Year 2024,
Volume: 12 Issue: 4, 337 - 348
Melike Başer
,
Ebu Yusuf Güven
,
Muhammed Ali Aydın
References
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Honeypot system,” Webology, vol. 19, no. 1, pp. 5497–5514,
2022.
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//www.comparitech.com/antivirus/malware-statistics-facts/
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on malware analysis and mitigation techniques,” Computer
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techniques,” in 2021 International Conference on Information
Technology (ICIT). IEEE, 2021, pp. 371–376.
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Conference on Trends in Electronics and Informatics (ICOEI).
IEEE, 2021, pp. 1481–1486.
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A hardware-enhanced antivirus engine to accelerate real-time,
signature-based malware detection,” Expert Systems with Applications, vol. 201, p. 117083, 2022.
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S. Sobhan, M. A. Khan, M. Whitman, A. Cuzzocrea, D. Lo,
A. Rahman et al., “Malware detection and prevention using
artificial intelligence techniques,” in 2021 IEEE International
Conference on Big Data (Big Data). IEEE, 2021, pp. 5369–
5377.
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Lima, S. L. d. P. Silva, A. de Andrade, and A. M. da Silva,
“Artificial intelligence-based antivirus in order to detect malware preventively,” Progress in Artificial Intelligence, vol. 10,
no. 1, pp. 1–22, 2021.
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anomaly detection of malware using knn,” in 2022 2nd International Conference on Innovative Practices in Technology
and Management (ICIPTM), vol. 2. IEEE, 2022, pp. 774–779.
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intrusion detection for system security,” in 2021 International
Conference on Communication information and Computing
Technology (ICCICT). IEEE, 2021, pp. 1–5.
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malware detection,” Tsehay Admassu Assegie.” An Optimized
KNN Model for Signature-Based Malware Detection”. International Journal of Computer Engineering In Research Trends
(IJCERT), ISSN, pp. 2349–7084, 2021.
- [23] M. Zyout, R. Shatnawi, and H. Najadat, “Malware classification approaches utilizing binary and text encoding of
permissions,” International Journal of Information Security,
pp. 1–26, 2023.
- [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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and analysis pe malware using modern honey network (mhn),”
in 2020 8th International Conference on Cyber and IT Service
Management (CITSM). IEEE, 2020, pp. 1–5.
- [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.
- [27] C. Moore, “Detecting ransomware with honeypot techniques,”
in 2016 Cybersecurity and Cyberforensics Conference (CCC).
IEEE, 2016, pp. 77–81.
- [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.
- [29] J. Aycock, Computer viruses and malware. Springer Science
& Business Media, 2006, vol. 22.
- [30] R. Ball, “Computer viruses, computer worms, and the selfreplication of programs,” in Viruses in all Dimensions: How
an Information Code Controls Viruses, Software and Microorganisms. Springer, 2023, pp. 73–85.
- [31] M. N. Alenezi, H. Alabdulrazzaq, A. A. Alshaher, and M. M.
Alkharang, “Evolution of malware threats and techniques: a
review,” International Journal of Communication Networks
and Information Security, vol. 12, no. 3, pp. 326–337, 2020.
- [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
- [33] J. Aycock, Spyware and adware. Springer Science & Business
Media, 2010, vol. 50.
- [34] I. Kuzminykh and M. Yevdokymenko, “Analysis of security
of rootkit detection methods,” in 2019 IEEE International
Conference on Advanced Trends in Information Theory (ATIT).
IEEE, 2019, pp. 196–199.
- [35] N. A. Mims, “Chapter 14 - the botnet problem,” in Computer
and Information Security Handbook (Fourth Edition), J. R.
Vacca, Ed. Morgan Kaufmann, 2025, pp. 261–272.
- [36] M. Swanson and B. Guttman, “NIST SP 800-12 Rev. 1:
An Introduction to Information Security,” National Institute
of Standards and Technology (NIST), Tech. Rep. 800-12
Rev. 1, 2017, accessed: 2024-10-28. [Online]. Available:
https://csrc.nist.gov/pubs/sp/800/12/r1/final
- [37] A. Warikoo, “Perspective chapter: Ransomware,” in MalwareDetection and Defense. IntechOpen, 2023.
- [38] E. Salimi and N. Arastouie, “Backdoor detection system
using artificial neural network and genetic algorithm,” in 2011
International Conference on Computational and Information
Sciences, 2011, pp. 817–820.
- [39] H. W. Kim, “A study on countermeasures by detecting trojantype downloader/dropper malicious code,” International Journal of Advanced Culture Technology, vol. 9, no. 4, pp. 288–
294, 2021.
