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Reseach Article

A Survey on Malware and Malware Detection Systems

by Imtithal A. Saeed, Ali Selamat, Ali M. A. Abuagoub
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 16
Year of Publication: 2013
Authors: Imtithal A. Saeed, Ali Selamat, Ali M. A. Abuagoub
10.5120/11480-7108

Imtithal A. Saeed, Ali Selamat, Ali M. A. Abuagoub . A Survey on Malware and Malware Detection Systems. International Journal of Computer Applications. 67, 16 ( April 2013), 25-31. DOI=10.5120/11480-7108

@article{ 10.5120/11480-7108,
author = { Imtithal A. Saeed, Ali Selamat, Ali M. A. Abuagoub },
title = { A Survey on Malware and Malware Detection Systems },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 16 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 25-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number16/11480-7108/ },
doi = { 10.5120/11480-7108 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:25:04.056157+05:30
%A Imtithal A. Saeed
%A Ali Selamat
%A Ali M. A. Abuagoub
%T A Survey on Malware and Malware Detection Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 16
%P 25-31
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Over the last decades, there were lots of studies made on malware and their countermeasures. The most recent reports emphasize that the invention of malicious software is rapidly increasing. Moreover, the intensive use of networks and Internet increases the ability of the spreading and the effectiveness of this kind of software. On the other hand, researchers and manufacturers making great efforts to produce anti-malware systems with effective detection methods for better protection on computers. In this paper, a detailed review has been conducted on the current situation of malware infection and the work done to improve anti-malware or malware detection systems. Thus, it provides an up-to-date comparative reference for developers of malware detection systems.

References
  1. Mcafee and Lab, 2013 Threats Predictions. 2013.
  2. Berkenkopf, R. B. S. , G Data Malware Report. 2010.
  3. Ye, Y. , et al. , Intelligent file scoring system for malware detection from the gray list, in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. 2009, ACM: Paris, France. p. 1385-1394.
  4. Rieck, K. , Malheur A novel tool for malware analysis 2012.
  5. Pinz, C. I. , et al. , Improving the security level of the FUSION@ multi-agent architecture. Expert Syst. Appl. , 2012. 39(8): p. 7536-7545.
  6. Ammar Ahmed E. Elhadi, M. A. Maarof, and A. H. Osman, Malware Detection Based on Hybrid Signature Behaviour Application Programming Interface Call Graph. American Journal of Applied Sciences, 2012. 9 (3): p. 283-288.
  7. Kevadia Kaushal, P. S. , Nilesh Prajapati, Metamorphic Malware Detection Using Statistical Analysis. International Journal of Soft Computing and Engineering (IJSCE), 2012. 2(3).
  8. Yanfang Ye, T. L. , Shenghuo Zhu,Weiwei Zhuang,Egemen Tas,Umesh Gupta,Melih Abdulhayoglu, Combining file content and file relations for cloud based malware detection, in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 2011, ACM: San Diego, California, USA. p. 222-230.
  9. Christodorescu, M. , et al. , Semantics-Aware Malware Detection, in Proceedings of the 2005 IEEE Symposium on Security and Privacy. 2005, IEEE Computer Society. p. 32-46.
  10. Yin, H. , et al. , Panorama: capturing system-wide information flow for malware detection and analysis, in Proceedings of the 14th ACM conference on Computer and communications security. 2007, ACM: Alexandria, Virginia, USA. p. 116-127.
  11. Vinod, P. , et al. , Survey on Malware Detection Methods. 2009.
  12. Zeltser, L. , what is cloud Anti-Virus and how it does work.
  13. Jiang, X. , X. Wang, and D. Xu, Stealthy malware detection through vmm-based "out-of-the-box" semantic view reconstruction, in Proceedings of the 14th ACM conference on Computer and communications security. 2007, ACM: Alexandria, Virginia, USA. p. 128-138. [14. ] Automated dynamic binary analysis. 2007.
  14. Deepak Venugopal, G. H. , Efficient signature based malware detection on mobile devices. Mob. Inf. Syst. , 2008. 4(1): p. 33-49.
  15. Kolbitsch, C. , et al. , Effective and efficient malware detection at the end host, in Proceedings of the 18th conference on USENIX security symposium. 2009, USENIX Association: Montreal, Canada. p. 351-366.
  16. Zhou, S. T. a. M. , A Heuristic Approach for Detection of Obfuscated Malware,. IEEE, 2009.
  17. Ahmed, M. , et al. NIDS: A Network Based Approach to Intrusion Detection and Prevention. in Computer Science and Information Technology - Spring Conference, 2009. IACSITSC '09. International Association of. 2009.
  18. Gar?nkel, T. and M. Rosenblum, A virtual machine introspection based architecture for intrusion detection. 2003: p. 191--206.
  19. Lagar-Cavilla, H. A. , Flexible Computing with Virtual Machines. 2009.
  20. Gorodetsky, V. , et al. , Multi-agent Peer-to-Peer Intrusion Detection Computer Network Security, V. Gorodetsky, I. Kotenko, and V. A. Skormin, Editors. 2007, Springer Berlin Heidelberg. p. 260-271.
  21. Ye, D. , An Agent-Based Framework for Distributed Intrusion Detections. 2009.
  22. Ou, C. -M. and C. R. Ou, Agent-Based immunity for computer virus: abstraction from dendritic cell algorithm with danger theory, in Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing. 2010, Springer-Verlag: Hualien, Taiwan. p. 670-678.
  23. Bijani, S. and D. Robertson, Intrusion detection in open peer-to-peer multi-agent systems, in Proceedings of the 5th international conference on Autonomous infrastructure, management, and security: managing the dynamics of networks and services. 2011, Springer-Verlag: Nancy, France. p. 177-180.
  24. Dong, H. , et al. Research on adaptive distributed intrusion detection system model based on Multi-Agent. in Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on. 2011.
  25. Ou, C. M. , Multiagent-based computer virus detection systems: abstraction from dendritic cell algorithm with danger theory. Springerlink, 2011.
  26. Paritosh Das, R. N. , A Temporal Logic Based Approach to Multi-Agent Intrusion Detection and Prevention. 2012. 28. McGraw, G. and G. Morrisett, Attacking Malicious Code: A Report to the Infosec Research Council. IEEE Softw. , 2000. 17(5): p. 33-41.
  27. Xufang, L. , P. K. K. Loh, and F. Tan. Mechanisms of Polymorphic and Metamorphic Viruses. in Intelligence and Security Informatics Conference (EISIC), 2011 European. 2011.
  28. EroCarrera. and P. Silberman, STATE OF MALWARE: FAMILY TIES. 2010.
  29. Egele, M. , et al. , A survey on automated dynamic malware-analysis techniques and tools. ACM Comput. Surv. , 2008. 44(2): p. 1-42.
  30. Idika, N. and A. P. Mathur. , A Survey of Malware Detection Techniques. 2007.
  31. Goldman, E. , Dissecting Spam's Purported Harms. 2003.
  32. Webster, M. , Algebraic specification of computer viruses and their environments. Selected Papers from the First Conference on Algebra and Coalgebra in Computer Science Young Researchers Workshop (CALCO-jnr 2005), 2005.
  33. Grimes, R. A. , Malicious Mobile Code: Virus Protection for Windows. O'Reilly Media, 2001.
  34. Vaccaro, H. S. and G. E. Liepins. Detection of anomalous computer session activity. in Security and Privacy, 1989. Proceedings. , 1989 IEEE Symposium on. 1989.
  35. Teng, H. S. , K. Chen, and S. C. Lu. Adaptive real-time anomaly detection using inductively generated sequential patterns. in Research in Security and Privacy, 1990. Proceedings. , 1990 IEEE Computer Society Symposium on. 1990.
  36. Heberlein, L. T. , et al. A network security monitor. in Research in Security and Privacy, 1990. Proceedings. , 1990 IEEE Computer Society Symposium on. 1990.
  37. Winkler, J. R. , A Unix Prototype for Intrusion and Anomaly Detection in Secure Networks (1990). Proceeding. 13 th National Computer Security Conference, 1990.
  38. P. García-Teodoro, J. D. -V. , G. Maciá-Fernández, E. Vázquez, Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 2009 28.
  39. Bolzoni, D. and S. Etalle, APHRODITE: an Anomaly-based Architecture for False Positive Reduction. 2006, Centre for Telematics and Information Technology, University of Twente: Enschede.
  40. Chandola, V. , A. Banerjee, and V. Kumar, Anomaly detection: A survey. ACM Comput. Surv. , 2009. 41(3): p. 1-58.
  41. Professor, S. M. B. a. R. B. V. a. A. P. a. A. , Fuzzy Data Mining And Genetic Algorithms Applied To Intrusion Detection. In Proceedings of the National Information Systems Security Conference (NISSC), 2000.
  42. Kozushko, H. , Intrusion Detection: Host-Based and Network-Based Intrusion Detection Systems. 2003.
  43. Basicevic, F. , M. Popovic, and V. Kovacevic. The use of distributed network-based IDS systems in detection of evasion attacks. in Telecommunications, 2005. advanced industrial conference on telecommunications/service assurance with partial and intermittent resources conference/e-learning on telecommunications workshop. aict/sapir/elete 2005. proceedings. 2005.
  44. Gou, X. , W. Jin, and D. Zhao, Multi-agent system for Worm Detection and Containment in Metropolitan Area Networks. Journal of Electronics (China), 2006. 23(2): p. 259-265.
  45. Mechtri, L. , F. D. Tolba, and S. Ghanemi. MASID: Multi-Agent System for Intrusion Detection in MANET. in Information Technology: New Generations (ITNG), 2012 Ninth International Conference on. 2012.
  46. Silva, M. , D. Lopes, and Z. Abdelouahab, A Remote IDS Based on Multi-Agent Systems, Web Services and MDA, in Proceedings of the International Conference on Software Engineering Advances. 2006, IEEE Computer Society. p. 64.
  47. Pinz, C. I. , et al. , Real-time CBR-agent with a mixture of experts in the reuse stage to classify and detect DoS attacks. Appl. Soft Comput. , 2011. 11(7): p. 4384-4398.
  48. Steroids, S. o. , Malware online scanners. accessed 12/4/2013.
  49. Chaugule, A. , Z. Xu, and S. Zhu, A specification based intrusion detection framework for mobile phones, in Proceedings of the 9th international conference on Applied cryptography and network security. 2011, Springer-Verlag: Nerja, Spain. p. 19-37.
  50. Blount, J. J. , D. R. Tauritz, and S. A. Mulder. Adaptive Rule-Based Malware Detection Employing Learning Classifier Systems: A Proof of Concept. in Computer Software and Applications Conference Workshops (COMPSACW), 2011 IEEE 35th Annual. 2011.
  51. Asmaa S. Ashoor, S. G. , Intrusion Detection System (IDS): case study. 2011 International Conference on Advanced Materials Engineering IPCSIT, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Malware Malware Detection Systems Antivirus