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Ağ trafiği analizi ile derin öğrenme tabanlı Android kötücül yazılım tespiti

Year 2022, Volume: 37 Issue: 4, 1823 - 1838, 28.02.2022
https://doi.org/10.17341/gazimmfd.937374

Abstract

Günümüzde giderek yaygınlaşan mobil cihazlar, gelişen multimedya iletişimi ve uygulamaları sayesinde bilgisayarların sağladığı çoğu özelliği kullanıcılarına sunmaktadır. Günümüzde, gelişmiş multimedya uygulamaları geleneksel cep telefonları tarafından desteklenmektedir. Mobil cihazlar artan işlevsellikleri ile birlikte zenginleştirilmiş internet deneyimi, finansal işlemler, sosyal medya platformlarına erişim, paylaşım, müzik ve video gibi birçok amaç için kullanılmaktadır. Bankacılık ve alışveriş gibi hassas kişisel veri aktarımlarının yapıldığı işlemleri, mobil cihazlar üzerinden gerçekleştirme, mobil cihazları saldırganların hedefi haline getirmektedir. Bu çalışmada, mobil uygulamaların ağ üzerindeki etkileşimlerine dayalı olarak derin öğrenme tabanlı bir kötü amaçlı yazılım tespit sistemi geliştirilmiştir. Geliştirilen LSTM tabanlı derin öğrenme modeli, doğruluk, kesinlik, duyarlılık ve F-1 puanı metrikleri kullanılarak NB, RF, SVM, MLP ve CNN ile karşılaştırmalı olarak analiz edilmiştir. Deneysel sonuçlar, geliştirilen LSTM tabanlı derin öğrenme modelinin kötücül yazılım tespitinde %95 doğruluk oranı ile karşılaştırılan modellere göre daha başarılı olduğunu göstermiştir.

