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題名 歸納惡意軟體特徵
Malware Family Characterization作者 劉其峰
Liu, Chi-Feng貢獻者 郁方
Yu, Fang
劉其峰
Liu, Chi-Feng關鍵詞 遞歸神經網路
增長層級式自我組織映射圖
長短期記憶
惡意軟體
動態分析
序列編碼
RNN
GHSOM
LSTM
Malware
Sequence encoding
Dynamic analysis日期 2018 上傳時間 3-九月-2018 15:47:50 (UTC+8) 摘要 Nowadays, a massive amount of sensitive data which are accessible and connected through personal computers and cloud services attracts hackers to develop malicious software (malware) to steal them. Owing to the success of deep learning on image and language recognition, researchers direct security systems to analyze and identify malware with deep learning approaches. This paper addresses the problem of analyzing and identifying complex and unstructured malware behaviors by proposing a framework of combining unsupervised and supervised learning algorithms with a novel sequence-aware encoding method. Particularly, we adopt a hybrid GHSOM (the Growing Hierarchical Self-Organizing Map) algorithm to cluster and encode similar malware behavior sequences from system call sequences to clustering feature vectors. Then, a Recurrent Neural Network (RNN) is trained to detect malware and predict their corresponding malware families based on the sequence of the behavior vectors. Our experiments show that the accuracy rate can be up to 0.98 in malware detection and 0.719 in malware classification of an 18-category malware dataset. 參考文獻 [1] A.-r. M. https://commons.wikimedia.org/wiki/User:BiObserve (Raster version previously uploaded to Wikimedia)Alex Graves and G. H. (original)Eddie Antonio Santos (SVG version with TeX math), “Peephole long short-term memory,” ”[CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons”.[2] R. J. Canzanese Jr, “Detection and classification of malicious processes using system all analysis,” Ph.D. dissertation, Drexel University, 2015.[3] T. Moore, D. J. Pym, C. Ioannidis et al., Economics of information security and privacy. Springer, 2010.[4] N. Idika and A. P. Mathur, “A survey of malware detection techniques,” Purdue University, vol. 48, 2007.[5] “Manalyze,” https://github.com/JusticeRage/Manalyze, [Online; accessed 4-May2018].[6] S. Forrest, S. A. Hofmeyr, A. Somayaji, and T. A. Longstaff, “A sense of self for unix processes,” in Security and Privacy, 1996. Proceedings., 1996 IEEE Symposium on. IEEE, 1996, pp. 120–128.[7] M. Rhode, P. Burnap, and K. Jones, “Early stage malware prediction using recurrent neural networks,” arXiv preprint arXiv:1708.03513, 2017.[8] X. Wang and S. M. Yiu, “A multi-task learning model for malware classification with useful file access pattern from api call sequence,” arXiv preprint arXiv:1610.05945, 2016.[9] B. Kolosnjaji, A. Zarras, G. Webster, and C. Eckert, “Deep learning for classification of malware system call sequences,” in Australasian Joint Conference on Artificial Intelligence. Springer, 2016, pp. 137–149.[10] S. Tobiyama, Y. Yamaguchi, H. Shimada, T. Ikuse, and T. Yagi, “Malware detection with deep neural network using process behavior,” in Computer Software and Applications Conference (COMPSAC), 2016 IEEE 40th Annual, vol. 2. IEEE, 2016, pp. 577–582.[11] R. Pascanu, J. W. Stokes, H. Sanossian, M. Marinescu, and A. Thomas, “Malware classification with recurrent networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. IEEE, 2015, pp. 1916–1920.[12] C.-H. Chiu, J.-J. Chen, and F. Yu, “An effective distributed ghsom algorithm for unsupervised clustering on big data,” in Big Data (BigData Congress), 2017 IEEE International Congress on. IEEE, 2017, pp. 297–304.[13] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997. [Online]. Available: http://dx.doi.org/10. 1162/neco.1997.9.8.1735[14] H. Sak, A. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acoustic modeling,” in Fifteenth annual conference of the international speech communication association, 2014.[15] Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE transactions on neural networks, vol. 5, no. 2, pp. 157–166, 1994.[16] F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with lstm,” 1999.[17] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014.[18] T. Mikolov, M. Karafi´at, L. Burget, J. Cernock"y, and S. Khudanpur, “Recurrent ˇ neural network based language model,” in Eleventh Annual Conference of the International Speech Communication Association, 2010.[19] A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, 2013, pp. 6645–6649.[20] I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems, 2014, pp. 3104– 3112.