Malware Detection and Analysis: Challenges and Research Opportunities

Malwares are continuously growing in sophistication and numbers. Over the last decade, remarkable progress has been achieved in anti-malware mechanisms. However, several pressing issues (e.g., unknown malware samples detection) still need to be addressed adequately. This article first presents a concise overview of malware along with anti-malware and then summarizes various research challenges. This is a theoretical and perspective article that is hoped to complement earlier articles and works.

[1]  Xin Li,et al.  DeepAM: a heterogeneous deep learning framework for intelligent malware detection , 2018, Knowledge and Information Systems.

[2]  Di Wu,et al.  DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data , 2017, 2017 IEEE Symposium on Computers and Communications (ISCC).

[3]  Qiguang Miao,et al.  Malware detection using bilayer behavior abstraction and improved one-class support vector machines , 2015, International Journal of Information Security.

[4]  Rahil Hosseini,et al.  A state-of-the-art survey of malware detection approaches using data mining techniques , 2018, Human-centric Computing and Information Sciences.

[5]  Igor Santos,et al.  OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection , 2012, CISIS/ICEUTE/SOCO Special Sessions.

[6]  Muttukrishnan Rajarajan,et al.  Android Security: A Survey of Issues, Malware Penetration, and Defenses , 2015, IEEE Communications Surveys & Tutorials.

[7]  Stavros D. Nikolopoulos,et al.  A graph-based model for malware detection and classification using system-call groups , 2017, Journal of Computer Virology and Hacking Techniques.

[8]  Lotfi Ben Romdhane,et al.  Minimal contrast frequent pattern mining for malware detection , 2016, Comput. Secur..

[9]  David Brumley,et al.  Blanket Execution: Dynamic Similarity Testing for Program Binaries and Components , 2014, USENIX Security Symposium.

[10]  Alexander Pretschner,et al.  Leveraging Compression-Based Graph Mining for Behavior-Based Malware Detection , 2019, IEEE Transactions on Dependable and Secure Computing.

[11]  Donald F. Towsley,et al.  Security importance assessment for system objects and malware detection , 2017, Comput. Secur..

[12]  Byung-Gon Chun,et al.  TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones , 2010, OSDI.

[13]  Yang Liu,et al.  A multi-view context-aware approach to Android malware detection and malicious code localization , 2017, Empirical Software Engineering.

[14]  Roberto Baldoni,et al.  Survey on the Usage of Machine Learning Techniques for Malware Analysis , 2017, ArXiv.

[15]  Salvatore J. Stolfo,et al.  Data mining methods for detection of new malicious executables , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.

[16]  Zheng Yan,et al.  A hybrid approach of mobile malware detection in Android , 2017, J. Parallel Distributed Comput..

[17]  David Camacho,et al.  MOCDroid: multi-objective evolutionary classifier for Android malware detection , 2017, Soft Comput..

[18]  Erdogan Dogdu,et al.  Malware classification using deep learning methods , 2018, ACM Southeast Regional Conference.

[19]  Sakir Sezer,et al.  DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection , 2019, IEEE Transactions on Cybernetics.