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When VirusShare was first being sketched out in 2011 with the goal of collecting, indexing, and freely sharing samples of malware to analysts, researchers, and the informaion security community, I didn't quite comprehend the scope of the need for such a repository and how well it would be received. VirusShare is proud to have played a small part in assisting the tireless efforts of the global research community and I am thankful to the many researchers who have contributed to the project. Below is a list of publications related to very interesting malware research and have cited VirusShare as a data source.
 
2017
 
Wang, S., Chen, Z., Li, X., Wang, L., Ji, K., & Zhao, C. (2017, August). Android Malware Clustering Analysis on Network-Level Behavior. In International Conference on Intelligent Computing (pp. 796-807). Springer, Cham.

Abbas, M. F. B., & Srikanthan, T. (2017, July). Low-Complexity Signature-Based Malware Detection for IoT Devices. In International Conference on Applications and Techniques in Information Security (pp. 181-189). Springer, Singapore.

Rubio Ayala, S. An automated behaviour-based malware analysis method based on free open source software.

Pektaş, A., & Acarman, T. (2017, May). Ensemble Machine Learning Approach for Android Malware Classification Using Hybrid Features. In International Conference on Computer Recognition Systems (pp. 191-200). Springer, Cham.

Kim, H., Cho, T., Ahn, G. J., & Yi, J. H. (2017). Risk assessment of mobile applications based on machine learned malware dataset. Multimedia Tools and Applications, 1-16.

Miller, C., Glendowne, D., Cook, H., Thomas, D., Lanclos, C., & Pape, P. (2017). Insights gained from constructing a large scale dynamic analysis platform. Digital Investigation, 22, S48-S56.

Ramirez, A. G., Pedreira, M. M., Grigoras, C., Betev, L., Lara, C., & Collaboration, U. (2017). A Security Monitoring Framework For Virtualization Based HEP Infrastructures. arXiv preprint arXiv:1704.04782.

Zhu, H. J., You, Z. H., Zhu, Z. X., Shi, W. L., Chen, X., & Cheng, L. (2017). DroidDet: Effective and robust detection of android malware using static analysis along with rotation forest model. Neurocomputing.

Pooryousef, S., & Amini, M. (2017). Enhancing Accuracy of Android Malware Detection using Intent Instrumentation. In ICISSP (pp. 380-388).

Sujyothi, A., & Acharya, S. (2017). Dynamic Malware Analysis and Detection in Virtual Environment. International Journal of Modern Education and Computer Science, 9(3), 48.

Alruhaily, N., Bordbar, B., & Chothia, T. (2017). Towards an Understanding of the Misclassification Rates of Machine Learning-based Malware Detection Systems. In ICISSP (pp. 101-112).

Wei, F., Li, Y., Roy, S., Ou, X., & Zhou, W. (2017, July). Deep Ground Truth Analysis of Current Android Malware. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (pp. 252-276). Springer, Cham.

Blasco, J., & Chen, T. M. (2017). Automated generation of colluding apps for experimental research. Journal of Computer Virology and Hacking Techniques, 1-12.

Idrees, F., Rajarajan, M., Conti, M., Chen, T. M., & Rahulamathavan, Y. (2017). PIndroid: A novel Android malware detection system using ensemble learning methods. Computers & Security, 68, 36-46.

Fowler, J. E. Compression of Virtual-Machine Memory in Dynamic Malware Analysis.

Nguyen-Vu, L., Chau, N. T., Kang, S., & Jung, S. (2017). Android Rooting: An Arms Race between Evasion and Detection.

Li, Y., Jang, J., Hu, X., & Ou, X. (2017). Android malware clustering through malicious payload mining. arXiv preprint arXiv:1707.04795.

Kotzias, P., & Caballero, J. An Analysis of Pay-per-Install Economics Using Entity Graphs.

Anh, H. N., Ng, W. K., & Ariyapala, K. Predicting Risk Level of Executables: an Application of Online Learning. In Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17).

Hatada, M., & Mori, T. Detecting and Classifying Android PUAs by similarity of DNS queries.

Zhu, H. J., Jiang, T. H., Ma, B., You, Z. H., Shi, W. L., & Cheng, L. (2017). HEMD: a highly efficient random forest-based malware detection framework for Android. Neural Computing and Applications, 1-9.

Carlin, D., O’Kane, P., & Sezer, S. (2017). Dynamic Analysis of Malware Using Run-Time Opcodes. In Data Analytics and Decision Support for Cybersecurity (pp. 99-125). Springer, Cham.

Ye, Y., Wu, L., Hong, Z., & Huang, K. (2017). A Risk Classification Based Approach for Android Malware Detection. KSII Transactions on Internet & Information Systems, 11(2).

Webster, G. D., Kolosnjaji, B., von Pentz, C., Kirsch, J., Hanif, Z. D., Zarras, A., & Eckert, C. (2017, July). Finding the Needle: A Study of the PE32 Rich Header and Respective Malware Triage. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (pp. 119-138). Springer, Cham.

Tang, X., & Bai, Z. CS846 Project Report.

Yang, X., Lo, D., Li, L., Xia, X., Bissyandé, T. F., & Klein, J. (2017). Characterizing malicious Android apps by mining topic-specific data flow signatures. Information and Software Technology.

Jerlin, A., & Chinnappan, J. (2017). ESAA: Efficient Sequence Alignment Algorithm for Dynamic Malware Analysis in Windows Executable Using API Call Sequence. DNA sequence, 291.

Nauman, M., Tanveer, T. A., Khan, S., & Syed, T. A. (2017). Deep neural architectures for large scale android malware analysis. Cluster Computing, 1-20.

Wang, X., Wang, W., He, Y., Liu, J., Han, Z., & Zhang, X. (2017). Characterizing Android apps’ behavior for effective detection of malapps at large scale. Future Generation Computer Systems, 75, 30-45.

馮志峰 [Feng Zhifeng]. (2017). 基於機器學習 & Android Dynamic Framework 的惡意軟體檢測和攔截 [Malware Detection and Blocking Based on Machine Learning & Android Dynamic Framework]. 臺灣大學資訊工程學研究所學位論文 [Institute of Information Engineering, Taiwan University], 1-37.