[21] T. Kohonen, “The self-organizing map,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1464–1480, 1990.[22] A. Rauber, D. Merkl, and M. Dittenbach, “The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data,” IEEE Transactions on Neural Networks, vol. 13, no. 6, pp. 1331–1341, 2002.[23] H. Shi, T. Hamagami, K. Yoshioka, H. Xu, K. Tobe, and S. Goto, “Structural classification and similarity measurement of malware,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 9, no. 6, pp. 621–632, 2014.[24] W. Shuwei, W. Baosheng, Y. Tang, and Y. Bo, “Malware clustering based on snn density using system calls,” in International Conference on Cloud Computing and Security. Springer, 2015, pp. 181–191.[25] M. Dittenbach, D. Merkl, and A. Rauber, “The growing hierarchical self-organizing map,” in Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, vol. 6. IEEE, 2000, pp. 15–19.[26] C. Guarnieri, A. Tanasi, J. Bremer, and M. Schloesser, “The cuckoo sandbox,” 2012.[27] Y.-H. Li, Y.-R. Tzeng, and F. Yu, “Viso: Characterizing malicious behaviors of virtual machines with unsupervised clustering,” in Cloud Computing Technology and Science (CloudCom), 2015 IEEE 7th International Conference on. IEEE, 2015, pp. 34–41.[28] S.-W. Lee and F. Yu, “Securing kvm-based cloud systems via virtualization introspection,” in System Sciences (HICSS), 2014 47th Hawaii International Conference on. IEEE, 2014, pp. 5028–5037.[29] F. Yu, S.-y. Huang, L.-c. Chiou, and R.-h. Tsaih, “Clustering ios executable using self-organizing maps,” in Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013, pp. 1–8.[30] R.-S. Pirscoveanu, M. Stevanovic, and J. M. Pedersen, “Clustering analysis of malware behavior using self organizing map,” in Cyber Situational Awareness, Data Analytics And Assessment (CyberSA), 2016 International Conference On. IEEE, 2016, pp. 1–6.[31] S. Marinai, E. Marino, and G. Soda, “Embedded map projection for dimensionality reduction-based similarity search,” in Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). Springer, 2008, pp. 582–591.[32] “Virustotal,” https://www.virustotal.com/en/, [Online; accessed 4-April-2018].[33] M. Sebasti´an, R. Rivera, P. Kotzias, and J. Caballero, “Avclass: A tool for massive malware labeling,” in International Symposium on Research in Attacks, Intrusions, and Defenses. Springer, 2016, pp. 230–253.[34] Z. C. Lipton, J. Berkowitz, and C. Elkan, “A critical review of recurrent neural networks for sequence learning,” arXiv preprint arXiv:1506.00019, 2015.[35] W. Hu and Y. Tan, “Black-box attacks against rnn based malware detection algorithms,” arXiv preprint arXiv:1705.08131, 2017.[36] “strace(1) - linux man page,” https://linux.die.net/man/1/strace, [Online; accessed 5-April-2018].[37] S.-W. Hsiao, Y.-N. Chen, Y. S. Sun, and M. C. Chen, “A cooperative botnet profilingand detection in virtualized environment,” in Communications and Network Security (CNS), 2013 IEEE Conference on. IEEE, 2013, pp. 154–162.[38] “Linux syscall reference,” https://syscalls.kernelgrok.com/, [Online; accessed 11- August-2018]. 描述 碩士
國立政治大學
資訊管理學系
105356019資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105356019 資料類型 thesis dc.contributor.advisor 郁方 zh_TW dc.contributor.advisor Yu, Fang en_US dc.contributor.author (作者) 劉其峰 zh_TW dc.contributor.author (作者) Liu, Chi-Feng en_US dc.creator (作者) 劉其峰 zh_TW dc.creator (作者) Liu, Chi-Feng en_US dc.date (日期) 2018 en_US dc.date.accessioned 3-九月-2018 15:47:50 (UTC+8) - dc.date.available 3-九月-2018 15:47:50 (UTC+8) - dc.date.issued (上傳時間) 3-九月-2018 15:47:50 (UTC+8) - dc.identifier (其他 識別碼) G0105356019 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/119881 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 105356019 zh_TW dc.description.abstract (摘要) Nowadays, a massive amount of sensitive data which are accessible and connected through personal computers and cloud services attracts hackers to develop malicious software (malware) to steal them. Owing to the success of deep learning on image and language recognition, researchers direct security systems to analyze and identify malware with deep learning approaches. This paper addresses the problem of analyzing and identifying complex and unstructured malware behaviors by proposing a framework of combining unsupervised and supervised learning algorithms with a novel sequence-aware encoding method. Particularly, we adopt a hybrid GHSOM (the Growing Hierarchical Self-Organizing Map) algorithm to cluster and encode similar malware behavior sequences from system call sequences to clustering feature vectors. Then, a