Data Mining and Machine Learning Techniques for Cyber Security Intrusion Detection

Authors(3) :-M. Nikhil Kumar, K.V.S. Koushik, K. John Sundar

An interruption detection system is programming that screens a solitary or a system of PCs for noxious exercises that are gone for taking or blue penciling data or debasing system conventions. Most procedure utilized as a part of the present interruption detection system are not ready to manage the dynamic and complex nature of digital assaults on PC systems. Despite the fact that effective versatile strategies like different systems of machine learning can bring about higher detection rates, bring down false caution rates and sensible calculation and correspondence cost. With the utilization of information mining can bring about incessant example mining, order, grouping and smaller than normal information stream. This study paper depicts an engaged writing review of machine learning and information digging techniques for digital investigation in help of interruption detection. In view of the quantity of references or the pertinence of a rising strategy, papers speaking to every technique were distinguished, perused, and compressed. Since information are so essential in machine learning and information mining approaches, some notable digital informational indexes utilized as a part of machine learning and information digging are portrayed for digital security is displayed, and a few proposals on when to utilize a given technique are given.

Authors and Affiliations

M. Nikhil Kumar
Department of CSE, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India
K.V.S. Koushik
Department of CSE, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India
K. John Sundar
Department of CSE, VR Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India

Component, Formatting, Style, Styling, Insert

  1. Zhenlong Li, Chaowei Yang, Baoxuan Jin, Manzhu Yu, Kai Liu, Min Sun, Matthew Zhan, "Enabling Big Geoscience Data Analytics with a Cloud-BasedMapReduce-Enabled and Service- Oriented Workflow Framework", Research Article, Plos One, DOI:10.1371/journal.pone.0116781 March5, 2015
  2. Dutty DQ, Schnase, JL, Thompson JH, Freeman SM, CluneTL, "Preliminary Evaluation of MapReduce for High-Performance Climate Data Analysis", NASAnew technology report white paper, 2012
  3. Santiago A.Nunes, Luciana A.S. Romani, Ana M.H. Avila, "Analysis of Large Scale Climate Data: How Well Climate Change Models and Data from Real Sensor Networks Agree?", 22nd international conference on world wide web,New York, USA, pp.517-526,ACM,ISBN:978-14503-2038-2,2013.
  4. Yang C, Goodchild M, Huang Q, Nebert D, Raskin R, "Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?", International Journal of Digital Earth, pp. 305-329,Vol. 4, No. 4, July 2011.
  5. Vatika Sharma, Meenu Dave, "SQL and NOSQLDatabses", International Journal of Advanced Research in Computer Science and Software Engineering.pp. 20-27, volume 2, Issue 8, august 2012, ISSN:2277 128X.
  6. Songnian Li, SuzanaDragicevic, FrancesAnton Castro, Monika Sester, Stephan Winter, ArzuColtekin, Christopher Pettit, "Geospatial big data handling theory and methods: A review and research challenges", Volume2 | Issue2 || March-April-2017 | www.ijsrcseit.com 97 ISPRS Journal of Photogrammetry and Remote Sensing, pp. 119-133, Volume 115, May 2016.
  7. Tong Zhang, Jing Li, Qing Liu, Qunying Huang, "Cloud-Enabled Remote Visualization Tool for Time Variant Climate Analytics", journal of Environmental Modelling&Software Science Direct, pp. 513-518, Volume 75, January 2013.
  8. GemaBello-Orgaza, Jason Jnugb, David Camacho, "Social big data: Recent achievements and new challenges", Journal of Information Fusion,ScienceDirect, pp. 45-59, Volume 28, March 2016.
  9. Stetano Nativi, Paolo Mazzetti, Mattia Santoro, FabrizioPapeschi, Max Carglia, Osamu Ochiai, "Big Modelling & SSoftware, ScienceDirect, pp. 1-26, Volume 68, June 2015.
  10. Yu Zheng, "Methodologies for Cross-Domain Data Fusion: An Overview", IEEE Transactions on big Data, pp. 16-34, Volume:1, Issue:1, TBD-2015-05-0037, March 2015.
  11. Yu Zheng, "Crowdsourcing geospatial data", ISPRS Journal of Photogrammetry and Remote Sensing, ScienceDirect, pp.550-557, Volume 65, Issue 6, November 2010.

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 162-167
Manuscript Number : CSEIT1183334
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

M. Nikhil Kumar, K.V.S. Koushik, K. John Sundar, "Data Mining and Machine Learning Techniques for Cyber Security Intrusion Detection ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.162-167, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1183334

Article Preview

Follow Us

Contact Us