Honey Pot: A Major Technique for Intrusion Detection

  • Rajalakshmi Selvaraj
  • Venu Madhav Kuthadi
  • Tshilidzi Marwala
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

Generally, Intrusion detection system (IDS) is installed in industrial environment for protecting network that works based on signature, where they are not capable of detecting most unidentified attacks. The detection of undefined attack and intrusion is not more helpful to identify the several kinds of attack, where intrusion-based attack has become a challenging task to detect intruder on network. A skilled attacker can obtain a sensible information and data from the system after knowing the weakness. Distributed denial of service (DDoS) is a major thread over the security and most enlarging thread in recent days. There are so many types of Denial of Service (DoS) such as Teardrop, Smurf, Ping of Death, and Clone attack. The aim of the cyber defense system is to detect the main cause of the several counter attacks on the enterprise network. On the way to fix these issues, we are proposing a novel idea that relies on honey pot technique and packet data analysis which are trained by the sample of malware after using the Intrusion detection technique in both ways separately as Network and Anomaly intrusion detection system. Some approaches are not being easily implemented in the network of real enterprises, because of practicability training system which is trained by the sample of malware or deep analysis of packet inspection or depends on the host-based technique that requires a big capacity for storage over the enterprise. The honey pots are one of the most successful techniques to collect the sample of malware for the purpose of analysis and identification of attacks. Honey pot is a novel technology which consists of massive energy and possibilities in the field of security. It helps reading the behavior of the attack and attacker information.

Keywords

Honey pot IDS Packet analysis Intruder 

References

  1. 1.
    Shyamasundar, L.B.: An auto configurated hybrid honeypot for improving security in computer systems. Int. J. Comput. Sci. Inform. Technol. 6(1), 84–88 (2015)Google Scholar
  2. 2.
    Parimala, H.C., Kavitha, B.: Achieving higher network security by preventing DDoS attack using honeypot. Int. J. Comput. Netw. Secur. 6(1), 40–45 (2014)Google Scholar
  3. 3.
    Suruchi, N., Sandeep, K.: Advanced honeypot system for analysing network security. Int. J. Curr. Res. Acad. Rev. 2(4), 65–70 (2014)Google Scholar
  4. 4.
    Fatih, H., Abdulkadir, P., Erkam, U., Bakır, Emre., Necati, S.: An automated bot detection system through honeypots for large-scale. In: 6th International Conference on Cyber Conflict, Estonia, pp. 255–272 (2014)Google Scholar
  5. 5.
    Meghana, S., Vidya, D.: Intrusion detection technique using data mining approach: survey. Int. J. Innov. Res. Comput. Commun. Eng. 2(11), 6352–6359 (2014)Google Scholar
  6. 6.
    Brijendra, P., Ramakrishna, C., Rakesh, S., Sanjeev, K.: Implementation of port density based dynamic clustering algorithm on honey net data. Int. J. Adv. Comput. Eng. Netw. 2(6), 76–82 (2014)Google Scholar
  7. 7.
    Dasen, R., Juan., W., and Qiren, Y.: An intrusion detection algorithm based on decision tree technology. In: Asia-Pacific Conference on Information Processing, Shenzhen, pp. 333–335 (2009)Google Scholar
  8. 8.
    McHugh, J., Christie, A., Allen, J.: Defending yourself: the role of intrusion detection system. IEEE 17(5), 42–51 (2000)Google Scholar
  9. 9.
    Jeremy, B., Jean-Francois, L., Christian, T.: Security and results of a large-scale high-interaction honeypot. J. Comput. 4(5), 395–404 (2009)Google Scholar
  10. 10.
    Das, V.: Honeypot scheme for distributed denial-of-service attack. In: International Conference on Advanced Computer Control, India, pp. 497–501 (2009)Google Scholar
  11. 11.
    Kuthadi, V.M., Rajendra, C., Selvaraj, R.: A study of security challenges in wireless sensor networks. JATIT 20(1), 39–44 (2010)Google Scholar
  12. 12.
    Divya, A.C.: GHIDS: A Hybrid Honeypot System Using Genetic Algorithm. Int. J. Comput. Technol. Appl. 3(1), 187–191 (2012)Google Scholar
  13. 13.
    Yun, Y., Hongli, Y.: Design of distributed honeypot system based on intrusion tracking. In: 3rd International Conference on Communication Software and Networks, China, pp. 196–198 (2011)Google Scholar
  14. 14.
    Jiqiang, Z., Keqi, W.: Design and implementation of dynamic virtual network. In: International Conference on Electronic and Mechanical Engineering and Information Technology, Harbin, China, pp. 2131–2134 (2011)Google Scholar
  15. 15.
    Selvaraj, R., Kuthadi, V.M., Marwala, T.: An effective ODAIDS-HPs approach for preventing, detecting and responding to DDoS attacks. Brit. J. Appl. Sci. Technol. 5(5), 500–509 (2015)CrossRefGoogle Scholar
  16. 16.
    Siva, T., Phalguna, K.E.S.: Controlling various network based ADoS attacks in cloud computing environment: by using port hopping technique. Int. J. Eng. Trends Technol. 4(5), 2099–2104 (2013)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Rajalakshmi Selvaraj
    • 1
    • 2
  • Venu Madhav Kuthadi
    • 3
  • Tshilidzi Marwala
    • 1
  1. 1.Faculty of Engineering and the Built EnvironmentUniversity of JohannesburgJohannesburgSouth Africa
  2. 2.Department of Computer ScienceBIUSTGaboroneBotswana
  3. 3.Department of AISUniversity of JohannesburgJohannesburgSouth Africa

Personalised recommendations