References

  • 1. Wang S., Chen Z., Yan Q., Yang B., Peng L., Jia Z., A mobile malware detection method using behavior features in network traffic, Journal of Network and Computer Applications, 133, 15-25, 2019.
  • 2. Gezici B., Tarhan A., Chouseinoglou O., Mobil uygulamaların evriminde karmaşıklık, boyut ve iç kalite gelişimi: Keşifsel bir çalışma, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34(3), 1483-1500, 2018.
  • 3. Andrade E.D.O., Viterbo J., Vasconcelos C.N., Guérin J., Bernardini F.C., A model based on lstm neural networks to identify five different types of malware, Procedia Computer Science, 159, 182-191, 2019.
  • 4. Grønli T.M., Hansen J., Ghinea G., Younas M., Mobile application platform heterogeneity: Android vs Windows Phone vs iOS vs Firefox OS, In 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, Victoria, Kanada, 635-641, 13-16 Mayıs 2014.
  • 5. Feng P., Ma J., Sun C., Xu X., Ma Y., A novel dynamic Android malware detection system with ensemble learning. IEEE Access, 6, 30996-31011, 2018.
  • 6. Sarwar M., Soomro T.R., Impact of smartphone’s on society, European journal of scientific research, 98(2), 216-226, 2013.
  • 7. Siau K., Lim E. P., Shen Z., Mobile commerce: Promises, challenges and research agenda, Journal of Database Management (JDM), 12(3), 4-13, 2001.
  • 8. Yesilyurt M., Yalman Y., Security threats on mobile devices and their effects: estimations for the future, International Journal of Security and Its Applications, 10(2), 13-26, 2016.
  • 9. Sheikh A.A., Ganai P.T., Malik N.A., Dar K.A., Smartphone: Android Vs iOS. The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), 1(4), 141-148, 2013.
  • 10. Baabdullah A.M., Alalwan A.A., Rana N.P., Al Shraah A., Kizgin H., Patil P.P. (2019, June). Mobile App Stores from the User’s Perspective, In International Working Conference on Transfer and Diffusion of IT, Accra, Gana, 21-30, 21–22 Haziran, 2019.
  • 11. Kabakus A.T., Dogru I.A., An in-depth analysis of Android malware using hybrid techniques, Digital Investigation, 24, 25-33, 2018.
  • 12. Wu D.J., Mao C.H., Wei T.E., Lee H.M., Wu K.P., Droidmat: Android malware detection through manifest and api calls tracing, In 2012 Seventh Asia Joint Conference on Information Security, Tokyo, Japonya, 62-69, 9-10 Ağustos, 2012.
  • 13. Kapratwar A., Di Troia F., Stamp M., Static and dynamic analysis of android malware, In ICISSP, Porto, Portekiz, 653-662, 19-21 Şubat, 2017.
  • 14. Arora A., Garg S., Peddoju S.K., Malware detection using network traffic analysis in android based mobile devices, In 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies, Oxford, İngiltere, 66-71, 10-12 Eylül, 2014.
  • 15. Malik J., Kaushal R., CREDROID: Android malware detection by network traffic analysis, In Proceedings of the 1st acm workshop on privacy-aware mobile computing, New York, ABD, 28-36, Temmuz, 2016.
  • 16. Wang S., Chen Z., Zhang L., Yan Q., Yang B., Peng L., Jia Z., Trafficav: An effective and explainable detection of mobile malware behavior using network traffic, In 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), Beijing, Çin, 1-6, 20-21 Haziran, 2016.
  • 17. Milosevic N., Dehghantanha A., Choo K.K.R., Machine learning aided Android malware classification, Computers & Electrical Engineering, 61, 266-274, 2017.
  • 18. Karbab E. B., Debbabi M., Derhab A., Mouheb D., MalDozer: Automatic framework for android malware detection using deep learning, Digital Investigation, 24, 48-59, 2018.
  • 19. Mehtab A., Shahid W.B., Yaqoob T., Amjad M.F., Abbas H., Afzal H., Saqib M.N., AdDroid: rule-based machine learning framework for android malware analysis, Mobile Networks and Applications, 25(1), 180-192, 2020.
  • 20. Jang-Jaccard J., Nepal S., A survey of emerging threats in cybersecurity, Journal of Computer and System Sciences, 80(5), 973-993, 2014.
  • 21. Or-Meir O., Nissim N., Elovici Y., Rokach L., Dynamic malware analysis in the modern era—A state of the art survey, ACM Computing Surveys (CSUR), 52(5), 1-48, 2019.
  • 22. Rabbani M., Wang Y. L., Khoshkangini R., Jelodar H., Zhao R., Hu P., A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing, Journal of Network and Computer Applications, 151, 102507, 2020.
  • 23. Seo S.H., Gupta A., Sallam A.M., Bertino E., Yim K., Detecting mobile malware threats to homeland security through static analysis, Journal of Network and Computer Applications, 38, 43-53, 2014.
  • 24. Utku A, Doğru İ.A., Android kötücül yazılımlar için izin tabanlı tespit sistemi, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 32(4), 1015-1024, 2017.
  • 25. Li Q., Li X., Android malware detection based on static analysis of characteristic tree, In 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Xi'an, Çin, 84-91, 17-19 Eylül, 2015.
  • 26. Dhaya R., Poongodi M., Detecting software vulnerabilities in android using static analysis, In 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramanathapuram, Hindistan, 915-918, 8-10 Mayıs 2014.
  • 27. Grégio A.R., Fernandes Filho D.S., Afonso V.M., Santos R.D., Jino M., de Geus P.L., Behavioral analysis of malicious code through network traffic and system call monitoring, In Evolutionary and Bio-Inspired Computation: Theory and Applications, 2011.
  • 28. Kaggle. Android ağ trafiği veriseti. https://www.kaggle.com/xwolf12/network-traffic-android-malware. Yayın tarihi 2019. Erişim tarihi Şubat 1, 2021.
Year 2022, Volume: 37 Issue: 4, 1823 - 1838, 28.02.2022
https://doi.org/10.17341/gazimmfd.937374

Abstract

References

  • 1. Wang S., Chen Z., Yan Q., Yang B., Peng L., Jia Z., A mobile malware detection method using behavior features in network traffic, Journal of Network and Computer Applications, 133, 15-25, 2019.
  • 2. Gezici B., Tarhan A., Chouseinoglou O., Mobil uygulamaların evriminde karmaşıklık, boyut ve iç kalite gelişimi: Keşifsel bir çalışma, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 34(3), 1483-1500, 2018.
  • 3. Andrade E.D.O., Viterbo J., Vasconcelos C.N., Guérin J., Bernardini F.C., A model based on lstm neural networks to identify five different types of malware, Procedia Computer Science, 159, 182-191, 2019.
  • 4. Grønli T.M., Hansen J., Ghinea G., Younas M., Mobile application platform heterogeneity: Android vs Windows Phone vs iOS vs Firefox OS, In 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, Victoria, Kanada, 635-641, 13-16 Mayıs 2014.
  • 5. Feng P., Ma J., Sun C., Xu X., Ma Y., A novel dynamic Android malware detection system with ensemble learning. IEEE Access, 6, 30996-31011, 2018.
  • 6. Sarwar M., Soomro T.R., Impact of smartphone’s on society, European journal of scientific research, 98(2), 216-226, 2013.
  • 7. Siau K., Lim E. P., Shen Z., Mobile commerce: Promises, challenges and research agenda, Journal of Database Management (JDM), 12(3), 4-13, 2001.
  • 8. Yesilyurt M., Yalman Y., Security threats on mobile devices and their effects: estimations for the future, International Journal of Security and Its Applications, 10(2), 13-26, 2016.
  • 9. Sheikh A.A., Ganai P.T., Malik N.A., Dar K.A., Smartphone: Android Vs iOS. The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), 1(4), 141-148, 2013.
  • 10. Baabdullah A.M., Alalwan A.A., Rana N.P., Al Shraah A., Kizgin H., Patil P.P. (2019, June). Mobile App Stores from the User’s Perspective, In International Working Conference on Transfer and Diffusion of IT, Accra, Gana, 21-30, 21–22 Haziran, 2019.
  • 11. Kabakus A.T., Dogru I.A., An in-depth analysis of Android malware using hybrid techniques, Digital Investigation, 24, 25-33, 2018.
  • 12. Wu D.J., Mao C.H., Wei T.E., Lee H.M., Wu K.P., Droidmat: Android malware detection through manifest and api calls tracing, In 2012 Seventh Asia Joint Conference on Information Security, Tokyo, Japonya, 62-69, 9-10 Ağustos, 2012.
  • 13. Kapratwar A., Di Troia F., Stamp M., Static and dynamic analysis of android malware, In ICISSP, Porto, Portekiz, 653-662, 19-21 Şubat, 2017.
  • 14. Arora A., Garg S., Peddoju S.K., Malware detection using network traffic analysis in android based mobile devices, In 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies, Oxford, İngiltere, 66-71, 10-12 Eylül, 2014.
  • 15. Malik J., Kaushal R., CREDROID: Android malware detection by network traffic analysis, In Proceedings of the 1st acm workshop on privacy-aware mobile computing, New York, ABD, 28-36, Temmuz, 2016.
  • 16. Wang S., Chen Z., Zhang L., Yan Q., Yang B., Peng L., Jia Z., Trafficav: An effective and explainable detection of mobile malware behavior using network traffic, In 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), Beijing, Çin, 1-6, 20-21 Haziran, 2016.
  • 17. Milosevic N., Dehghantanha A., Choo K.K.R., Machine learning aided Android malware classification, Computers & Electrical Engineering, 61, 266-274, 2017.
  • 18. Karbab E. B., Debbabi M., Derhab A., Mouheb D., MalDozer: Automatic framework for android malware detection using deep learning, Digital Investigation, 24, 48-59, 2018.
  • 19. Mehtab A., Shahid W.B., Yaqoob T., Amjad M.F., Abbas H., Afzal H., Saqib M.N., AdDroid: rule-based machine learning framework for android malware analysis, Mobile Networks and Applications, 25(1), 180-192, 2020.
  • 20. Jang-Jaccard J., Nepal S., A survey of emerging threats in cybersecurity, Journal of Computer and System Sciences, 80(5), 973-993, 2014.
  • 21. Or-Meir O., Nissim N., Elovici Y., Rokach L., Dynamic malware analysis in the modern era—A state of the art survey, ACM Computing Surveys (CSUR), 52(5), 1-48, 2019.
  • 22. Rabbani M., Wang Y. L., Khoshkangini R., Jelodar H., Zhao R., Hu P., A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing, Journal of Network and Computer Applications, 151, 102507, 2020.
  • 23. Seo S.H., Gupta A., Sallam A.M., Bertino E., Yim K., Detecting mobile malware threats to homeland security through static analysis, Journal of Network and Computer Applications, 38, 43-53, 2014.
  • 24. Utku A, Doğru İ.A., Android kötücül yazılımlar için izin tabanlı tespit sistemi, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 32(4), 1015-1024, 2017.
  • 25. Li Q., Li X., Android malware detection based on static analysis of characteristic tree, In 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Xi'an, Çin, 84-91, 17-19 Eylül, 2015.
  • 26. Dhaya R., Poongodi M., Detecting software vulnerabilities in android using static analysis, In 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, Ramanathapuram, Hindistan, 915-918, 8-10 Mayıs 2014.
  • 27. Grégio A.R., Fernandes Filho D.S., Afonso V.M., Santos R.D., Jino M., de Geus P.L., Behavioral analysis of malicious code through network traffic and system call monitoring, In Evolutionary and Bio-Inspired Computation: Theory and Applications, 2011.
  • 28. Kaggle. Android ağ trafiği veriseti. https://www.kaggle.com/xwolf12/network-traffic-android-malware. Yayın tarihi 2019. Erişim tarihi Şubat 1, 2021.
There are 28 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Anıl Utku 0000-0002-7240-8713