Recurrent Neural Network (RNN) is trained to detect malware and predict their corresponding malware families based on the sequence of the behavior vectors. Our experiments show that the accuracy rate can be up to 0.98 in malware detection and 0.719 in malware classification of an 18-category malware dataset. en_US dc.description.tableofcontents Contents1 Introduction 12 Related Work 32.1 Neural Network for Malware Classification and Detection 32.2 Recurrent Neural Networks 42.3 Unsupervised Learning Clustering 53 Methodology 63.1 System Overview 63.2 Software Pool 73.3 System Call Frequency Encoding Method 83.4 Unsupervised Learning Clustering 93.5 The Hierarchical SOM Encoding Method 113.5.1 Decimal Encoding Method 123.5.2 Weighted One-hot Encoding Method 133.6 Malware Family Labeling 153.7 Recurrent Neural Network 154 Experiment 174.1 System Architecture 174.2 Experiment Dataset 184.3 Unsupervised Learning Clustering 194.4 Recurrent Neural Network 204.5 Malware Detection Experiment 204.6 Malware Family Classification Experiment 215 Conclusion 23List of Figures1 Long Short-Term Memory Model [1] 42 System overview 73 The structure of GHSOM 104 Decimal encoding method 125 The weighted one-hot encoding method 146 Many-to-one LSTM model with softmax layer 167 System architecture 178 1000-gram interval behavior vectors 199 Testing accuracy of 1000-gram decimal encoding vector 2410 Testing accuracy of 1000-gram weighted one-hot encoding vector 2511 Testing accuracy of 2000-gram decimal encoding vector 2612 Testing accuracy of 2000-gram weighted one-hot encoding vector 27List of Tables1 System calls reference [2] 192 Interval behavior cluster vectors by weighted one-hot encoding method 203 Interval behavior cluster vectors by decimal encoding method 214 Result of malware detection experiment 215 Training & testing accuracy of 1000-gram vectors by decimal encodingmethod 226 Training & testing accuracy of 1000-gram vectors by weighted one-hot en-coding method 227 Training & testing accuracy of 2000-gram vectors by decimal encodingmethod 238 Training & testing accuracy of 2000-gram vectors by weighted one-hot en-coding method 24 zh_TW dc.format.extent 846476 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105356019 en_US dc.subject (關鍵詞) 遞歸神經網路 zh_TW dc.subject (關鍵詞) 增長層級式自我組織映射圖 zh_TW dc.subject (關鍵詞) 長短期記憶 zh_TW dc.subject (關鍵詞) 惡意軟體 zh_TW dc.subject (關鍵詞) 動態分析 zh_TW dc.subject (關鍵詞) 序列編碼 zh_TW dc.subject (關鍵詞) RNN en_US dc.subject (關鍵詞) GHSOM en_US dc.subject (關鍵詞) LSTM en_US dc.subject (關鍵詞) Malware en_US dc.subject (關鍵詞) Sequence encoding en_US dc.subject (關鍵詞) Dynamic analysis en_US dc.title (題名) 歸納惡意軟體特徵 zh_TW dc.title (題名) Malware Family Characterization en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] A.-r. M. https://commons.wikimedia.org/wiki/User:BiObserve (Raster version previously uploaded to Wikimedia)Alex Graves and G. H. (original)Eddie Antonio Santos (SVG version with TeX math), “Peephole long short-term memory,” ”[CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons”.[2] R. J. Canzanese Jr, “Detection and classification of malicious processes using system all analysis,” Ph.D. dissertation, Drexel University, 2015.[3] T. Moore, D. J. Pym, C. Ioannidis et al., Economics of information security and privacy. Springer, 2010.[4] N. Idika and A. P. Mathur, “A survey of malware detection techniques,” Purdue University, vol. 48, 2007.[5] “Manalyze,” https://github.com/JusticeRage/Manalyze, [Online; accessed 4-May2018].[6] S. Forrest, S. A. Hofmeyr, A. Somayaji, and T. A. Longstaff, “A sense of self for unix processes,” in Security and Privacy, 1996. Proceedings., 1996 IEEE Symposium on. IEEE, 1996, pp. 120–128.[7] M. Rhode, P. Burnap, and K. Jones, “Early stage malware prediction using recurrent neural networks,” arXiv preprint arXiv:1708.03513, 2017.[8] X. Wang and S. M. Yiu, “A multi-task learning model for malware classification with useful file access pattern from api call sequence,” arXiv preprint arXiv:1610.05945, 2016.[9] B. Kolosnjaji, A. Zarras, G. Webster, and C. Eckert, “Deep learning for classification of malware system call sequences,” in Australasian Joint Conference on Artificial Intelligence. Springer, 2016, pp. 137–149.[10] S. Tobiyama, Y. Yamaguchi, H. Shimada, T. Ikuse, and T. Yagi, “Malware detection with deep neural network using process behavior,” in Computer Software and Applications Conference (COMPSAC), 2016 IEEE 40th Annual, vol. 2. IEEE, 2016, pp. 577–582.