- [40] A. Damodaran, F. D. Troia, C. A. Visaggio, T. H. Austin,
and M. Stamp, “A comparison of static, dynamic, and hybrid
analysis for malware detection,” Journal of Computer Virology
and Hacking Techniques, vol. 13, no. 1, pp. 1–12, 2017.
- [41] O. A. Aslan and R. Samet, “A comprehensive review on ¨
malware detection approaches,” IEEE Access, vol. 8, pp. 6249–
6271, 2020.
- [42] Z. Bazrafshan, H. Hashemi, S. M. H. Fard, and A. Hamzeh, “A
survey on heuristic malware detection techniques,” in The 5th
Conference on Information and Knowledge Technology, 2013,
pp. 113–120.
- [43] Y. K. B. M. Yunus and S. B. Ngah, “Review of
hybrid analysis technique for malware detection,” IOP
Conference Series: Materials Science and Engineering, vol.
769, no. 1, p. 012075, feb 2020. [Online]. Available:
https://doi.org/10.1088/1757-899x/769/1/012075
- [44] R. Sihwail, K. Omar, and K. A. Z. Ariffin, “An effective
memory analysis for malware detection and classification,”
Comput., Mater. Continua, vol. 67, no. 2, pp. 2301–2320,
2021.
- [45] K. Monnappa, Learning Malware Analysis: Explore the concepts, tools, and techniques to analyze and investigate Windows malware. Packt Publishing Ltd, 2018.
- [46] O. Or-Meir, N. Nissim, Y. Elovici, and L. Rokach, “Dynamic
malware analysis in the modern era—a state of the art survey,”
ACM Computing Surveys (CSUR), vol. 52, no. 5, pp. 1–48,
2019.
- [47] Y. K. B. M. Yunus and S. B. Ngah, “Review of hybrid analysis
technique for malware detection,” in IOP Conference Series:
Materials Science and Engineering. IOP Publishing, 2020,
p. 012075.
- [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.
Analysis of Malicious Files Gathering via Honeypot Trap System and Benchmark of Anti-Virus Software
Year 2024,
Volume: 12 Issue: 4, 337 - 348
Melike Başer
,
Ebu Yusuf Güven
,
Muhammed Ali Aydın
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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Journal of information security, vol. 20, pp. 371–386, 2021.
- [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.
- [10] D. Laka, “Malware: Types, analysis and classification,” Analysis and Classification (January 14, 2022), 2022.
- [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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and analysis tools,” International Journal of Network Security
& Its Applications (IJNSA) Vol, vol. 12, 2020.
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tools and techniques mitigating computer and mobile malware
attacks,” Computers & Electrical Engineering, vol. 92, p.
107143, 2021.
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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.
- [17] M. Botacin, M. Z. Alves, D. Oliveira, and A. Gregio, “Heaven: ´
A hardware-enhanced antivirus engine to accelerate real-time,
signature-based malware detection,” Expert Systems with Applications, vol. 201, p. 117083, 2022.
- [18] M. J. H. Faruk, H. Shahriar, M. Valero, F. L. Barsha,
S. Sobhan, M. A. Khan, M. Whitman, A. Cuzzocrea, D. Lo,
A. Rahman et al., “Malware detection and prevention using
artificial intelligence techniques,” in 2021 IEEE International
Conference on Big Data (Big Data). IEEE, 2021, pp. 5369–
5377.
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Lima, S. L. d. P. Silva, A. de Andrade, and A. M. da Silva,
“Artificial intelligence-based antivirus in order to detect malware preventively,” Progress in Artificial Intelligence, vol. 10,
no. 1, pp. 1–22, 2021.
- [20] S. Rani, K. Tripathi, Y. Arora, and A. Kumar, “Analysis of
anomaly detection of malware using knn,” in 2022 2nd International Conference on Innovative Practices in Technology
and Management (ICIPTM), vol. 2. IEEE, 2022, pp. 774–779.
- [21] A. Katkar, S. Shukla, D. Shaikh, and P. Dange, “Malware
intrusion detection for system security,” in 2021 International
Conference on Communication information and Computing
Technology (ICCICT). IEEE, 2021, pp. 1–5.
- [22] T. A. Assegie, “An optimized knn model for signature-based
malware detection,” Tsehay Admassu Assegie.” An Optimized
KNN Model for Signature-Based Malware Detection”. International Journal of Computer Engineering In Research Trends
(IJCERT), ISSN, pp. 2349–7084, 2021.
- [23] M. Zyout, R. Shatnawi, and H. Najadat, “Malware classification approaches utilizing binary and text encoding of
permissions,” International Journal of Information Security,
pp. 1–26, 2023.
