Zhiqiang Wang
ABSTRACT
Due to the open source and fragmentation of the Android system, its security is increasingly challenged. Currently, Android malware detection has certain deficiencies in large-scale and automation detection. In this paper, we proposed an Android malware detection framework based on Convolutional Neural Network (CNN). We used static analysis tools and python scripts to automatically extract 1003 static features, and transformed the features of each sample into a two-dimensional matrix as input to the CNN model. We selected 5000 malicious samples and 5000 benign samples for verification. The experimental results show that the detection accuracy of CNN reaches 99.68%, which is much higher than other algorithms.
- G Data, Bochum, Germany, Tech. Rep. [Online]. Available: https://www.gdatasoftware.com/mobile-internet-security-android.Google Scholar
- Feng Y, Anand S, Dillig I, et al (2014). [ACM Press the 22nd ACM SIGSOFT International Symposium - Hong Kong, China (2014.11.16-2014.11.21)] Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2014 - Apposcopy: semantics-based detection of Android malware through static analysis[J], 576--587.Google Scholar
- Chin E, Felt A P, Greenwood K, et al (2011) [ACM Press the 9th international conference - Bethesda, Maryland, USA (2011.06.28-2011.07.01)] Proceedings of the 9th international conference on Mobile systems, applications, and services - MobiSys \"11 - Analyzing inter-application communication in Android[C]// International Conference on Mobile Systems. ACM, 239.Google Scholar
- Sanz B, Santos I, Laorden C, et al (2013). PUMA: Permission Usage to Detect Malware in Android.[M]// International Joint Conference CISIS'12-ICEUTE'12-SOCO'12 Special Sessions. Springer Berlin Heidelberg.Google Scholar
- Oncreate W. (2016). Static Analyzer for Detecting Privacy Leaks in Android Applications[J].Google Scholar
- Shabtai A, Kanonov U, Elovici Y, et al (2012). "Andromaly": a behavioral malware detection framework for android devices[J]. Journal of Intelligent Information Systems, 38(1), 161--190.Google ScholarDigital Library
- Enck W, Gilbert P, Chun B G, et al (2014). Taint Droid: an information flow tracking system for real-time privacy monitoring on smartphones[C]//Usenix Symposium on Operating Systems Design and Implementation, OSDI 2010, October 4-6, 2010, Vancouver, Bc, Canada, Proceedings. DBLP, 393--407.Google Scholar
- Kwong L, Yin Y H (2012). DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis[C]// Usenix Conference on Security Symposium. USENIX Association.Google Scholar
- Tam K, Khan S J, Fattori A, et al (2015). [Internet Society Network and Distributed System Security Symposium - San Diego, CA (February 8-11, 2015)] Proceedings 2015 Network and Distributed System Security Symposium - CopperDroid: Automatic Reconstruction of Android Malware Behaviors[J].Google Scholar
- Yan J, Qi Y, Rao Q (2018). Detecting Malware with an Ensemble Method Based on Deep Neural Network[J]. Security & Communication Networks, 2018(1), 1--16.Google Scholar
- TaeGuen K, BooJoong K, Mina R, et al (2019). A Multimodal Deep Learning Method for Android Malware Detection Using Various Features[C]// IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 14(3), 3.Google Scholar
- Yuan Z, Lu Y, Xue Y (2016). Droiddetector: Android malware characterization and detection using deep learning[J]. Tsinghua Science & Technology, 21(1), 114--123.Google ScholarCross Ref
Index Terms
- Android Malware Detection Based on Convolutional Neural Networks
Recommendations
Deep Android Malware Detection
CODASPY '17: Proceedings of the Seventh ACM on Conference on Data and Application Security and PrivacyIn this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features ...
Deep Neural Networks for Automatic Android Malware Detection
ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017Because of the explosive growth of Android malware and due to the severity of its damages, the detection of Android malware has become an increasing important topic in cybersecurity. Currently, the major defense against Android malware is commercial ...
A Novel Android Malware Detection Approach Based on Convolutional Neural Network
ICCSP 2018: Proceedings of the 2nd International Conference on Cryptography, Security and PrivacyWith the explosive growth of Android malware, there is a pressure for us to improve the performance of existing malware detection approaches. In this paper, we proposed DeepClassifyDroid, a novel android malware detection system based on deep learning. ...
Comments