Research Article
Year 2017,
Volume: 32 Issue: 4, 1015 - 1024, 08.12.2017
Abstract
Günümüzde
mobil cihazların kullanım alanlarında ve işlevselliklerinde büyük bir artış
yaşanmaktadır. Mobil cihazlar kullanıcıların kişisel bilgilerini sakladıkları
platformlar haline gelmişlerdir. Bu gibi özelliklerinden dolayı mobil cihazlar
saldırganların hedefi konumuna gelmiştir. Bu çalışmada, Naive Bayes ve KNN
algoritmaları kullanılarak izin tabanlı bir kötücül yazılım tespit sistemi
geliştirilmiştir. Geliştirilen sistemin test sonuçları her bir algoritma için
analiz edilerek karşılaştırılmıştır. Analiz sonuçları Naive Bayes
sınıflandırıcısının %97,29 oranında kötücül yazılım tespitinde başarılı
olduğunu, Knn sınıflandırıcısının ise %97,74 oranında kötücül yazılım
tespitinde başarılı olduğunu göstermiştir.
References
- 1. He D., Chan S., Guizani M., Mobile application security: malware threats and defenses, Wireless Communications, 22 (1), 138-144, 2015.
- 2. Tekerek A., Gemci C., Bay Ö.F., Design and implementation of a web-based intrusion prevention system: a new hybrid model, Journal of the Faculty of Engineering and Architecture of Gazi University, 31 (3), 645-653, 2016.
- 3. Wang X., Yang Y., Zeng Y., Tang C., Shi J., Xu K., A Novel Hybrid Mobile Malware Detection System Integrating Anomaly Detection With Misuse Detection, In Proceedings of the 6th International Workshop on Mobile Cloud Computing and Services, 15-22, 2015.
- 4. Kabakuş A.T., Doğru İ.A., Çetin A., Android kötücül yazılım tespit ve koruma sistemleri, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 31 (1), 9-16, 2015.
- 5. Talha K.A., Alper D.I., Aydin C., APK Auditor: Permission-based Android malware detection system, Digital Investigation, 13, 1-14, 2015.
- 6. Burguera I., Zurutuza U., Nadjm-Tehranı S., Crowdroid: behavior-based malware detection system for android, Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices, 15-26, 2011.
- 7. Shabtai A., Kanonov U., Elovici Y., Glezer C., Weiss Y., Andromaly: A behavioral malware detection framework for android devices, Journal of Intelligent Information Systems, 38 (1), 161-190, 2012.
- 8. 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, Information Security (Asia JCIS), 2012 Seventh Asia Joint Conference, 62-69, 2012.
- 9. Arp D., Spreitzenbarth M., Hübner M., Gascon H., Rieck K., Drebin: Effective and explainable detection of android malware in your pocket, Proceedings of the Annual Symposium on Network and Distributed System Security (NDSS), 2014.
- 10. La Polla M., Martinelli F., Sgandurra D., A survey on security for mobile devices, Communications Surveys & Tutorials IEEE, 15 (1), 446-471, 2013.
- 11. Dua L., Bansal D., Taxonomy: Mobile Malware Threats and Detection Techniques, Computer Science & Information Technology (CS & IT), 213-221, 2014.
- 12. Wang X., Yang Y., Zeng,Y., Accurate mobile malware detection and classification in the cloud, SpringerPlus, 2015.
- 13. Shen Y.C., Chien R., Hung S.H., Toward Efficient Dynamic Analysis and Testing for Android Malware, IT CoNvergence PRActice (INPRA), 2 (3), 14-23, 2014.
- 14. Tchakount´e F., Permission-based Malware Detection Mechanisms on Android: Analysis and Perspectives, Journal of Computer Science and Sofware Application, 1 (2), 63-77, 2014.
- 15. Aung, Z., Zaw W., Permission-Based Android Malware Detection, International Journal Of Scientific & Technology Research, 2 (3), 228-234, 2013.
- 16. Ren J., Lee S.D., Chen X., Kao B., Cheng R., Cheung D., Naive bayes classification of uncertain data, Ninth IEEE International Conference on Data Mining, 944-949, 2009.
- 17. Dragomir E.G., Air quality index prediction using K-nearest neighbor technique, Bulletin of PG University of Ploiesti, Series Mathematics, Informatics, Physics, LXII, 1, 2010.
Year 2017,
Volume: 32 Issue: 4, 1015 - 1024, 08.12.2017
Abstract
References
- 1. He D., Chan S., Guizani M., Mobile application security: malware threats and defenses, Wireless Communications, 22 (1), 138-144, 2015.
- 2. Tekerek A., Gemci C., Bay Ö.F., Design and implementation of a web-based intrusion prevention system: a new hybrid model, Journal of the Faculty of Engineering and Architecture of Gazi University, 31 (3), 645-653, 2016.
- 3. Wang X., Yang Y., Zeng Y., Tang C., Shi J., Xu K., A Novel Hybrid Mobile Malware Detection System Integrating Anomaly Detection With Misuse Detection, In Proceedings of the 6th International Workshop on Mobile Cloud Computing and Services, 15-22, 2015.
- 4. Kabakuş A.T., Doğru İ.A., Çetin A., Android kötücül yazılım tespit ve koruma sistemleri, Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 31 (1), 9-16, 2015.
- 5. Talha K.A., Alper D.I., Aydin C., APK Auditor: Permission-based Android malware detection system, Digital Investigation, 13, 1-14, 2015.
- 6. Burguera I., Zurutuza U., Nadjm-Tehranı S., Crowdroid: behavior-based malware detection system for android, Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices, 15-26, 2011.
- 7. Shabtai A., Kanonov U., Elovici Y., Glezer C., Weiss Y., Andromaly: A behavioral malware detection framework for android devices, Journal of Intelligent Information Systems, 38 (1), 161-190, 2012.
- 8. 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, Information Security (Asia JCIS), 2012 Seventh Asia Joint Conference, 62-69, 2012.
- 9. Arp D., Spreitzenbarth M., Hübner M., Gascon H., Rieck K., Drebin: Effective and explainable detection of android malware in your pocket, Proceedings of the Annual Symposium on Network and Distributed System Security (NDSS), 2014.
- 10. La Polla M., Martinelli F., Sgandurra D., A survey on security for mobile devices, Communications Surveys & Tutorials IEEE, 15 (1), 446-471, 2013.
- 11. Dua L., Bansal D., Taxonomy: Mobile Malware Threats and Detection Techniques, Computer Science & Information Technology (CS & IT), 213-221, 2014.
- 12. Wang X., Yang Y., Zeng,Y., Accurate mobile malware detection and classification in the cloud, SpringerPlus, 2015.
- 13. Shen Y.C., Chien R., Hung S.H., Toward Efficient Dynamic Analysis and Testing for Android Malware, IT CoNvergence PRActice (INPRA), 2 (3), 14-23, 2014.
- 14. Tchakount´e F., Permission-based Malware Detection Mechanisms on Android: Analysis and Perspectives, Journal of Computer Science and Sofware Application, 1 (2), 63-77, 2014.
- 15. Aung, Z., Zaw W., Permission-Based Android Malware Detection, International Journal Of Scientific & Technology Research, 2 (3), 228-234, 2013.
- 16. Ren J., Lee S.D., Chen X., Kao B., Cheng R., Cheung D., Naive bayes classification of uncertain data, Ninth IEEE International Conference on Data Mining, 944-949, 2009.
- 17. Dragomir E.G., Air quality index prediction using K-nearest neighbor technique, Bulletin of PG University of Ploiesti, Series Mathematics, Informatics, Physics, LXII, 1, 2010.
There are 17 citations in total.
Details
| Journal Section | Makaleler |
|---|---|
| Authors | |
| Publication Date | December 8, 2017 |
| Submission Date | March 1, 2016 |
| Acceptance Date | August 4, 2017 |
| Published in Issue | Year 2017 Volume: 32 Issue: 4 |