More Web Proxy on the site http://driver.im/

research-article

An improved k-Means Clustering algorithm for Intrusion Detection using Gaussian function

Authors:

Gunupudi Rajesh Kumar,

N. Mangathayaru,

G. NarasimhaAuthors Info & Claims

ICEMIS '15: Proceedings of the The International Conference on Engineering & MIS 2015

Article No.: 69, Pages 1 - 7

https://doi.org/10.1145/2832987.2833082

Published: 24 September 2015 Publication History

Editorial Notes

NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the ICEMIS 2015 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.

Abstract

In this paper the major objective is to design and analyze the suitability of Gaussian similarity measure for intrusion detection. The objective is to use this as a distance measure to find the distance between any two data samples of training set such as DARPA Data Set, KDD Data Set. This major objective is to use this measure as a distance metric when applying k-means algorithm. The novelty of this approach is making use of the proposed distance function as part of k-means algorithm so as to obtain disjoint clusters. This is followed by a case study, which demonstrates the process of Intrusion Detection. The proposed similarity has fixed upper and lower bounds.

References

[1]

Alok Sharma, Arun K Pujari, Kuldip K Paliwal, Intrusion Detection using text processing techniques with a kernel based similarity measure, Elsevier Journal of Computers and Security, P. 488--495 2007

Digital Library

[2]

Yihua Liao, V. Rao Vemuri, Using Text Categorization Techniques for Intrusion Detection, Proceedings of the 11th USENIX Security Symposium, Pages 51--59 USENIX Association Berkeley, CA, USA 2002

Digital Library

[3]

Review: A survey of intrusion detection techniques in Cloud, ChiragModi, Dhiren Patel, BhaveshBorisaniya, Hiren Patel, Avi Patel, MuttukrishnanRajarajan, Elsevier Journal of Network and Computer Applications

Digital Library

[4]

Towards adaptive character frequency-based exclusive signature matching scheme and its applications in distributed intrusion detection, YuxinMeng, Wenjuan Li, Lam--For Kwok, Computer Networks 57 (2013) 3630--3640, 2013

Digital Library

[5]

Review: A survey of intrusion detection techniques in Cloud, ChiragModi, Dhiren Patel, BhaveshBorisaniya, Hiren Patel, Avi Patel, MuttukrishnanRajarajan, Elsevier Journal of Network and Computer Applications

Digital Library

[6]

Mohammad Saniee Abadeh, Hamid Mohamadi, Jafar Habibi Design and analysis of genetic fuzzy systems for intrusion detection in computer networks, Elsevier Journal of Expert Systems with Applications 38, (2011) Pages 7067--7075

Digital Library

[7]

Intrusion detection using neural based hybrid classication methods, M. Govindarajan, RM. Chandrasekaran, Elsevier, Computer Networks 55 (2011) 1662--1671

Digital Library

[8]

An anomaly intrusion detection method by clustering normal user behavior, Sang Hyun Oh andWon Suk Lee

[9]

Lee, W. Stolfo, S. Kui, M.: A Data Mining Framework for Building Intrusion Detection Models. IEEE Symposium on Security and Privacy (1999) 120--132

[10]

Manganaris, S., Christensen, M., Zerkle, D., Hermiz, K.: A Data Mining Analysis of RTID Alarms. Proceedings of Recent Advances in Intrusion Detection, Second International Workshop (1999)

[11]

Vladimir N. Vapnik. 1995. The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc., New York, NY, USA.

Digital Library

[12]

Thorsten Joachims. 1999. Making large-scale support vector machine learning practical. In Advances in kernel methods, Bernhard Schlkopf, Christopher J. C. Burges, and Alexander J. Smola (Eds.). MIT Press, Cambridge, MA, USA 169--184.

Digital Library

[13]

Neminath Hubballi and Vinoth Suryanarayanan. 2014. Review: False alarm minimization techniques in signature-based intrusion detection systems: A survey. Comput. Commun. 49 (August 2014), 1--17.

Digital Library

[14]

Yuxin Meng, Wenjuan Li, and Lam-For Kwok. 2013. Towards adaptive character frequency-based exclusive signature matching scheme and its applications in distributed intrusion detection. Comput. Netw. 57, 17 (December 2013), 3630--3640.

Digital Library

[15]

Wenke Lee and Salvatore J. Stolfo. 1998. Data mining approaches for intrusion detection. In Proceedings of the 7th conference on USENIX Security Symposium - Volume 7 (SSYM98), Vol. 7. USENIX Association, Berkeley, CA, USA, 6--6.

Digital Library

[16]

Daniel Barbar & #225;, Julia Couto, Sushil Jajodia, and Ningning Wu. 2001. ADAM: a testbed for exploring the use of data mining in intrusion detection. SIGMOD Rec. 30, 4 (December 2001), 15--24.

Digital Library

[17]

Lee, W. Stolfo, S. Kui, M.: A Data Mining Framework for Building Intrusion Detection Models. IEEE Symposium on Security and Privacy (1999) 120--132

[18]

Manganaris, S., Christensen, M., Zerkle, D., Hermiz, K.: Stefanos Manganaris, Marvin Christensen, Dan Zerkle, and Keith Hermiz. 2000. A data mining analysis of RTID alarms. Comput. Netw. 34, 4 (October 2000), 571--577.

