Total views : 427

A Hybrid Scheme based on Big Data Analytics using Intrusion Detection System

Affiliations

  • Department of CSE, Andhra Loyola Institute of Engineering College, Vijayawada - 520008, Andhra Pradesh, India

Abstract


Objective: Network security plays a key role for many organizations. Host based and network based Intrusion Detection Systems are available in the market depending upon the detection technology used by them. The objective of this research paper is maintaining security across the heterogeneous data from homogeneous sources and co-relating the heterogeneous data from different sources using hybrid strategy. Methods/Statistical Analysis: A real time detection Intrusion Prevention Systems (IPS), prevents security intrusions by gathering and composing with technologies. Findings: Heterogeneous data from different sources has been collected from KDD Cup Dataset and segregated into learning phase and detection phase. In the learning phase, known attacks will be identified. Similarly detection phase also will consider the same. Applications/Improvements: The proposed system specifies a set of rules and high DoS, R2L, U2R, Probe. One may attempt to get good results by improving the efficiency and reducing the complexity present in the model. In future several reduction techniques may be studied to get more features.

Keywords

Big-Data, Host Based, IDS, Network Based, Security.

Full Text:

 |  (PDF views: 438)

References


  • Center for Strategic and International Studies. The economic impact of cybercrime and cyber espionage. Technical report. McAfee. Available from: http://www.mcafee.com/us/resources/reports/rp-economic-impact-cybercrime.pdf
  • A case study in security big data analysis. Available from: http://www.darkreading.com/analytics/security-mointoring. a-case-study-in-security-big-data-analysis/d/d-id/1137299
  • Denning DE. An intrusion-detection model. Soft Eng IEEE Trans SE. 1987; 13(2):222–32.
  • Frank J. Artificial intelligence and intrusion detection: Current and future directions. Proceedings of the 17th National Computer Security Conference; Baltimore, MD, USA. 1994. p. 1–12.
  • Group BDW big data analytics for security intelligence. Available from: http://downlods.cloudsecurityalliance.org/initiatives/bdwg/Big_Data_Analytics_for_Security_intelligence.pdf
  • Information assurance solutions group, defense in depth. Technical report; 2015. National Security Agency. Available from: http://www.nsa.gov/ia/_files/support/defenseindepth.pdf
  • Julisch K, Dacier M. Mining intrusion detection alarms for actionable knowledge. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; Edmonton, Alberta, Canada. 2002. p. 366–75.
  • Data management: Controlling data volume, velocity and variety. Technical Report 949; META Group (now Gartner). Available from: http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
  • Nassr M, Bouna AB, Mallui Q. Secure outsourcing of network flow data analysis. IEEE International Congress on Big Data; Santa Clara, CA, USA. 2013; 2(3):431–2.
  • Ponemon Institute LLC. Cost of cyber crime study: United States. Technical Report. Ponemon Institute. Available from: http://www.ponemon.org/local/upload/file/2012_US_Cost_of_Cyber_Crime_Study_FINAL6%20.pdf
  • Roesch M. Snort: Lightweight intrusion detection for networks. LISA. USENIX; Seattle, WA, USA. 1999. p. 229–38.
  • Sourcefire, Snort, Home Page. Available from: http;//www.snort.org/
  • Verizon RISK Team, data breach investigations report. Technical Report. Verizon. Available from: www.verizonenterprise.com/verizon-insights-lab/dbir/
  • Zikopoulos P, Parasuraman K, Deutsch T, Giles J, Corrigan D. Harness the power of big data the IBM big data platform. New York, NY: McGraw Hill Professional; 2012.
  • Arora SK, Vijan S, GabaGS. Detection and analysis of black hole attack using IDS. Indian Journal of Science and Technology. 2016 May; 9(20). DOI: 10.17485/ijst/2016/v9i20/85588.
  • Srikanth BVS, Reddy VK. Efficiency of stream processing engines for processing BIGDATA Streams. Indian Journal of Science and Technology. 2016 Apr; 9(14). DOI: 10.17485/ijst/2016/v9i14/84797.
  • Kyoo-sung N, Doo-sik L. Bigdata platform design and implementation model. Indian Journal of Science and Technology. 2015 Aug; 8(18). DOI: 10.17485/ijst/2015/v8i18/75864.
  • Renjit JA, Shunmuganathan KL. Network based anomaly intrusion detection system using SVM. Indian Journal of Science and Technology. 2011 Sep; 4(9). DOI: 10.17485/ijst/2011/v4i9/30239.
  • Azad C, Jha VK. Data mining based hybrid intrusion detection system. Indian Journal of Science and Technology. 2014 Jun; 7(6):781–9.
  • Mourougan S, Aramudhan M. Hybrid evolutionary algorithm based intrusion detection system for denial of service attacks. 2015 Dec; 8(35). DOI: 10.17485/ijst/2015/v8i35/86652.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.