Fan, M., Liu, J., Wang, W., Li, H., Tian, Z., & Liu, T. (2017). DAPASA: detecting android piggybacked apps through sensitive subgraph analysis. IEEE Transactions on Information Forensics and Security, 12(8), 1772-1785.

Moussa, M., Di Penta, M., Antoniol, G., & Beltrame, G. (2017, May). ACCUSE: helping users to minimize Android app privacy concerns. In Proceedings of the 4th International Conference on Mobile Software Engineering and Systems (pp. 144-148). IEEE Press.

Bosu, A., Liu, F., Yao, D. D., & Wang, G. (2017, April). Collusive data leak and more: Large-scale threat analysis of inter-app communications. In Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security (pp. 71-85). ACM.

Thoresen, H. M. (2017). Automated triage of samples for malware analysis Master's thesis, NTNU.

张仁斌, & 李钢 [Zhang Renbin, & Li Gang]. (2017). 基于关联特征的贝叶斯 Android 恶意程序检测技术 [Bayesian Android malware detection techniques based on the associated feature]. 计算机应用与软件 [Computer software and applications], 34(1), 286-292.

何文才, 闫翔宇, 刘培鹤, & 刘畅 [He Wencai, Yan Xiangyu, Liu Peihe, & Liu Chang]. (2017). 基于最小距离分类器的 Android 恶意软件检测方案 [Android malware detection scheme based on minimum distance classifier]. 计算机应用研究 [Computer Research], 34(7), 2184-2188.

Dong, Y. (2017). Android Malware Prediction by Permission Analysis and Data Mining.

Feng, P., Ma, J., & Sun, C. (2017). Selecting Critical Data Flows in Android Applications for Abnormal Behavior Detection. Mobile Information Systems, 2017.

Wang, T. S. (2017). 應用頻譜分析與群體結構辨識於網路攻擊偵防之研究 [Application of Spectrum Analysis and Group Structure Recognition in Network Attack Detection]. 成功大學電腦與通信工程研究所學位論文 [Successful University Computer and Communication Engineering Research Institute], 1-128.

程运安, & 汪奕祥 [Cheng Yunan, & Wang Yi Xiang]. (2017). 基于权限统计的 Android 恶意应用检测算法 [Android malicious application detection algorithm based on statistical authority]. 计算机应用与软件 [Computer Applications and Software], 1, 055.

Brandon Jr, R. A. (2017). Vector Space Representations of Executable Code Doctoral dissertation, University of Maryland, Baltimore County.

邴丕政, 戴紫彬, & 戴强 [Bing Yizheng, Dai Zhibin, & Dai Qiang]. (2017). 基于哈希树的度量证据可信存储方案设计 [Design of Trusted Storage Schemes for Metrics Based on Hash Trees]. 计算机应用与软件 [Computer Applications and Software], 34(1), 316-320.

Xu, Y., Wu, C., Zheng, K., Wang, X., Niu, X., & Lu, T. (2017). Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection. Security and Communication Networks, 2017.

Javaheri, D., & Hosseinzadeh, M. A Framework for Recognition and Confronting of Obfuscated Malwares Based on Memory Dumping and Filter Drivers. Wireless Personal Communications, 1-19.

Sun, R., Yuan, X., Lee, A., Bishop, M., Porter, D. E., Li, X., ... & Oliveira, D. (2017). The Dose Makes the Poison--Leveraging Uncertainty for Effective Malware Detection.

Kumara, A., & Jaidhar, C. D. (2017). Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMM. Future Generation Computer Systems.

Narayanan, A., Chandramohan, M., Chen, L., & Liu, Y. (2017). A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization. arXiv preprint arXiv:1704.01759.

Petsios, T., Tang, A., Stolfo, S., Keromytis, A. D., & Jana, S. (2017, May). NEZHA: Efficient Domain-Independent Differential Testing. In Proceedings of the 38th IEEE Symposium on Security & Privacy, (San Jose, CA).

Xie, X. (2017). Security Improvement in Cloud Computing Environment through Memory Analysis Doctoral dissertation, The University of North Carolina at Charlotte.

张骁敏, 刘静, 庄俊玺, & 赖英旭 [Zhang Xiao Min, Liu Jing, Zhuang Jun Xi, & Lai Yingxu]. (2017). 基于权限与行为的 Android 恶意软件检测研究 [Research Android malware detection and behavior-based permissions]. 网络与信息安全学报 [Network and Information Security Technology], 3(3), 51-57.

Silva, R. C. (2017). Malflow: um framework para geração automatizada de assinaturas de malwares baseado em fluxo de dados de rede. [Malflow: a framework for automated generation of malware signatures based on network data flow.] Master's Thesis, Instituto de Biociências, Letras e Ciências Exatas. [Institute of Biosciences, Letters and Exact Sciences]

Le Guernic, C., & Legay, A. (2017, April). Ransomware and the Legacy Crypto API. In Risks and Security of Internet and Systems: 11th International Conference, CRiSIS 2016, Roscoff, France, September 5-7, 2016, Revised Selected Papers (Vol. 10158, p. 11). Springer.

Botacin, M. F., de Geus, P. L., & Grégio, A. R. A. (2017). The other guys: automated analysis of marginalized malware. Journal of Computer Virology and Hacking Techniques, 1-12.

Kirubavathi, G., & Anitha, R. (2017). Structural analysis and detection of android botnets using machine learning techniques. International Journal of Information Security, 1-15.

Kumar, A., Kuppusamy, K. S., & Aghila, G. (2017). A learning model to detect maliciousness of portable executable using integrated feature set. Journal of King Saud University-Computer and Information Sciences.

Meng, G. (2017). A Semantic-based Analysis of Android Malware for Detection, Generation, and Trend Analysis. Doctoral dissertation, Nanyang Technological University.

Feng, P., Sun, C., & Ma, J. (2017). Measuring the risk value of sensitive dataflow path in Android applications. Security and Communication Networks.

Mishra, P., Pilli, E. S., Varadharajan, V., & Tupakula, U. (2017). Intrusion detection techniques in cloud environment: A survey. Journal of Network and Computer Applications, 77, 18-47.

Wang, T. S., Lin, H. T., Cheng, W. T., & Chen, C. Y. (2017). DBod: Clustering and detecting DGA-based botnets using DNS traffic analysis. Computers & Security, 64, 1-15.

Leite, L., Silva, D. G., & Grégio, A. (2017) Agrupamento de malvvare por comportamento de execução usando lógica fuzzy. [Malware clustering by execution behavior using fuzzy logic.] O Simpósio Brasileiro em Segurança da Informação e de Sistemas Computacionais. [The Brazilian Symposium on Information Security and Computational Systems.]

 
2016
 
Yadegari, B. (2016). Automatic deobfuscation and reverse engineering of obfuscated code. The University of Arizona.

Marques, J. M. C. L. (2016). Controlo e Ocultação de dados pessoais em dispositivos móveis.

Tännler, L., & Vetsch, M. (2016). Forensic Triage Kit. Management, 1, 5.

Valente, E. C. A., Shishido, H. Y., & Estrella, J. C. Detecç ao Distribuıda de Programas Maliciosos para Dispositivos Móveis em Ambientes de Computaç ao em Nuvem.

Cho, T., Kim, H., Lee, J., Jung, M., & Yi, J. H. (2016). A Scheme for Identifying Malicious Applications Based on API Characteristics. Journal of the Korea Institute of Information Security and Cryptology, 26(1), 187-196.

Gandotra, E., Bansal, D., & Sofat, S. (2016, December). Zero-day malware detection. In Embedded Computing and System Design (ISED), 2016 Sixth International Symposium on (pp. 171-175). IEEE.

Cho, H. S., Lee, S. G., Kim, N. H., Kim, B. I., & Lee, T. J. (2016). U.S. Patent Application No. 15/006,708.

Jazi, H. H., & Ghorbani, A. A. (2016, December). Dynamic graph-based malware classifier. In Privacy, Security and Trust (PST), 2016 14th Annual Conference on (pp. 112-120). IEEE.

Shalaginov, A., & Franke, K. (2016, December). Automated intelligent multinomial classification of malware species using dynamic behavioural analysis. In Privacy, Security and Trust (PST), 2016 14th Annual Conference on (pp. 70-77). IEEE.

Deepta, K. P., & Salim, A. (2016, November). Detecting Malwares Using Dynamic Call Graphs and Opcode Patterns. In International Conference on Advances in Computing and Data Sciences (pp. 91-101). Springer, Singapore.

Palisse, A., Le Bouder, H., Lanet, J. L., Le Guernic, C., & Legay, A. (2016, September). Ransomware and the Legacy Crypto API. In International Conference on Risks and Security of Internet and Systems (pp. 11-28). Springer, Cham.

Huynh, N. A., Ng, W. K., & Do, H. G. (2016, October). On periodic behavior of malware: experiments, opportunities and challenges. In Malicious and Unwanted Software (MALWARE), 2016 11th International Conference on (pp. 1-8). IEEE.

Korczynski, D. (2016, October). RePEconstruct: reconstructing binaries with self-modifying code and import address table destruction. In Malicious and Unwanted Software (MALWARE), 2016 11th International Conference on (pp. 1-8). IEEE.

LEE, S. G., CHO, H. S., Kim, N. H., Kim, B. I., & Lee, T. J. (2016). U.S. Patent Application No. 15/006,761.

LEE, S. G., CHO, H. S., Kim, N. H., Kim, B. I., & Lee, T. J. (2016). U.S. Patent Application No. 15/006,770.

Yao, D., & Ryder, B. (2016). Detection Of Malware Collusion With Static Dependence Analysis On Inter APP Communication (No. AFRL-RI-RS-TR-2016-277). Virginia Polytechnic Institute and State University Blacksburg United States.

Afonso, V. M. (2016). Improving Android security with malware detection and automatic security policy generation / Aprimorando a segurança do Android através de detecção de malware e geração automática de políticas.

Villanova-Pascual, O. (2016). Malware en Android y medidas de prevención.

Baset, M. (2016). Machine Learning for Malware Detection. Master's Disseratation, Heriot Watt University.

Fu, J., Li, P., Lin, Y., & Ding, S. (2016, August). Android App Malicious Behavior Detection Based on User Intention. In Trustcom/BigDataSE/I​ SPA, 2016 IEEE (pp. 560-567). IEEE.

Su, D., Wang, W., Wang, X., & Liu, J. (2016, August). Anomadroid: Profiling Android Applications' Behaviors for Identifying Unknown Malapps. In Trustcom/BigDataSE/I​ SPA, 2016 IEEE (pp. 691-698). IEEE.

DeLoach, J., Caragea, D., & Ou, X. Android Malware Detection with Weak Ground Truth Data. In Big Data (Big Data), 2016 IEEE International Conference on

Fraley, J. B., & Figueroa, M. (2016, March). Polymorphic malware detection using topological feature extraction with data mining. In SoutheastCon, 2016 (pp. 1-7). IEEE.

Das, S. K. (2016). Hardware-Assisted Online Defense Against Malware and Exploits. Doctoral dissertation, Nanyang Technological University.

Martin, W., Sarro, F., Jia, Y., Zhang, Y., & Harman, M. (2016). A survey of app store analysis for software engineering. IEEE Transactions on Software Engineering.

Das, S., Liu, Y., Zhang, W., & Chandramohan, M. (2016). Semantics-based online malware detection: Towards efficient real-time protection against malware. IEEE Transactions on Information Forensics and Security, 11(2), 289-302.

Jang, J. W., Yun, J., Mohaisen, A., Woo, J., & Kim, H. K. (2016). Detecting and classifying method based on similarity matching of Android malware behavior with profile. SpringerPlus, 5(1), 273.

Martín, A., Menéndez, H. D., & Camacho, D. (2016, October). String-based malware detection for android environments. In International Symposium on Intelligent and Distributed Computing (pp. 99-108). Springer International Publishing.

Sebastián, M., Rivera, R., Kotzias, P., & Caballero, J. (2016, September). Avclass: A tool for massive malware labeling. In International Symposium on Research in Attacks, Intrusions, and Defenses (pp. 230-253). Springer International Publishing.

Suarez-Tangil, G., Tapiador, J. E., Lombardi, F., & Di Pietro, R. (2016). ALTERDROID: differential fault analysis of obfuscated smartphone malware. IEEE Transactions on Mobile Computing, 15(4), 789-802.

Sood, A. K., Zeadally, S., & Enbody, R. J. (2016). An Empirical Study of HTTP-based Financial Botnets. IEEE Transactions on Dependable and Secure Computing, 13(2), 236-251.

Wu, S., Wang, P., Li, X., & Zhang, Y. (2016). Effective detection of android malware based on the usage of data flow APIs and machine learning. Information and Software Technology, 75, 17-25.

Narayanan, A., Chandramohan, M., Chen, L., Liu, Y., & Saminathan, S. (2016). subgraph2vec: Learning distributed representations of rooted sub-graphs from large graphs. arXiv preprint arXiv:1606.08928.

Kolosnjaji, B., Zarras, A., Lengyel, T., Webster, G., & Eckert, C. (2016). Adaptive semantics-aware malware classification. Detection of Intrusions and Malware, and Vulnerability Assessment, 419-439.

Narayanan, A., Meng, G., Yang, L., Liu, J., & Chen, L. (2016, July). Contextual Weisfeiler-Lehman graph kernel for malware detection. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 4701-4708). IEEE.

Xu, L., Zhang, D., Jayasena, N., & Cavazos, J. Hadm: Hybrid analysis for detection of malware.

Truong, D. T., Cheng, G., Jakalan, A., Guo, X. J., & Zhou, A. P. (2016). Detecting DGA-based botnet with DNS traffic analysis in monitored network. 網際網路技術學刊 [Journal of the Internet Technology], 17(2), 217-230.

Sgandurra, D., Muñoz-González, L., Mohsen, R., & Lupu, E. C. (2016). Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection. arXiv preprint arXiv:1609.03020.

Hansen, S. S., Larsen, T. M. T., Stevanovic, M., & Pedersen, J. M. (2016, February). An approach for detection and family classification of malware based on behavioral analysis. In Computing, Networking and Communications (ICNC), 2016 International Conference on (pp. 1-5). IEEE.

Ceron, J. M., Margi, C. B., & Granville, L. Z. (2016, June). MARS: An SDN-based malware analysis solution. In Computers and Communication (ISCC), 2016 IEEE Symposium on (pp. 525-530). IEEE.

Grini, L. S., Shalaginov, A., & Franke, K. (2016). Study of soft computing methods for large-scale multinomial malware types and families detection. In Proceedings of the The 6th World Conference on Soft Computing.

Martín, A., Menéndez, H. D., & Camacho, D. (2016). MOCDroid: multi-objective evolutionary classifier for Android malware detection. Soft Computing, 1-11.

Meng, G., Xue, Y., Mahinthan, C., Narayanan, A., Liu, Y., Zhang, J., & Chen, T. (2016, May). Mystique: Evolving Android Malware for Auditing Anti-Malware Tools. In Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security (pp. 365-376). ACM.

Kazdagli, M., Huang, L., Reddi, V., & Tiwari, M. (2016). EMMA: A New Platform to Evaluate Hardware-based Mobile Malware Analyses. arXiv preprint arXiv:1603.03086.

Huang, K., Han, J., Chen, S., & Feng, Z. (2016, October). A Skewness-Based Framework for Mobile App Permission Recommendation and Risk Evaluation. In International Conference on Service-Oriented Computing (pp. 252-266). Springer International Publishing.

Lim, J., & Yi, J. H. (2016). Structural analysis of packing schemes for extracting hidden codes in mobile malware. EURASIP Journal on Wireless Communications and Networking, 2016(1), 221.

Bhattacharya, S., Menéndez, H. D., Barr, E., & Clark, D. (2016). ITect: Scalable Information Theoretic Similarity for Malware Detection. arXiv preprint arXiv:1609.02404.

Truong, D. T., & Cheng, G. (2016). Detecting domain‐flux botnet based on DNS traffic features in managed network. Security and Communication Networks, 9(14), 2338-2347.

Kolosnjaji, B., Zarras, A., Webster, G., & Eckert, C. (2016, December). Deep Learning for Classification of Malware System Call Sequences. In Australasian Joint Conference on Artificial Intelligence (pp. 137-149). Springer International Publishing.

Mosli, R., Li, R., Yuan, B., & Pan, Y. (2016, May). Automated malware detection using artifacts in forensic memory images. In Technologies for Homeland Security (HST), 2016 IEEE Symposium on (pp. 1-6). IEEE.

Cybenko, G., Stocco, G., & Sweeney, P. (2016). Quantifying Covertness in Deceptive Cyber Operations. Cyber Deception, 53-69.

Awan, S., & Saqib, N. A. (2016). Detection of Malicious Executables Using Static and Dynamic Features of Portable Executable (PE) File. In Security, Privacy and Anonymity in Computation, Communication and Storage: SpaCCS 2016 International Workshops, TrustData, TSP, NOPE, DependSys, BigDataSPT, and WCSSC, Zhangjiajie, China, November 16-18, 2016, Proceedings 9 (pp. 48-58). Springer International Publishing.

Allen, J. L. (2016). pDroid. Master's Thesis, University of Tennessee.

Fan, M., Liu, J., Luo, X., Chen, K., Chen, T., Tian, Z., ... & Liu, T. (2016, October). Frequent Subgraph Based Familial Classification of Android Malware. In Software Reliability Engineering (ISSRE), 2016 IEEE 27th International Symposium on (pp. 24-35). IEEE.

Kumara, M. A., & Jaidhar, C. D. (2016, December). VMI Based Automated Real-Time Malware Detector for Virtualized Cloud Environment. In International Conference on Security, Privacy, and Applied Cryptography Engineering (pp. 281-300). Springer International Publishing.

Xu, L. (2016). Android malware classification using parallelized machine learning methods. Doctoral dissertation, University of Delaware.

Eckert, C. (2016, June). Adaptive Semantics-Aware Malware Classification. In Detection of Intrusions and Malware, and Vulnerability Assessment: 13th International Conference, DIMVA 2016, San Sebastián, Spain, July 7-8, 2016, Proceedings (Vol. 9721, p. 419). Springer.

Raff, E., Zak, R., Cox, R., Sylvester, J., Yacci, P., Ward, R., ... & Nicholas, C. (2016). An investigation of byte n-gram features for malware classification. Journal of Computer Virology and Hacking Techniques, 1-20.

Faruki, P., Zemmari, A., Gaur, M. S., Laxmi, V., & Conti, M. (2016, June). MimeoDroid: Large Scale Dynamic App analysis on Cloned Devices via Machine Learning Classifiers. In Dependable Systems and Networks Workshop, 2016 46th Annual IEEE/IFIP International Conference on (pp. 60-65). IEEE.

Han, J., Feng, Z., Chen, S., & Huang, K. (2016, June). A Framework for Permission Recommendation and Risk Evaluation Based on Skewness-Based Filtering. In Services Computing (SCC), 2016 IEEE International Conference on (pp. 774-777). IEEE.

Bedford, A., Garvin, S., Desharnais, J., Tawbi, N., Ajakan, H., Audet, F., & Lebel, B. (2016, October). Andrana: Quick and Accurate Malware Detection for Android. In International Symposium on Foundations and Practice of Security (pp. 20-35). Springer, Cham.

Sinha, L., Bhandari, S., Faruki, P., Gaur, M. S., Laxmi, V., & Conti, M. (2016, January). FlowMine: Android app analysis via data flow. In Consumer Communications & Networking Conference (CCNC), 2016 13th IEEE Annual (pp. 435-441). IEEE.

Kazdagli, M., Reddi, V. J., & Tiwari, M. (2016, October). Quantifying and improving the efficiency of hardware-based mobile malware detectors. In Microarchitecture (MICRO), 2016 49th Annual IEEE/ACM International Symposium on (pp. 1-13). IEEE.

Nataraj, L., & Manjunath, B. S. (2016). SPAM: Signal Processing to Analyze Malware [Applications Corner]. IEEE Signal Processing Magazine, 33(2), 105-117.

Mohsen, F., & Shehab, M. (2016, November). The Listening Patterns to System Events by Benign and Malicious Android Apps. In Collaboration and Internet Computing (CIC), 2016 IEEE 2nd International Conference on (pp. 546-553). IEEE.

Dara, S., & Muralidhara, V. N. (2016). Privacy Preserving Architectures for Collaborative Intrusion Detection. arXiv preprint arXiv:1602.02452.

Banin, S., Shalaginov, A., & Franke, K. (2016). Memory access patterns for malware detection. Norsk informasjonssikkerhetskonferanse (NISK), 96-107.

Wang, G., Ray, I., Calero, J. M. A., & Thampi, S. M. (Eds.). (2016). Security, Privacy and Anonymity in Computation, Communication and Storage: SpaCCS 2016 International Workshops, TrustData, TSP, NOPE, DependSys, BigDataSPT, and WCSSC, Zhangjiajie, China, November 16-18, 2016, Proceedings.

Garcia, J., Hammad, M., & Malek, S. (2016). Lightweight, Obfuscation-Resilient Detection and Family Identification of Android Malware. Institute for Software Research.

Mariconti, E., Onaolapo, J., Ross, G., & Stringhini, G. (2016, August). What's your major threat? On the differences between the network behavior of targeted and commodity malware. In Availability, Reliability and Security (ARES), 2016 11th International Conference on (pp. 599-608). IEEE.

Mariconti, E., Onwuzurike, L., Andriotis, P., De Cristofaro, E., Ross, G., & Stringhini, G. (2016). MAMADROID: Detecting Android Malware by Building Markov Chains of Behavioral Models. arXiv preprint arXiv:1612.04433.

Zhang, Y., Tan, T., Li, Y., & Xue, J. (2016). Ripple: Reflection Analysis for Android Apps in Incomplete Information Environments. arXiv preprint arXiv:1612.05343.

Xu, L., Zhang, D., Alvarez, M. A., Morales, J. A., Ma, X., & Cavazos, J. (2016, June). Dynamic Android Malware Classification Using Graph-Based Representations. In Cyber Security and Cloud Computing (CSCloud), 2016 IEEE 3rd International Conference on (pp. 220-231). IEEE.

Ping, M., Alsulami, B., & Mancoridis, S. On the Effectiveness of Application Characteristics in the Automatic Classification of Malware on Smartphones. Drexel University

Pektaş, A., Çavdar, M., & Acarman, T. (2016, October). Android Malware Classification by Applying Online Machine Learning. In International Symposium on Computer and Information Sciences (pp. 72-80). Springer International Publishing.

Gandotra, E., Bansal, D., & Sofat, S. (2016). Malware Threat Assessment Using Fuzzy Logic Paradigm. Cybernetics and Systems, 1-20.

Jang, J. W., Yun, J., Mohaisen, A., Woo, J., & Kim, H. K. (2016). Andro-profiler: Detecting and Classifying Android Malware based on Behavioral Profiles. arXiv preprint arXiv:1606.01403.

Pagani, F., De Astis, M., Graziano, M., Lanzi, A., & Balzarotti, D. (2016, June). Measuring the Role of Greylisting and Nolisting in Fighting Spam. In Dependable Systems and Networks (DSN), 2016 46th Annual IEEE/IFIP International Conference on (pp. 562-571). IEEE.

Sadeghi, A., Bagheri, H., & Garcia, J. (2016). A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Software. IEEE Transactions on Software Engineering.

Shalaginov, A., Grini, L. S., & Franke, K. (2016, July). Understanding Neuro-Fuzzy on a class of multinomial malware detection problems. In Neural Networks (IJCNN), 2016 International Joint Conference on (pp. 684-691). IEEE.

Tian, K., Yao, D., Ryder, B. G., & Tan, G. (2016, May). Analysis of Code Heterogeneity for High-Precision Classification of Repackaged Malware. In Security and Privacy Workshops (SPW), 2016 IEEE (pp. 262-271). IEEE.

Sadeghi, A., Bagheri, H., Garcia, J., & Malek, S. (2016). A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Apps. Institute for Software Research Technical Report #UCI-ISR-16-1.

黄梅根, & 曾云科 [Meigen, H & Yunke, Z]. (2016). 基于权限组合的 Android 窃取隐私恶意应用检测方法 [Detection Method for Android Malware of Privacy Stealing Based on Permissions Combination]. 计算机应用与软件 [Computer Applications and Software], 33(9), 320-323.

水野翔, 畑田充弘, 森達哉, & 後藤滋樹 [Mizuno, S., Hatada, M., Mori, T., & Goto, S.]. (2016). HTTP ヘッダフィールドの可変性に基づくマルウェア感染端末の特定 [Identification of Malware-Infected Terminals Based on the Variability of HTTP Header Fields]. コンピュータセキュリティシンポジウム 2016 論文集 [Computer Security Symposium 2016 Proceedings], 2016(2), 632-639.

Cho, T., Kim, H., Lee, J., Jung, M., & Yi, J. H. (2016). API 특성 정보기반 악성 애플리케이션 식별 기법 [A Scheme for Identifying Malicious Applications Based on API Characteristics]. Journal of The Korea Institute of Information Security & Cryptology, 26(1).

吉村豪康, 橋本正樹, 辻秀典, & 田中英彦 [Akiyasu, Y., Masaki, H., Hidenori, T., & Hidehiko, T.]. (2016). TOMOYO Linux を用いた Linux マルウェアの動的解析環境構築に向けた初期的検討 [Initial Study for Building Dynamic Environment of Linux Malware Using TOMOYO Linux]. コンピュータセキュリティシンポジウム 2016 論文集 [Computer Security Symposium 2016 Proceedings], 2016(2), 518-525.

پارسا, سیفی, & علائیان. (2016). ارائه یک رهیافت جدید مبتنی بر گراف وابستگی بین فراخوانی های سیستمی برای استخراج الگوهای رفتاری مخرب. مجله پدافند الکترونیکی و سایبری, 4(3).‎
[Parsa, Saifi, & Layyan. (2016). Providing a new approach based on the dependency graph system calls for the extraction of destructive behavior patterns. Journal of electronic and cyber defense, 4(3).]

黄梅根, & 曾云科 [Huang, M., & Zend, Y.]. (2016). 基于离群点检测算法的窃取隐私应用检测方法的研究. [Study on Detection of Stealing Privacy Based on Outlier Detection Algorithm.]

Dimotikalis, P. (2016). Memory Forensics and Bitcoin mining malware. Master's Thesis, International Hellenic University.

 
2015
 
Nataraj, L. (2015). A Signal Processing Approach To Malware Analysis. University of California, Santa Barbara.

Yang, W., Xiao, X., Andow, B., Li, S., Xie, T., & Enck, W. (2015, May). Appcontext: Differentiating malicious and benign mobile app behaviors using context. In Software Engineering (ICSE), 2015 IEEE/ACM 37th IEEE International Conference on (Vol. 1, pp. 303-313). IEEE.

Avdiienko, V., Kuznetsov, K., Gorla, A., Zeller, A., Arzt, S., Rasthofer, S., & Bodden, E. (2015, May). Mining apps for abnormal usage of sensitive data. In Proceedings of the 37th International Conference on Software Engineering-Volume 1 (pp. 426-436). IEEE Press.

Yadegari, B., Johannesmeyer, B., Whitely, B., & Debray, S. (2015, May). A generic approach to automatic deobfuscation of executable code. In Security and Privacy (SP), 2015 IEEE Symposium on (pp. 674-691). IEEE.

Elish, K. O., Shu, X., Yao, D. D., Ryder, B. G., & Jiang, X. (2015). Profiling user-trigger dependence for Android malware detection. Computers & Security, 49, 255-273.

Kang, H., Jang, J. W., Mohaisen, A., & Kim, H. K. (2015). Detecting and classifying android malware using static analysis along with creator information. International Journal of Distributed Sensor Networks.

Afonso, V. M., de Amorim, M. F., Grégio, A. R. A., Junquera, G. B., & de Geus, P. L. (2015). Identifying Android malware using dynamically obtained features. Journal of Computer Virology and Hacking Techniques, 11(1), 9-17.

Rasthofer, S., Arzt, S., Miltenberger, M., & Bodden, E. (2015). Harvesting runtime data in android applications for identifying malware and enhancing code analysis.

Jang, J. W., Kang, H., Woo, J., Mohaisen, A., & Kim, H. K. (2015). Andro-AutoPsy: anti-malware system based on similarity matching of malware and malware creator-centric information. Digital Investigation, 14, 17-35.

Li, Y., Sundaramurthy, S. C., Bardas, A. G., Ou, X., Caragea, D., Hu, X., & Jang, J. (2015, August). Experimental study of fuzzy hashing in malware clustering analysis. In 8th workshop on cyber security experimentation and test (cset 15) (Vol. 5, No. 1, p. 52). USENIX Association.

Kotzias, P., Matic, S., Rivera, R., & Caballero, J. (2015, October). Certified PUP: abuse in authenticode code signing. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (pp. 465-478). ACM.

Bhandari, S., Gupta, R., Laxmi, V., Gaur, M. S., Zemmari, A., & Anikeev, M. (2015, September). DRACO: DRoid analyst combo an android malware analysis framework. In Proceedings of the 8th International Conference on Security of Information and Networks (pp. 283-289). ACM.

Shijo, P. V., & Salim, A. (2015). Integrated static and dynamic analysis for malware detection. Procedia Computer Science, 46, 804-811.

Pirscoveanu, R. S., Hansen, S. S., Larsen, T. M., Stevanovic, M., Pedersen, J. M., & Czech, A. (2015, June). Analysis of malware behavior: Type classification using machine learning. In Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), 2015 International Conference on (pp. 1-7). IEEE.

Rasthofer, S., Arzt, S., Kolhagen, M., Pfretzschner, B., Huber, S., Bodden, E., & Richter, P. (2015, July). Droidsearch: A tool for scaling android app triage to real-world app stores. In Science and Information Conference (SAI), 2015 (pp. 247-256). IEEE.

Naval, S., Laxmi, V., Rajarajan, M., Gaur, M. S., & Conti, M. (2015). Employing program semantics for malware detection. IEEE Transactions on Information Forensics and Security, 10(12), 2591-2604.

Garcia, J., Hammad, M., Pedrood, B., Bagheri-Khaligh, A., & Malek, S. (2015). Obfuscation-resilient, efficient, and accurate detection and family identification of android malware. Department of Computer Science, George Mason University, Technical Report

Gheorghe, L., Marin, B., Gibson, G., Mogosanu, L., Deaconescu, R., Voiculescu, V. G., & Carabas, M. (2015). Smart malware detection on Android. Security and Communication Networks, 8(18), 4254-4272.

Li, Y., Shen, T., Sun, X., Pan, X., & Mao, B. (2015, October). Detection, Classification and Characterization of Android Malware Using API Data Dependency. In International Conference on Security and Privacy in Communication Systems (pp. 23-40). Springer International Publishing.

