skip to main content
10.1145/3154273.3154311acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicdcnConference Proceedingsconference-collections
research-article

An Attack Graph-based On-line Multi-step Attack Detector

Published:04 January 2018Publication History

ABSTRACT

Modern distributed systems are characterized by complex deployment designed to ensure high availability through replication and diversity, to tolerate the presence of failures and to limit the possibility of successful compromising. However, software is not free from bugs that generate vulnerabilities that could be exploited by an attacker through multiple steps.

This paper presents an attack-graph based multi-step attack detector aiming at detecting a possible on-going attack early enough to take proper countermeasures through; a Visualization interfaced with the described attack detector presents the security operator with the relevant pieces of information, allowing a better comprehension of the network status and providing assistance in managing attack situations (i.e., reactive analysis mode). We first propose an architecture and then we present the implementation of each building block. Finally, we provide an evaluation of the proposed approach aimed at highlighting the existing trade-off between accuracy of the detection and detection time.

References

  1. {n. d.}. Common Vulnerabilities and Exposures: the Standard for Information Security Vulnerability Names. https://cve.mitre.org. ({n. d.}).Google ScholarGoogle Scholar
  2. {n. d.}. The Esper project. http://esper.codehaus.org/. ({n. d.}).Google ScholarGoogle Scholar
  3. {n. d.}. The PANOPTESEC Project. http://www.panoptesec.eu. ({n. d.}).Google ScholarGoogle Scholar
  4. Ahmed Aleroud and George Karabatis. 2017. Contextual information fusion for intrusion detection: a survey and taxonomy. Knowledge and Information Systems (2017), 1--57. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Marco Angelini, Nicolas Prigent, and Giuseppe Santucci. 2015. PERCIVAL: proactive and reactive attack and response assessment for cyber incidents using visual analytics. In Visualization for Cyber Security (VizSec), 2015 IEEE Symposium on. IEEE, 1--8.Google ScholarGoogle ScholarCross RefCross Ref
  6. Marco Angelini and Giuseppe Santucci. 2017. Cyber situational awareness: from geographical alerts to high-level management. Journal of Visualization 20, 3 (2017), 453--459. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Marco Angelini and Giuseppe Santucci. August 24-26, 2015, Tokyo, Japan. Visual Cyber Situational Awareness for Critical Infrastructures. In Proceedings of ACM VINCI '15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. M. Bonifacio, A. M. Cansian, A. C. P. L. F. De Carvalho, and E. S. Moreira. 1998. Neural networks applied in intrusion detection systems. In 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227), Vol. 1. 205--210 vol.1.Google ScholarGoogle Scholar
  9. Yacine Bouzida and Sylvain Gombault. 2003. Intrusion detection using principal component analysis. In Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics. Citeseer.Google ScholarGoogle Scholar
  10. Rung-Ching Chen and Su-Ping Chen. 2008. Intrusion detection using a hybrid support vector machine based on entropy and TF-IDF. International Journal of Innovative Computing, Information, and Control (IJICIC) 4, 2 (2008), 413--424.Google ScholarGoogle Scholar
  11. Domenico Cotroneo, Andrea Paudice, and Antonio Pecchia. 2016. Automated Root Cause Identification of Security Alerts. Future Gener. Comput. Syst. 56, C (March 2016), 375--387. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Ozgur Depren, Murat Topallar, Emin Anarim, and M. Kemal Ciliz. 2005. An Intelligent Intrusion Detection System (IDS) for Anomaly and Misuse Detection in Computer Networks. Expert Syst. Appl. 29, 4 (Nov. 2005), 713--722. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Bingrui Foo, Yu-Chun Mao, and Eugene Spafford. 2005. ADEPTS: Adaptive Intrusion Response Using Attack Graphs in an E-Commerce Environment. In Proceedings of the 2005 International Conference on Dependable Systems and Networks (DSN '05). IEEE Computer Society, Washington, DC, USA, 508--517. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Emden R. Gansner and Stephen C. North. 2000. An open graph visualization system and its applications to software engineering. SOFTWARE - PRACTICE AND EXPERIENCE 30, 11 (2000), 1203--1233. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Richard P. Lippmann. 2002. NetSPA: a Network Security Planning Architecture. Master Thesis. (2002).Google ScholarGoogle Scholar
