A Comprehensive Survey on Malware Detection Techniques
Article No.: 4, Pages 1 - 6
Abstract
Malware, malicious software designed to infiltrate and compromise computer systems, poses an ever-growing threat in today's interconnected world. This comprehensive review delves into the diverse landscape of malware detection techniques. We categorize and analyze these techniques. Each approach's strengths, weaknesses, and real-world applications are thoroughly examined. Evaluation metrics crucial to assessing detection effectiveness are elucidated. This paper aims to provide a comprehensive overview of the challenges in malware detection techniques face in protecting cyberspace against attacks, by presenting a literature on such emerging techniques for cyber security. It also provides brief descriptions of each techniques method. It finally discusses the challenges of using these techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of malware detection.
References
[1]
A. Bijalwan, Network Forensics: Privacy and Security, Oxon, UK: CRC Press, Dec 2021.
[2]
K. Vala, “A Review on Fileless Malware Analysis Techniques,” International Journal of Engineering Research & Technology (IJERT), vol. 9, no. May-2020, pp. 46-49, 2020.
[3]
S. Rami, O. Khairuddin and K. A. Z. Ariffin, “Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis,” International Journal on Advance Science Engineering Information Technology, vol. 8, pp. 2088-5334, 2018.
[4]
Aslan and . R. Samet, “A Comprehensive Review on Malware Detection Approaches,” IEEE Access, vol. 8, pp. 6249 - 6271, 2020 .
[5]
G. Jingjing, S. Binglin, D. Xiaojiang, W. Jun, Z. Yi and W. Ziwang, “Consortium Blockchain-Based Malware,” IEEE Access, vol. 6, pp. 12118 - 12128, 2018.
[6]
H. KyoungSoo, K. BooJoong and I. Eul Gyu, “Malware Analysis Using Visualized Image Matrices,” The Scientific World Journal, vol. 2014, pp. 1-15, 2014.
[7]
A. Venkatesan, “Code Obfuscation and Virus Detection,” 2008.
[8]
A. Moser, C. Kruegel and E. Kirda, “Limits of Static Analysis for Malware Detection,” in Twenty-Third Annual Computer Security Applications Conference, Miami Beach, FL, USA, 2007.
[9]
E. M. Alkhateeb, “Dynamic Malware Detection using API Similarity,” in IEEE International Conference on Computer and Information Technology, 2017.
[10]
Z. Bazrafshan, H. Hashemi, S. M. H. Fard and H. Ali, “A survey on heuristic malware detection techniques,” in The 5th Conference on Information and Knowledge Technology, Shiraz, Iran, 2013.
[11]
M. G. Schultz, E. Eleazar and F. Zadok, “Data Mining Methods for Detection of New Malicious Executables,” in Security and Privacy, 2001.
[12]
F. Song and T. Touili, “LTL Model-Checking for Malware Detection,” in International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Paris Diderot, France, 2013.
[13]
D. S. Berman, A. L. Buczak, J. S. Chavis and C. L. Corbett, “A Survey of Deep Learning Methods for Cyber Security,” MDPI, Vols. 10(4), 122, 2019.
[14]
Ö. Aslan, M. Ozkan-Okay and D. Gupta, “Review of Cloud-Based Malware Detection System:Opportunities, Advances and Challenges,” EJERS, European Journal of Engineering and Technology Research, vol. 6, no. 3, pp. 1-8, 2021.
[15]
S. S. Hatem, M. H. Wafy and M. M. El-Khouly, “Malware Detection in Cloud Computing,” (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 5, no. 4, pp. 187-192, 2014.
[16]
T. Isohara, K. Takemori and A. Kubota, “Kernel-based Behavior Analysis for Android Malware Detection,” in 2011 Seventh International Conference on Computational Intelligence and Security, Sanya, China, 2011.
[17]
J. Gu, B. Sun, X. Du, J. Wang, Y. Zhuang and Z. Wang, “Consortium Blockchain-Based Malware Detection in Mobile Devices,” Special Section on Research Challenges and Opportuinites in Security, vol. 6, pp. 12218-12128, 2017.
[18]
A. Efe and S. Hussin, “Malware Visualization Techniques,” International Journal of Applied Mathematics Electronics and Computers, vol. 8, no. 1, pp. 7-20, 2020.
[19]
A. Makandar and A. Patrot, “Malware Class Recognition Using Image Processing Techniques,” in International Conference on Data Management, Analytics and Innovation (ICDMAI), Pune, India, 2017.
