Intrusion Detection Systems (IDS)—An Overview with a Generalized Framework

  • Ranjit PanigrahiEmail author
  • Samarjeet Borah
  • Akash Kumar Bhoi
  • Pradeep Kumar Mallick
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)


The greatest challenge in the present era of Internet and communication technologies is the identification of spiteful activities in a system or network. An Intrusion Detection Systems (IDS) is the traditional approach that they use to follow to minimize such kind of activities. This paper provides an overview of an IDS highlighting its basic architecture and functioning behavior along with a proposed framework. It also provides a classification of threats. The IDSs are classified into various categories based on many criteria. A generalized framework of intrusion detection has been proposed to be implemented by the future network engineer.


Intrusion detection Network Host-based IDS Network security Anomaly detection Misuse detection 


  1. 1.
    Abraham, T.: IDDM: Intrusion Detection Using Data Mining Techniques. Defense Science and Technology Organization. Accessed on Feb 2016
  2. 2.
    Kemmerer, R.A., Vigna, G.: Intrusion detection: a brief history and overview. Comput. Soc. 35(4) (2002).
  3. 3.
    Kemmerer, R.A., Vigna, G.: Intrusion detection: a brief history and overview. Comput. Soc. 35(4) (2002). Scholar
  4. 4.
    Anjum, F., Mouchtaris, P.: Intrusion detection systems. In: Security for Wireless Ad Hoc Networks. Wiley (2007).
  5. 5.
    Douligeris, C., Serpanos, D.N.: Intrusion detection versus intrusion protection. In: Network Security: Current Status and Future Directions. IEEE (2007).
  6. 6.
    Ovaska, S.J.: Intrusion detection for computer security. In: Computationally Intelligent Hybrid Systems: The Fusion of Soft Computing and Hard Computing. IEEE (2005). Scholar
  7. 7.
    Viegas, E.K., Santin, A.O., Oliveira, L.S.: Toward a reliable anomaly-based intrusion detection in real-world environments. Comput. Netw. 127, 200–216 (2017)., ID: 271990CrossRefGoogle Scholar
  8. 8.
    Kendall, K.R.: A database of computer attacks for the evaluation of intrusion detection systems. Ph.D. Dissertation (1999)Google Scholar
  9. 9.
    Babar, S., Mahalle, P., Stango, A., Prasad, N., Prasad, R.: Proposed security model and threat taxonomy for the Internet of Things (IoT). In: International Conference on Network Security and Applications, pp. 420–429. Springer, Berlin (2010)CrossRefGoogle Scholar
  10. 10.
    Welch, D., Lathrop, S.: Wireless security threat taxonomy. In: Information Assurance Workshop, 2003, pp. 76–83. IEEE Systems, Man and Cybernetics Society. IEEE (2003)Google Scholar
  11. 11.
    Hindy, H., Brosset, D., Bayne, E., Seeam, A., Tachtatzis, C., Atkinson, R., Bellekens, X.: A taxonomy and survey of intrusion detection system design techniques. Netw. Threats Datasets (2018)Google Scholar
  12. 12.
    McClure, S., Scambray, J., Kurtz, G.: Hacking Exposed: Network Security Secrets and Solutions, 6th edn. McGraw-Hill Osborne Media (2009). ASIN=0071613749
  13. 13.
    Dubrawsky, I.: Topologies and IDS, Book: How to Cheat at Securing Your Network, pp. 281–315. Syngress (2007). ISBN 9781597492317,,
  14. 14.
    Beal, V.: Intrusion Detection (IDS) and Prevention (IPS) Systems. URL:, 15 July 2005
  15. 15.
    Kumar, N., Angral, S., Sharma, R.: Integrating intrusion detection system with network monitoring. Int. J. Sci. Res. Publ. 4(5) (2014). ISSN 2250-3153Google Scholar
  16. 16.
    Parande, V., Kori, S.: Host based intrusion detection system. Int. J. Sci. Res. (IJSR) (2015). ISSN (Online): 2319-7064Google Scholar
  17. 17.
    Kumar, B.S., Chandra, T., Raju, R.S.P., Ratnakar, M., Baba, S.D., Sudhakar, N.: Intrusion detection system-types and prevention. Int. J. Comput. Sci. Inf. Technol. 4(1), 77–82 (2013)Google Scholar
  18. 18.
    Debar, H., Curry, D., Feinstein, B.: The Intrusion Detection Message Exchange Format (IDMEF)., No. 4765, Mar 2007
  19. 19.
    Hee, C., Jo, B., Choi, S., Park, T.: Feature selection for intrusion detection using NSL-KDD. Recent Adv. Comput. Sci. 184–187 (2013). ISBN: 978-960-474-354-4Google Scholar
  20. 20.
    Singh, J., Ram, H., Sodhi, J.S.: Improving efficiency of Apriori algorithm using transaction reduction. Int. J. Sci. Res. Publ. 3(1) (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ranjit Panigrahi
    • 1
    Email author
  • Samarjeet Borah
    • 1
  • Akash Kumar Bhoi
    • 2
  • Pradeep Kumar Mallick
    • 3
  1. 1.Department of Computer ApplicationsSikkim Manipal Institute of Technology (SMIT), Sikkim Manipal UniversityMajitarIndia
  2. 2.Department of Electrical and Electronics EngineeringSikkim Manipal Institute of Technology, Sikkim Manipal UniversityMajitarIndia
  3. 3.School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) UniversityBhubaneswarIndia

Personalised recommendations