ARCHIVES
Research Article
Malware Detection Using Neural Network
Krithika.S1
Aravind Raj.S2
Sandeep.S3
Adhish.M4
Kaviyarasu.M5
1Associate professor, Department of Computer Science and Engineering, paavai Engineering College, Namakkal, TN, India. 2345UG Students, Department of Computer Science and Engineering, paavai Engineering College, Namakkal, TN, India.
Published Online: May-June 2023
Pages: 31-35
Cite this article
↗ 10.59256/ijire.2023040251
References
1. Shabtai, R. Moskovitch, Y. Elovici, C. Glezer, Detection of malware by applying machine learning classifiers on static features: A stateof- the-art survey, Inf. Secur. Tech. Rep. 14 (1) (2009) 16–29.
2. M. Bailey, J. Oberheide, J. Andersen, Z. M. Mao, F. Jahanian, J. Nazario, Automated classification and analysis of internet malware. Recent advances in intrusion detection, Springer, 2007, pp. 178–197.
3. U. Bayer, P. M. Comparetti, C. Hlauschek, C. Kruegel, E. Kirda, Scalable, behavior-based malware clustering, in: NDSS, Vol. 9, 2009, pp. 8–11.
4. K. Rieck, P. Trinius, C. Willems, T. Holz, Automatic analysis of malware behavior using machine learning,Journal of Computer Security 19 (4) (2011) 639–668.https://doi.org/10.3233/JCS-2010-0410
5. Palahan, D. Babi´c, S. Chaudhuri, D. Kifer, Extraction of statistically significant malware behaviors, in: Computer Security Applications Conference, ACM, 2013, pp. 69–78.
6. M. Egele, M. Woo, P. Chapman, D. Brumley, Blanket execution: Dynamic similarity testing for program binaries and components, in: USENIX Security ’14, USENIX Association, San Diego, CA, 2014, pp. 303–317.
7. M. Lindorfer, C. Kolbitsch, P. M. Comparetti, Detecting environmentsensitive malware, in: Recent Advances in Intrusion Detection, Springer, 2011, pp. 338–357.
8. IMPORTANT INFORMATION REGARDING SANDBOXIE VERSIONS. https://www. sandboxie.com/ . [Accessed February 15, 2020].
9. Hassan Ramchoun; Mohammed Amine JanatiIdrissi; Mohammed Amine; Youssef Ghanou. Multilayer Perceptron: Architecture Optimization and Training. International Journal of Interactive Multimedia and Artificial Intelligence. 2016, 4, 26-30. https://doi.org/10.9781/ijimai.2016.415
10. Do Xuan, Cho, Nguyen, HoaDinh, and Dao, Mai Hoang. APT Attack Detection Based on Flow Network Analysis Techniques Using Deep Learning.Journal of Intelligent & Fuzzy Systems. pp. 1 – 17,. 2020. DOI: 10.3233/JIFS-200694
11. Abien Fred Agarap. Deep Learning using Rectified Linear Units (ReLU). arXiv 2018, arXiv:1803.08375.
12. KaiboDuan; SathiyaKeerthi, S.; Wei Chu; ShirishKrishnajShevade; AunNeowPoo. Multi-category Classification by Soft-Max Combination of Binary Classifiers.In proceedings ofthe 4th International Workshop, MCS 2003 Guildford, UK, 11–13 June 2003; pp 125–134.https://doi.org/10.1007/3-540-44938-8_13
13. HOW TO CREATE A MALWARE DETECTION SYSTEM WITH MACHINE LEARNING. https://www.evilsocket.net/2019/05/22/How-to-create-a-Malware-detection-system-with-Machine-Learning/?fbclid=IwAR1vuaOJA3UryaQATPsqKErktLft2Rt zzAB5kDvgOTo4U3dF4J-Op9te
2. M. Bailey, J. Oberheide, J. Andersen, Z. M. Mao, F. Jahanian, J. Nazario, Automated classification and analysis of internet malware. Recent advances in intrusion detection, Springer, 2007, pp. 178–197.
3. U. Bayer, P. M. Comparetti, C. Hlauschek, C. Kruegel, E. Kirda, Scalable, behavior-based malware clustering, in: NDSS, Vol. 9, 2009, pp. 8–11.
4. K. Rieck, P. Trinius, C. Willems, T. Holz, Automatic analysis of malware behavior using machine learning,Journal of Computer Security 19 (4) (2011) 639–668.https://doi.org/10.3233/JCS-2010-0410
5. Palahan, D. Babi´c, S. Chaudhuri, D. Kifer, Extraction of statistically significant malware behaviors, in: Computer Security Applications Conference, ACM, 2013, pp. 69–78.
6. M. Egele, M. Woo, P. Chapman, D. Brumley, Blanket execution: Dynamic similarity testing for program binaries and components, in: USENIX Security ’14, USENIX Association, San Diego, CA, 2014, pp. 303–317.
7. M. Lindorfer, C. Kolbitsch, P. M. Comparetti, Detecting environmentsensitive malware, in: Recent Advances in Intrusion Detection, Springer, 2011, pp. 338–357.
8. IMPORTANT INFORMATION REGARDING SANDBOXIE VERSIONS. https://www. sandboxie.com/ . [Accessed February 15, 2020].
9. Hassan Ramchoun; Mohammed Amine JanatiIdrissi; Mohammed Amine; Youssef Ghanou. Multilayer Perceptron: Architecture Optimization and Training. International Journal of Interactive Multimedia and Artificial Intelligence. 2016, 4, 26-30. https://doi.org/10.9781/ijimai.2016.415
10. Do Xuan, Cho, Nguyen, HoaDinh, and Dao, Mai Hoang. APT Attack Detection Based on Flow Network Analysis Techniques Using Deep Learning.Journal of Intelligent & Fuzzy Systems. pp. 1 – 17,. 2020. DOI: 10.3233/JIFS-200694
11. Abien Fred Agarap. Deep Learning using Rectified Linear Units (ReLU). arXiv 2018, arXiv:1803.08375.
12. KaiboDuan; SathiyaKeerthi, S.; Wei Chu; ShirishKrishnajShevade; AunNeowPoo. Multi-category Classification by Soft-Max Combination of Binary Classifiers.In proceedings ofthe 4th International Workshop, MCS 2003 Guildford, UK, 11–13 June 2003; pp 125–134.https://doi.org/10.1007/3-540-44938-8_13
13. HOW TO CREATE A MALWARE DETECTION SYSTEM WITH MACHINE LEARNING. https://www.evilsocket.net/2019/05/22/How-to-create-a-Malware-detection-system-with-Machine-Learning/?fbclid=IwAR1vuaOJA3UryaQATPsqKErktLft2Rt zzAB5kDvgOTo4U3dF4J-Op9te
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