[20]
M. Christodorescu and S. Jha, “Static Analysis of Executables to Detect Malicious Patterns,” in Proceeding of USENIX Security Symposium., Bellevue, WA, 2005.
[21]
K. R. Jidigam, T. H. Austin and M. Stamp, “Singular value decomposition and metamorphic detection,” Journal of Computer Virology and Hacking Techniques, vol. 11, pp. 2013-216, 2015.
[22]
C. Annachhatre, T. H. Austin and M. Stamp, “Hidden Markov models for malware classification,” Journal of Computer Virology and Hacking Techniques, vol. 11, p. 59–73, 2015.
[23]
C. Kolbitsch, . P. M. Comparetti, C. Kruegel, E. Kirda, X. Zhou and X. Wang, “Effective and efficient malware detection at the end host.,” In USENIX security symposium, vol. 4, no. 1, pp. 351-366, 2009.
[24]
W. Arnold and G. Tesauro, “Automatically generated Win32 heuristic virus detection,” in In Proceedings of the 2000 international virus bulletin conference, 2000.
[25]
Y. Ye, T. Li, Q. Jiang and Y. Wang, “CIMDS: Adapting Postprocessing Techniques of Associative Classification for Malware Detection,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 3, pp. 298 - 307, 2010.
[26]
A. Ojugo and A. O. Eboka, “Signature-based malware detection using approximate Boyer Moore string matching algorithm,” International Journal of Mathematical Sciences and Computing, vol. 5, no. 3, pp. 49-62, 2019.
[27]
F. Song and T. Touili, “Efficient Malware Detection Using Model-Checking,” International Symposium on Formal Methods, vol. 7436, p. 418–433, 2012.
[28]
Y. Ye, T. Li, S. Zhu, W. Zhuang, E. Tas, U. Gupta and M. Abdulhayoglu, “Combining file content and file relations for cloud based malware detection,” in In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '11), New York USA, 2011.
[29]
R. Mosli, R. Li, B. Yuan and Y. Pan, “Automated malware detection using artifacts in forensic memory images,” in IEEE Symposium on Technologies for Homeland Security (HST), Waltham, MA, USA, 2016.
[30]
R. Mosli, . R. Li, . B. Yuan and Y. Pan, “A Behavior-Based Approach for Malware Detection,” in IFIP Advances in Information and Communication Technology, 2017.
[31]
T. Isohara, K. Takemori and A. Kubota, “Kernel-based Behavior Analysis for Android Malware Detection,” in Seventh International Conference on Computational Intelligence and Security, Sanya, China, 2011.
[32]
A. Narayanan, . M. Chandramohan, . L. Chen and Y. Liu, “A multi-view context-aware approach to Android malware detection and malicious code localization,” Empirical Software Engineering, vol. 23, p. 1222–1274, 2018.
[33]
A. Azmoodeh, A. Dehghantanha, M. Conti, K. Kwang and R. Choo, “Detecting crypto-ransomware in IoT networks based on energy consumption footprint,” Journal of Ambient Intelligence and Humanized Computing, vol. 9, pp. 114-1152, 2018.
[34]
Venkatraman, M. Alazab and R. Vinayakumar, “A hybrid deep learning image-based analysis for effective malware detection,” Journal of Information Security and Applications, vol. 47, pp. 377-389, 2019.
[35]
R. Fuji, S. Usuzaki, K. Aburada, H. Yamaba, . T. Katayama, M. Park, . N. Shiratori and N. Okazaki, “Blockchain-Based Malware Detection Method Using Shared Signatures of Suspected Malware Files,” International Conference on Network-Based Information Systems, vol. 1036, p. 305–316, 2019.
Index Terms
- A Comprehensive Survey on Malware Detection Techniques
Recommendations
Malware Analysis Types & Techniques : A Survey
ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine IntelligenceMalware is like a digital troublemaker. It's a type of software that sneaks into computers and gadgets with harmful objectives. It can cause problems by stealing in- formation, damaging files, or making devices act strangely without the owner knowing. ...
A comprehensive survey on deep learning based malware detection techniques
AbstractRecent theoretical and practical studies have revealed that malware is one of the most harmful threats to the digital world. Malware mitigation techniques have evolved over the years to ensure security. Earlier, several classical ...
A Survey on Malware Detection Using Data Mining Techniques
In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed serious and evolving security threats to Internet users. To protect legitimate users from these threats, anti-malware software products from different companies, ...
Comments
Information & Contributors
Information
Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 94Total Downloads
- Downloads (Last 12 months)94
- Downloads (Last 6 weeks)9
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format