Digital Library

[19]

James J. Treinen and Ramakrishna Thurimella. 2006. A framework for the application of association rule mining in large intrusion detection infrastructures. In Proceedings of the 9th international conference on Recent Advances in Intrusion Detection (RAID06)

Digital Library

[20]

Wei-Chao Lin, Shih-Wen Ke, Chih-Fong Tsai, CANN: An intrusion detection system based on combining cluster centers and nearest neighbors, Knowledge-Based Systems 78 (2015) 13--21

Digital Library

Cited By

Jiang KZou FHuang HZheng LZhai H(2024)Open DGML: Intrusion Detection Based on Open-Domain Generation Meta-LearningApplied Sciences10.3390/app1413542614:13(5426)Online publication date: 22-Jun-2024
https://doi.org/10.3390/app14135426
Sharma VKumar ASharma K(2024)Digital twin: securing IoT networks using integrated ECC with blockchain for healthcare ecosystemKnowledge and Information Systems10.1007/s10115-024-02273-6Online publication date: 18-Nov-2024
https://doi.org/10.1007/s10115-024-02273-6
Fatani ADahou AAbd Elaziz MAl-qaness MLu SAlfadhli SAlresheedi S(2023)Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural NetworksSensors10.3390/s2309443023:9(4430)Online publication date: 30-Apr-2023
https://doi.org/10.3390/s23094430
Show More Cited By

Index Terms

An improved k-Means Clustering algorithm for Intrusion Detection using Gaussian function
1. Security and privacy
2. Social and professional topics
  1. Computing / technology policy
    1. Computer crime

Recommendations

Incremental Clustering Algorithm for Intrusion Detection Using Clonal Selection
PACIIA '08: Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 01

A computing cluster radius method is given, and the data is partitioned into initial clusters by comparing the distance from data to cluster centroid with the size of cluster radius. To implement clustering analysis about data with mixed attributes, ...
Intrusion Detection Based on Simulated Annealing and Fuzzy C-means Clustering
MINES '09: Proceedings of the 2009 International Conference on Multimedia Information Networking and Security - Volume 02

An intrusion detection method based on simulated annealing and fuzzy c-means clustering is proposed against the problems of sensitivity to initialization and local optimal solution caused by fuzzy c-means clustering algorithm. The ability of simulated ...
Hybrid Bisect K-Means Clustering Algorithm
BCGIN '11: Proceedings of the 2011 International Conference on Business Computing and Global Informatization

In this paper, we present a hybrid clustering algorithm that combines divisive and agglomerative hierarchical clustering algorithm. Our method uses bisect K-means for divisive clustering algorithm and Unweighted Pair Group Method with Arithmetic Mean (...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICEMIS '15: Proceedings of the The International Conference on Engineering & MIS 2015

September 2015

429 pages

ISBN:9781450334181

DOI:10.1145/2832987

Conference Chair:
Shadi Aljawarneh,
Program Chair:
Bassam Al-Shargabi

Copyright © 2015 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 the author(s) 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].

In-Cooperation

The Isra University
University of Aizu: University of Aizu
IBM: IBM

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 September 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICEMIS '15

ICEMIS '15: The International Conference on Engineering & MIS 2015

September 24 - 26, 2015

Istanbul, Turkey

Acceptance Rates

Overall Acceptance Rate 215 of 605 submissions, 36%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

42
Total Citations
View Citations
318
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)3

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jiang KZou FHuang HZheng LZhai H(2024)Open DGML: Intrusion Detection Based on Open-Domain Generation Meta-LearningApplied Sciences10.3390/app1413542614:13(5426)Online publication date: 22-Jun-2024
https://doi.org/10.3390/app14135426
Sharma VKumar ASharma K(2024)Digital twin: securing IoT networks using integrated ECC with blockchain for healthcare ecosystemKnowledge and Information Systems10.1007/s10115-024-02273-6Online publication date: 18-Nov-2024
https://doi.org/10.1007/s10115-024-02273-6
Fatani ADahou AAbd Elaziz MAl-qaness MLu SAlfadhli SAlresheedi S(2023)Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural NetworksSensors10.3390/s2309443023:9(4430)Online publication date: 30-Apr-2023
https://doi.org/10.3390/s23094430
N KS DK S JS D(2023)The Implementation of Supervised Algorithms for Intrusion Detection for Internet of Things Devices in Smart Home2023 International Conference on Recent Advances in Science and Engineering Technology (ICRASET)10.1109/ICRASET59632.2023.10420316(1-3)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICRASET59632.2023.10420316
Rachini AFares CAssaf MJamal BKhatoun R(2023)AI-Powered Network Intrusion Detection: A New Frontier in Cybersecurity2023 24th International Arab Conference on Information Technology (ACIT)10.1109/ACIT58888.2023.10453733(1-8)Online publication date: 6-Dec-2023
https://doi.org/10.1109/ACIT58888.2023.10453733
Dahou AAbd Elaziz MChelloug SAwadallah MAl-Betar MAl-qaness MForestiero A(2022)Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search AlgorithmComputational Intelligence and Neuroscience10.1155/2022/64735072022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6473507
Patel NMehtre BWankar R(2022)Detection of Intrusions using Support Vector Machines and Deep Neural Networks2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO56286.2022.9964756(1-5)Online publication date: 13-Oct-2022
https://doi.org/10.1109/ICRITO56286.2022.9964756
Thakkar ALohiya R(2022)A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directionsArtificial Intelligence Review10.1007/s10462-021-10037-955:1(453-563)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s10462-021-10037-9
Kumar GMangathayaru NKolli AKomatineni AReddy SReddy S(2022)Machine Learning Approach for Detection of Cardiology DiseasesAdvanced Informatics for Computing Research10.1007/978-3-031-09469-9_16(182-191)Online publication date: 25-Jun-2022
https://doi.org/10.1007/978-3-031-09469-9_16
Fatani ADahou AAl-qaness MLu SAbd Elaziz M(2021)Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection SystemSensors10.3390/s2201014022:1(140)Online publication date: 26-Dec-2021
https://doi.org/10.3390/s22010140
Show More Cited By

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents