THE METHODS OF ARTIFICIAL INTELLIGENCE FOR MALICIOUS APPLICATIONS DETECTION IN ANDROID OS
DOI:
https://doi.org/10.47839/ijc.15.3.851Keywords:
Cyber Security, Artificial Intelligence, Android, Artificial Immune System, Artificial Neural Networks, Malware Detection.Abstract
This paper presents and discusses a method for Android’s applications classification with the purpose of malware detection. Based on the application of an Artificial Immune System and Artificial Neural Networks we propose the “antivirus” system especially for Android system that can detect and block undesirable and malicious applications. This system can be characterized by self-adaption and self-evolution and can detect even unknown and previously unseen malicious applications. The proposed system is the part of our team’s big project named “Intelligent Cyber Defense System” that includes malware detection and classification module, intrusions detection and classification module, cloud security module and personal cryptography module. This paper contains the extended research that was presented during the IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2015) [1].References
S. Bezobrazov, A. Sachenko, M. Komar, Vladimir Rubanau, “Artificial immune system for Android OS,” in Proceedings of the 8th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2015), Warsaw, Poland, 24-26 September 2015, pp. 403-407.
Trend Micro: http://www.trendmicro.eu/index.html, 2015.
Kaspersky Lab: http://www.kaspersky.com, 2015.
S. Bezobrazov, and V. Golovko, “Artificial immune systems of the neural network for the malicious code detection,” in Proceedings of the 6th International Conference on Neural Networks and Artificial Intelligence (ICNNAI’2010), Brest, Belarus, 2010.
M. Komar, V. Golovko, A. Sachenko and S. Bezobrazov, “Intelligent system for detection of networking intrusion,” in Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing System (IDAACS’2011), Prague, Czech Republic, 15-17 September 2011, pp. 374-377.
V. Golovko, S. Bezobrazov, V. Melianchuk and M. Komar, “Evolution of immune detectors in intelligent security system for malware detection,” in Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing System (IDAACS’2011), Prague, Czech Republic, 15-17 September 2011, pp. 722-726.
M. Zhao et al, “A smartphone malware detection framework based on artificial immunology,” Journal of Networks, Vol. 8, No. 2, pp. 469-476, February 2013.
U. Vural, and H. Venter, “Detecting mobile spam botnets using artificial immune systems,” Advances in Digital Forensics VII, pp. 183-192, August 1987 [Digests of the 9th Annual Conference on Magnetics, Japan, 1982, p. 301].
A. Felt, E. Ha, S. Egelman, F. Haney, E. Chin, and D. Wagner, “Android permissions: user attention, comprehension, and behaviour,” in Proceedings of the Eight Symposium on Usable Privacy and Security (SOUPS’2012), 2012, p. 3.
A. Felt, E. Chin, S. Hanna, D. Song, and D. Wagner, “Android permissions demistyfied,” in Proceedings of the ACM Conference on Computer and Communications Security, Chicago, Illinois, USA, pp. 627-638, 2011.
K. Z. Chen, N. M. Johnson, V. D'Silva, S. Dai, K. MacNamara, T. R. Magrino, E. Xue, J. Wu, M. Rinard, D. X. Song, “Contextual policy enforcement in Android applications with permission event graphs” in Proceedings of the Network and Distributed System Security Symposium (NDSS’2013), 2013.
W. Enck, M. Ongtang, and P. McDaniel, “On lightweight mobile phone application certification” in Proceedings of the 16th ACM Conference on Computer and Communications Security (CCS’09), 2009, pp. 235-245.
I. Burguera, U. Zurutuza, and S. Nadjm-Tehrani, “Crowdroid: behavior-based malware detection system for Android,” in Proceedings the 1st ACM Workshop on Security and Privacy in Smartphones and Mobile Devices, Chicago, USA, 2011, pp. 15-26.
T. Blasing, L. Batyuk, A. Schmidt, S. Camtepe, and S. Albaryak, “An Android Application Sandbox system for suspicious software detection,” in Proceedings the 5th International Conference on Malicious and Unwanted Software (MALWARE), Nancy, Lorraine, October 2010, pp. 55-62.
App Manifest: http://developer.android.com/guide/topics/manifest/manifest-intro.html, 2015
Manifest permission: http://developer.android.com/reference/android/Manifest.permission.html, 2015.
S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1999.
T. Kohonen, Self-organizing Maps. Springer Series in Information Sciences, Springer, Berlin, Heidelberg, New York, Third Extended Edition, (30) 501, 2001.
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