[11] R. Pascanu, J. W. Stokes, H. Sanossian, M. Marinescu, and A. Thomas, “Malware classification with recurrent networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. IEEE, 2015, pp. 1916–1920.[12] C.-H. Chiu, J.-J. Chen, and F. Yu, “An effective distributed ghsom algorithm for unsupervised clustering on big data,” in Big Data (BigData Congress), 2017 IEEE International Congress on. IEEE, 2017, pp. 297–304.[13] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997. [Online]. Available: http://dx.doi.org/10. 1162/neco.1997.9.8.1735[14] H. Sak, A. Senior, and F. Beaufays, “Long short-term memory recurrent neural network architectures for large scale acoustic modeling,” in Fifteenth annual conference of the international speech communication association, 2014.[15] Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE transactions on neural networks, vol. 5, no. 2, pp. 157–166, 1994.[16] F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with lstm,” 1999.[17] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014.[18] T. Mikolov, M. Karafi´at, L. Burget, J. Cernock"y, and S. Khudanpur, “Recurrent ˇ neural network based language model,” in Eleventh Annual Conference of the International Speech Communication Association, 2010.[19] A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, 2013, pp. 6645–6649.[20] I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Advances in neural information processing systems, 2014, pp. 3104– 3112.[21] T. Kohonen, “The self-organizing map,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1464–1480, 1990.[22] A. Rauber, D. Merkl, and M. Dittenbach, “The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data,” IEEE Transactions on Neural Networks, vol. 13, no. 6, pp. 1331–1341, 2002.[23] H. Shi, T. Hamagami, K. Yoshioka, H. Xu, K. Tobe, and S. Goto, “Structural classification and similarity measurement of malware,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 9, no. 6, pp. 621–632, 2014.[24] W. Shuwei, W. Baosheng, Y. Tang, and Y. Bo, “Malware clustering based on snn density using system calls,” in International Conference on Cloud Computing and Security. Springer, 2015, pp. 181–191.[25] M. Dittenbach, D. Merkl, and A. Rauber, “The growing hierarchical self-organizing map,” in Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, vol. 6. IEEE, 2000, pp. 15–19.[26] C. Guarnieri, A. Tanasi, J. Bremer, and M. Schloesser, “The cuckoo sandbox,” 2012.[27] Y.-H. Li, Y.-R. Tzeng, and F. Yu, “Viso: Characterizing malicious behaviors of virtual machines with unsupervised clustering,” in Cloud Computing Technology and Science (CloudCom), 2015 IEEE 7th International Conference on. IEEE, 2015, pp. 34–41.[28] S.-W. Lee and F. Yu, “Securing kvm-based cloud systems via virtualization introspection,” in System Sciences (HICSS), 2014 47th Hawaii International Conference on. IEEE, 2014, pp. 5028–5037.[29] F. Yu, S.-y. Huang, L.-c. Chiou, and R.-h. Tsaih, “Clustering ios executable using self-organizing maps,” in Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013, pp. 1–8.[30] R.-S. Pirscoveanu, M. Stevanovic, and J. M. Pedersen, “Clustering analysis of malware behavior using self organizing map,” in Cyber Situational Awareness, Data Analytics And Assessment (CyberSA), 2016 International Conference On. IEEE, 2016, pp. 1–6.[31] S. Marinai, E. Marino, and G. Soda, “Embedded map projection for dimensionality reduction-based similarity search,” in Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). Springer, 2008, pp. 582–591.[32] “Virustotal,” https://www.virustotal.com/en/, [Online; accessed 4-April-2018].[33] M. Sebasti´an, R. Rivera, P. Kotzias, and J. Caballero, “Avclass: A tool for massive malware labeling,” in International Symposium on Research in Attacks, Intrusions, and Defenses. Springer, 2016, pp. 230–253.[34] Z. C. Lipton, J. Berkowitz, and C. Elkan, “A critical review of recurrent neural networks for sequence learning,” arXiv preprint arXiv:1506.00019, 2015.[35] W. Hu and Y. Tan, “Black-box attacks against rnn based malware detection algorithms,” arXiv preprint arXiv:1705.08131, 2017.[36] “strace(1) - linux man page,” https://linux.die.net/man/1/strace, [Online; accessed 5-April-2018].[37] S.-W. Hsiao, Y.-N. Chen, Y. S. Sun, and M. C. Chen, “A cooperative botnet profilingand detection in virtualized environment,” in Communications and Network Security (CNS), 2013 IEEE Conference on. IEEE, 2013, pp. 154–162.[38] “Linux syscall reference,” https://syscalls.kernelgrok.com/, [Online; accessed 11- August-2018]. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.MIS.025.2018.A05 -