Zakeri, M., Faraji Daneshgar, F., & Abbaspour, M. (2015). A static heuristic approach to detecting malware targets. Security and Communication Networks, 8(17), 3015-3027.

Bekerman, D., Shapira, B., Rokach, L., & Bar, A. (2015, September). Unknown malware detection using network traffic classification. In Communications and Network Security (CNS), 2015 IEEE Conference on (pp. 134-142). IEEE.

Jang, J. W., Woo, J., Mohaisen, A., Yun, J., & Kim, H. K. (2015). Mal-Netminer: malware classification approach based on social network analysis of system call graph. Mathematical Problems in Engineering, 2015.

Pektaş, A., Acarman, T., Falcone, Y., & Fernandez, J. C. (2015, October). Runtime-behavior based malware classification using online machine learning. In Internet Security (WorldCIS), 2015 World Congress on (pp. 166-171). IEEE.

Kim, J., Kim, T. G., & Im, E. G. (2015, October). Structural information based malicious app similarity calculation and clustering. In Proceedings of the 2015 Conference on research in adaptive and convergent systems (pp. 314-318). ACM.

Marengereke, T. M., & Sornalakshmi, K. (2015). Automated Malicious Android App Detection using Machine Learning Methods. International Journal of Engineering Development and Research.

Rowe, N. C. (2015, July). Finding contextual clues to malware using a large corpus. In Computers and Communication (ISCC), 2015 IEEE Symposium on (pp. 229-236). IEEE.

Tu, T. D., Guang, C., & Xin, L. Y. (2015, December). Detecting bot-infected machines based on analyzing the similar periodic DNS queries. In Communications, Management and Telecommunications (ComManTel), 2015 International Conference on (pp. 35-40). IEEE.

Subramaniam, L. V. Advanced Big Data Management and Analytics for Ubiquitous Sensors. International Journal of Distributed Sensor Networks

Rowe, N. C. (2015). Identifying Forensically Uninteresting Files Using a Large Corpus. EAI Endorsed Transactions on Security and Safety.

Svajcer, V. Building A Malware Lab in the Age of Big Data. Virus Bulletin Conference September 2015

Alaeiyan, M. H., & Parsa, S. (2015, November). Automatic loop detection in the Sequence of system calls. In Knowledge-Based Engineering and Innovation (KBEI), 2015 2nd International Conference on (pp. 720-723). IEEE.

Filiol, E., & Irolla, P. (2015). (In)Security of Mobile Banking...and of Other Mobile Apps ESIEA-Laboratoire de virologie et de cryptologie opérationnelles.

Nguyen, N. T., Le, B. C., Le, D. T., & Van Le, T. H. (2015). A New Method of Virus Detection Based on Maximum Entropy Model. Advanced Computational Methods for Knowledge Engineering, 151-161.

Glendowne, D., Miller, C., McGrew, W., & Dampier, D. (2015, January). CHARACTERISTICS OF MALICIOUS DLLS IN WINDOWS MEMORY. In IFIP International Conference on Digital Forensics (pp. 149-161). Springer International Publishing.

Kuo, C. C., Hsu, K. C., & Yang, C. S. (2015). A novel way to generate the rule patterns for detecting malicious PDF documents. Proceedings of the Asia-Pacific Advanced Network, 40, 43-49.

Tao, L., Zhushow, T., & Beijun, S. (2015). Automatically Detecting Privacy Leaks of Android Applications. Computer Applications and Software, 32(3), 297-301.

Kim, J. H., Park, J. K., & Im, E. (2015). Method Structural information based malicious Android application Similarity comparison method. Proceedings of the Korean Information Science Society, 125-127.

Monte Afonso, V. M., de Amorim, M. F., Grégio, A. R. A., & de Geus, P. L. Identifying Android malware using dynamically obtained features Anais do X Workshop de Teses, Dissertações e Trabalhos de Iniciação Científica em Andamento do IC-Unicamp.

許博學, & 陳奎研 [XU B., & CHEN, K.]. (2015). Android 惡意軟體偵查之研究 [Android Malicious Software Detection Research]. 全球商業經營管理學報 [Journal of Global Business Management], (7), 123-133.

张文梅 [Zhang, W.]. (2015). 基于 ZigBee 的家居监控系统设计 [ZigBee-based home monitoring system design]. 计算机应用与软件 [Computer Applications and Software], 32(3), 313-316.

Chan, K. W. (2015). Research on unsupervised point-to-point botnet detection based on session. Graduate School of Computer and Communication Engineering, 1-40.

Hägle, J. (2015). Utvärdering av metod för applikationsgranskning [Evaluation of the method of application review]. Linköping University, Thesis.

 
2014
 
Naval, S. (2014). Behavior-Based Dynamic Malware Detection Techniques.. Doctoral dissertation, MNIT Jaipur.

Arzt, S., Rasthofer, S., Fritz, C., Bodden, E., Bartel, A., Klein, J., ... & McDaniel, P. (2014). Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps. Acm Sigplan Notices, 49(6), 259-269.

Rasthofer, S., Arzt, S., & Bodden, E. (2014, February). A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks. In NDSS.

Neuner, S., Van der Veen, V., Lindorfer, M., Huber, M., Merzdovnik, G., Mulazzani, M., & Weippl, E. (2014). Enter sandbox: Android sandbox comparison. arXiv preprint arXiv:1410.7749.

Lindorfer, M., Volanis, S., Sisto, A., Neugschwandtner, M., Athanasopoulos, E., Maggi, F., ... & Ioannidis, S. (2014, July). AndRadar: fast discovery of android applications in alternative markets. In International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (pp. 51-71). Springer International Publishing.

Wang, W., Wang, X., Feng, D., Liu, J., Han, Z., & Zhang, X. (2014). Exploring permission-induced risk in android applications for malicious application detection. IEEE Transactions on Information Forensics and Security, 9(11), 1869-1882.

Nappa, A., Xu, Z., Rafique, M. Z., Caballero, J., & Gu, G. (2014, February). Cyberprobe: Towards internet-scale active detection of malicious servers. In Proceedings of the 2014 Network and Distributed System Security Symposium (NDSS 2014) (pp. 1-15).