Publication Date February 28, 2022
Submission Date May 14, 2021
Acceptance Date November 6, 2021
Published in Issue Year 2022 Volume: 37 Issue: 4

Cite

APA Utku, A. (2022). Ağ trafiği analizi ile derin öğrenme tabanlı Android kötücül yazılım tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 37(4), 1823-1838. https://doi.org/10.17341/gazimmfd.937374
AMA Utku A. Ağ trafiği analizi ile derin öğrenme tabanlı Android kötücül yazılım tespiti. GUMMFD. February 2022;37(4):1823-1838. doi:10.17341/gazimmfd.937374
Chicago Utku, Anıl. “Ağ trafiği Analizi Ile Derin öğrenme Tabanlı Android kötücül yazılım Tespiti”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37, no. 4 (February 2022): 1823-38. https://doi.org/10.17341/gazimmfd.937374.
EndNote Utku A (February 1, 2022) Ağ trafiği analizi ile derin öğrenme tabanlı Android kötücül yazılım tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37 4 1823–1838.
IEEE A. Utku, “Ağ trafiği analizi ile derin öğrenme tabanlı Android kötücül yazılım tespiti”, GUMMFD, vol. 37, no. 4, pp. 1823–1838, 2022, doi: 10.17341/gazimmfd.937374.
ISNAD Utku, Anıl. “Ağ trafiği Analizi Ile Derin öğrenme Tabanlı Android kötücül yazılım Tespiti”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 37/4 (February 2022), 1823-1838. https://doi.org/10.17341/gazimmfd.937374.
JAMA Utku A. Ağ trafiği analizi ile derin öğrenme tabanlı Android kötücül yazılım tespiti. GUMMFD. 2022;37:1823–1838.
MLA Utku, Anıl. “Ağ trafiği Analizi Ile Derin öğrenme Tabanlı Android kötücül yazılım Tespiti”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 37, no. 4, 2022, pp. 1823-38, doi:10.17341/gazimmfd.937374.
Vancouver Utku A. Ağ trafiği analizi ile derin öğrenme tabanlı Android kötücül yazılım tespiti. GUMMFD. 2022;37(4):1823-38.