- [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.
- [25] I. M. M. Matin and B. Rahardjo, “A framework for collecting
and analysis pe malware using modern honey network (mhn),”
in 2020 8th International Conference on Cyber and IT Service
Management (CITSM). IEEE, 2020, pp. 1–5.
- [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.
- [27] C. Moore, “Detecting ransomware with honeypot techniques,”
in 2016 Cybersecurity and Cyberforensics Conference (CCC).
IEEE, 2016, pp. 77–81.
- [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.
- [29] J. Aycock, Computer viruses and malware. Springer Science
& Business Media, 2006, vol. 22.
- [30] R. Ball, “Computer viruses, computer worms, and the selfreplication of programs,” in Viruses in all Dimensions: How
an Information Code Controls Viruses, Software and Microorganisms. Springer, 2023, pp. 73–85.
- [31] M. N. Alenezi, H. Alabdulrazzaq, A. A. Alshaher, and M. M.
Alkharang, “Evolution of malware threats and techniques: a
review,” International Journal of Communication Networks
and Information Security, vol. 12, no. 3, pp. 326–337, 2020.
- [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
- [33] J. Aycock, Spyware and adware. Springer Science & Business
Media, 2010, vol. 50.
- [34] I. Kuzminykh and M. Yevdokymenko, “Analysis of security
of rootkit detection methods,” in 2019 IEEE International
Conference on Advanced Trends in Information Theory (ATIT).
IEEE, 2019, pp. 196–199.
- [35] N. A. Mims, “Chapter 14 - the botnet problem,” in Computer
and Information Security Handbook (Fourth Edition), J. R.
Vacca, Ed. Morgan Kaufmann, 2025, pp. 261–272.
- [36] M. Swanson and B. Guttman, “NIST SP 800-12 Rev. 1:
An Introduction to Information Security,” National Institute
of Standards and Technology (NIST), Tech. Rep. 800-12
Rev. 1, 2017, accessed: 2024-10-28. [Online]. Available:
https://csrc.nist.gov/pubs/sp/800/12/r1/final
- [37] A. Warikoo, “Perspective chapter: Ransomware,” in MalwareDetection and Defense. IntechOpen, 2023.
- [38] E. Salimi and N. Arastouie, “Backdoor detection system
using artificial neural network and genetic algorithm,” in 2011
International Conference on Computational and Information
Sciences, 2011, pp. 817–820.
- [39] H. W. Kim, “A study on countermeasures by detecting trojantype downloader/dropper malicious code,” International Journal of Advanced Culture Technology, vol. 9, no. 4, pp. 288–
294, 2021.
- [40] A. Damodaran, F. D. Troia, C. A. Visaggio, T. H. Austin,
and M. Stamp, “A comparison of static, dynamic, and hybrid
analysis for malware detection,” Journal of Computer Virology
and Hacking Techniques, vol. 13, no. 1, pp. 1–12, 2017.
- [41] O. A. Aslan and R. Samet, “A comprehensive review on ¨
malware detection approaches,” IEEE Access, vol. 8, pp. 6249–
6271, 2020.
- [42] Z. Bazrafshan, H. Hashemi, S. M. H. Fard, and A. Hamzeh, “A
survey on heuristic malware detection techniques,” in The 5th
Conference on Information and Knowledge Technology, 2013,
pp. 113–120.
- [43] Y. K. B. M. Yunus and S. B. Ngah, “Review of
hybrid analysis technique for malware detection,” IOP
Conference Series: Materials Science and Engineering, vol.
769, no. 1, p. 012075, feb 2020. [Online]. Available:
https://doi.org/10.1088/1757-899x/769/1/012075
- [44] R. Sihwail, K. Omar, and K. A. Z. Ariffin, “An effective
memory analysis for malware detection and classification,”
Comput., Mater. Continua, vol. 67, no. 2, pp. 2301–2320,
2021.
- [45] K. Monnappa, Learning Malware Analysis: Explore the concepts, tools, and techniques to analyze and investigate Windows malware. Packt Publishing Ltd, 2018.
- [46] O. Or-Meir, N. Nissim, Y. Elovici, and L. Rokach, “Dynamic
malware analysis in the modern era—a state of the art survey,”
ACM Computing Surveys (CSUR), vol. 52, no. 5, pp. 1–48,
2019.
- [47] Y. K. B. M. Yunus and S. B. Ngah, “Review of hybrid analysis
technique for malware detection,” in IOP Conference Series:
Materials Science and Engineering. IOP Publishing, 2020,
p. 012075.
- [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.