  16. Guisong Liu, Zhang Yi, and Shangming Yang. 2007. A hierarchical intrusion detection model based on the {PCA} neural networks. Neurocomputing 70, 7âAŞ9 (2007), 1561--1568. Advances in Computational Intelligence and Learning 14th European Symposium on Artificial Neural Networks 200614th European Symposium on Artificial Neural Networks 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. S. Mathew, R. Giomundo, S. Upadhyaya, M. Sudit, and A. Stotz. 2006. Understanding Multistage Attacks by Attack-Track based Visualization of Heterogeneous Event Streams. In VizSec 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. S. Mathew, C. Shah, and S. Upadhyaya. 2005. An alert fusion framework for situation awareness of coordinated multistage attacks. In Third IEEE International Workshop on Information Assurance (IWIA'05). 95--104. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. S Noel, E Harley, KH Tam, M Limiero, and M Share. 2016. CyGraph: Graph-Based Analytics and Visualization for Cybersecurity. Handbook of Statistics 35 (2016), 117--167.Google ScholarGoogle ScholarCross RefCross Ref
  20. Steven Noel and Sushil Jajodia. 2005. Understanding Complex Network Attack Graphs Through Clustered Adjacency Matrices. In Proceedings of the 21st Annual Computer Security Applications Conference (ACSAC '05). IEEE Computer Society, Washington, DC, USA, 160--169. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Steven Noel, Sushil Jajodia, Brian O'Berry, and Michael Jacobs. 2003. Efficient Minimum-Cost Network Hardening Via Exploit Dependency Graphs. In Proceedings of the 19th Annual Computer Security Applications Conference (ACSAC '03). IEEE Computer Society, Washington, DC, USA, 86--. http://dl.acm.org/citation.cfm?id=956415.956451 Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Steven Noel, Eric Robertson, and Sushil Jajodia. 2004. Correlating Intrusion Events and Building Attack Scenarios Through Attack Graph Distances. In Proceedings of the 20th Annual Computer Security Applications Conference (ACSAC '04). IEEE Computer Society, Washington, DC, USA, 350--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Xinming Ou, Sudhakar Govindavajhala, and Andrew W. Appel. 2005. MulVAL: A Logic-based Network Security Analyzer. In Proceedings of the 14th Conference on USENIX Security Symposium - Volume 14 (SSYM'05). USENIX Association, Berkeley, CA, USA, 8--8. http://dl.acm.org/citation.cfm?id=1251398.1251406 Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Ali Ahmadian Ramaki, Morteza Amini, and Reza Ebrahimi Atani. 2015. RTECA: Real time episode correlation algorithm for multi-step attack scenarios detection. Computers & Security 49 (2015), 206--219. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. P. M. Rathod, N. Marathe, and A. V. Vidhate. 2014. A survey on Finite Automata based pattern matching techniques for network Intrusion Detection System (NIDS). In 2014 International Conference on Advances in Electronics Computers and Communications. 1--5.Google ScholarGoogle Scholar
  26. Indrajit Ray and Nayot Poolsapassit. 2005. Using Attack Trees to Identify Malicious Attacks from Authorized Insiders. In Proceedings of the 10th European Conference on Research in Computer Security (ESORICS'05). Springer-Verlag, Berlin, Heidelberg, 231--246. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Layal Samarji, Frédéric Cuppens, Nora Cuppens-Boulahia, Wael Kanoun, and Samuel Dubus. 2013. Situation Calculus and Graph Based Defensive Modeling of Simultaneous Attacks. In Cyberspace Safety and Security - 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013, Proceedings. 132--150.Google ScholarGoogle Scholar
  28. Oleg Sheyner, Joshua Haines, Somesh Jha, Richard Lippmann, and Jeannette M. Wing. 2002. Automated Generation and Analysis of Attack Graphs. In Proceedings of the 2002 IEEE Symposium on Security and Privacy (SP '02). IEEE Computer Society, Washington, DC, USA, 273--. http://dl.acm.org/citation.cfm?id=829514.830526 Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Grant Vandenberghe. 2008. Network Traffic Exploration Application: A Tool to Assess, Visualize, and Analyze Network Security Events. In VizSec 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Wei Wang and T. E. Daniels. 2005. Building evidence graphs for network forensics analysis. In 21st Annual Computer Security Applications Conference (ACSAC'05). 11 pp.--266. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Leevar Williams, Richard Lippmann, and Kyle Ingols. 2007. An Interactive Attack Graph Cascade and Reachability Display. In VizSec 2007.Google ScholarGoogle Scholar
  32. Saman A. Zonouz, Himanshu Khurana, William H. Sanders, and Timothy M. Yardley. 2014. RRE: A Game-Theoretic Intrusion Response and Recovery Engine. IEEE Trans. Parallel Distrib. Syst. 25, 2 (Feb. 2014), 395--406. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. An Attack Graph-based On-line Multi-step Attack Detector

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        ICDCN '18: Proceedings of the 19th International Conference on Distributed Computing and Networking
        January 2018
        494 pages
        ISBN:9781450363723
        DOI:10.1145/3154273

        Copyright © 2018 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 ACM 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: 4 January 2018

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader