • [5] N. Pachhala, S. Jothilakshmi, and B. P. Battula, “A comprehensive survey on identification of malware types and malware classification using machine learning techniques,” in 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2021, pp. 1207–1214.
  • [6] C. Rohith and G. Kaur, “A comprehensive study on malware detection and prevention techniques used by anti-virus,” in 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 2021, pp. 429–434.
  • [7] K. Oosthoek and C. Doerr, “Cyber threat intelligence: A product without a process?” International Journal of Intelligence and CounterIntelligence, vol. 34, no. 2, pp. 300–315, 2021.
  • [8] D. Aygor and E. Aktan, “The limitations of signature-based ¨ and dynamic analysis methods in detecting malwares: A case study,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 37, no. 1, pp. 305–315, 2022.
  • [9] U. Inayat, M. F. Zia, F. Ali, S. M. Ali, H. M. A. Khan, and W. Noor, “Comprehensive review of malware detection techniques,” in 2021 International Conference on Innovative Computing (ICIC). IEEE, 2021, pp. 1–6.
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  • [11] E. Tekiner, A. Acar, A. S. Uluagac, E. Kirda, and A. A. Selcuk, “Sok: cryptojacking malware,” in 2021 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE, 2021, pp. 120–139.
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  • [14] S. Varlioglu, N. Elsayed, Z. ElSayed, and M. Ozer, “The dangerous combo: Fileless malware and cryptojacking,” SoutheastCon 2022, pp. 125–132, 2022.
  • [15] T. Alsmadi and N. Alqudah, “A survey on malware detection techniques,” in 2021 International Conference on Information Technology (ICIT). IEEE, 2021, pp. 371–376.
  • [16] A. Chavan, K. Kerakalamatti, and S. Srivastva, “Implementation of portable antivirus system using signature-based detection and heuristic analysis,” in 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2021, pp. 1481–1486.
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  • [24] V. Sethia and A. Jeyasekar, “Malware capturing and analysis using dionaea honeypot,” in 2019 International Carnahan Conference on Security Technology (ICCST). IEEE, 2019, pp. 1–4.
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  • [26] A. Kyriakou and N. Sklavos, “Container-based honeypot deployment for the analysis of malicious activity,” in 2018 Global Information Infrastructure and Networking Symposium (GIIS). IEEE, 2018, pp. 1–4.
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  • [28] B. Wang, Y. Dou, Y. Sang, Y. Zhang, and J. Huang, “Iotcmal: Towards a hybrid iot honeypot for capturing and analyzing malware,” in ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020, pp. 1–7.
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  • [48] R. Sihwail, K. Omar, and K. Z. Ariffin, “A survey on malware analysis techniques: Static, dynamic, hybrid and memory analysis,” Int. J. Adv. Sci. Eng. Inf. Technol, vol. 8, no. 4-2, pp. 1662–1671, 2018.
  • [49] M. Bas¸er, E. Y. Guven, and M. A. Aydın, “Ssh and telnet pro- ¨ tocols attack analysis using honeypot technique:* analysis of ssh and telnet honeypot,” in 2021 6th International Conference on Computer Science and Engineering (UBMK). IEEE, 2021, pp. 806–811.
  • [50] R. Masri and M. Aldwairi, “Automated malicious advertisement detection using virustotal, urlvoid, and trendmicro,” in 2017 8th International Conference on Information and Communication Systems (ICICS). IEEE, 2017, pp. 336–341.
  • [51] A. Salem, S. Banescu, and A. Pretschner, “Maat: Automatically analyzing virustotal for accurate labeling and effective malware detection,” ACM Transactions on Privacy and Security (TOPS), vol. 24, no. 4, pp. 1–35, 2021.
There are 51 citations in total.

Details

Primary Language English
Subjects Software Testing, Verification and Validation
Journal Section Araştırma Articlessi
Authors

Melike Başer 0000-0003-0175-5045

Ebu Yusuf Güven 0000-0002-7587-3127

Muhammed Ali Aydın 0000-0002-1846-6090

Early Pub Date January 13, 2025
Publication Date
Submission Date July 1, 2024
Acceptance Date November 22, 2024
Published in Issue Year 2024 Volume: 12 Issue: 4

Cite

APA Başer, M., Güven, E. Y., & Aydın, M. A. (2025). Analysis of Malicious Files Gathering via Honeypot Trap System and Benchmark of Anti-Virus Software. Balkan Journal of Electrical and Computer Engineering, 12(4), 337-348. https://doi.org/10.17694/bajece.1506554

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