Jang, J. W., Yun, J., Woo, J., & Kim, H. K. (2014, April). Andro-profiler: anti-malware system based on behavior profiling of mobile malware. In Proceedings of the 23rd International Conference on World Wide Web (pp. 737-738). ACM.

Wolfe, B., Elish, K. O., & Yao, D. D. (2014, October). Comprehensive behavior profiling for proactive Android malware detection. In International Conference on Information Security (pp. 328-344). Springer International Publishing.

Faruki, P., Bharmal, A., Laxmi, V., Gaur, M. S., Conti, M., & Rajarajan, M. (2014, September). Evaluation of android anti-malware techniques against dalvik bytecode obfuscation. In Trust, Security and Privacy in Computing and Communications (TrustCom), 2014 IEEE 13th International Conference on (pp. 414-421). IEEE.

Wolfe, B., Elish, K., & Yao, D. (2014, December). High precision screening for Android malware with dimensionality reduction. In Machine Learning and Applications (ICMLA), 2014 13th International Conference on (pp. 21-28). IEEE.

Suarez-Tangil, G., Tapiador, J. E., & Peris-Lopez, P. (2014, December). Stegomalware: Playing hide and seek with malicious components in smartphone apps. In International Conference on Information Security and Cryptology (pp. 496-515). Springer International Publishing.

Alzahrani, A. J., Stakhanova, N., Ali, H. G., & Ghorbani, A. (2014). Characterizing Evaluation Practicesof Intrusion Detection Methodsfor Smartphones. Journal of Cyber Security and Mobility, 3(2), 89-132.

Gonzalez, H., Stakhanova, N., & Ghorbani, A. A. (2014, September). Droidkin: Lightweight detection of android apps similarity. In International Conference on Security and Privacy in Communication Systems (pp. 436-453). Springer International Publishing.

Jang, J. W., Woo, J., Yun, J., & Kim, H. K. (2014, April). Mal-netminer: malware classification based on social network analysis of call graph. In Proceedings of the 23rd International Conference on World Wide Web (pp. 731-734). ACM.

Musavi, S. A., & Kharrazi, M. (2014). Back to static analysis for kernel-level rootkit detection. IEEE Transactions on Information Forensics and Security, 9(9), 1465-1476.

Kazdagli, M., Huang, L., Reddi, V., & Tiwari, M. (2014, June). Morpheus: Benchmarking computational diversity in mobile malware. In Proceedings of the Third Workshop on Hardware and Architectural Support for Security and Privacy (p. 3). ACM.

Kang, H. J., Jang, J. W., Mohaisen, A., & Kim, H. K. (2014). Androtracker: Creator information based android malware classification system. In Information Security Applications-15th International Workshop, WISA (Vol. 8909).

Ghezelbigloo, Z., & VafaeiJahan, M. (2014, November). Role-opcode vs. opcode: The new method in computer malware detection. In Technology, Communication and Knowledge (ICTCK), 2014 International Congress on (pp. 1-6). IEEE.

Yun, J. S., Jang, J. W., & Kim, H. K. (2014). Andro-profiler: Anti-malware system based on behavior profiling of mobile malware. Journal of the Korea Institute of Information Security and Cryptology, 24(1), 145-154.

Ng, D. V., & Hwang, J. I. G. (2014, July). Android malware detection using the dendritic cell algorithm. In Machine Learning and Cybernetics (ICMLC), 2014 International Conference on (Vol. 1, pp. 257-262). IEEE.

van Lenthe, J. M. (2014). Combining Multiple Malware Detection Approaches for Achieving Higher Accuracy.

Severyn, S. N. (2014). Adapting Linguistic Deception Cues for Malware Detection.

McGrew, R. W. (2014). Instrumenting Point-of-Sale Malware.

BARTL, B. V. (2014). A Client Honeypot. Master's Thesis, Masarykova Univerzita, Fakulta Informatiky

Kumar, A., & Aghila, G. (2014, November). Portable executable scoring: What is your malicious score? In Science Engineering and Management Research (ICSEMR), 2014 International Conference on (pp. 1-5). IEEE.

Afonso, V. M., de Amorim, M. F., Ricardo, A., Grégio, A., Junquera, G. B., & de Geus, P. L. Identifying Android malware using dynamically obtained.

Shabani, N., & Jahan, M. V. (2014, November). Metamorphic virus detection based on Bayesian network. In Technology, Communication and Knowledge (ICTCK), 2014 International Congress on (pp. 1-8). IEEE.

Yun, J. S., Jang, J. W. & Kim, H. K. (2014). Andro-profiler: Anti-malware system based on behavior profiling of mobile malware. Journal of The Korea Institute of Information Security & Cryptology, 24(1).

武田圭史, & 露木航平 [Takeda, K., & Tsuyuki, K.]. (2014). 実行ファイルのプロファイリングによる調査対象ファイル特定手法の提案 [A Proposal for Techniques to Identify Files to Investigate by Executable File Profiling]. コンピュータセキュリティシンポジウム 2014 論文集 [Computer Security Symposium 2014 Proceedings], 2014(2), 519-526.

 
2013
 
Oktavianto, D., & Muhardianto, I. (2013). Cuckoo Malware Analysis. Packt Publishing Ltd.

Guri, M., Kedma, G., Sela, T., Carmeli, B., Rosner, A., & Elovici, Y. (2013, October). Noninvasive detection of anti-forensic malware. In Malicious and Unwanted Software:" The Americas"(MALWARE), 2013 8th International Conference on (pp. 1-10). IEEE.

Pareek, H., Eswari, P. R. L., & Babu, N. S. C. (2013). Malicious PDF document detection based on feature extraction and entropy. International Journal Journal of Security, Privacy and Trust Management, 2(5).

Dolmans, R., & Katz, W. (2013). RP1: Carberp Malware analysis. University of Amsterdam.

Pareek, H., Eswari, P. R. L., & Babu, S. C. (2013). APPBACS: AN APPLICATION BEHAVIOR ANALYSIS AND CLASSIFICATION SYSTEM. International Journal of Computer Science & Information Technology, 5(2), 53.

Hudel, C., & Shehab, M. (2013, December). Optimizing search for malware by hashing smaller amounts of data. In Internet Security (WorldCIS), 2013 World Congress on (pp. 112-117). IEEE.