Enhanced Intrusion Network System using Fuzzy –K-Mediod Clustering Method
Jaskirat Singh1, Sanjay Singla2
1Jaskirat Singh*, Department of Computer Science and Engineering at GGS College of Modern Technology(IKGPTU), Kharar (Mohali), Punjab, India.
2Dr Sanjay Singla is a Professor and Dean Academics, at GGS college of Modern Technology(IKGPTU), Kharar (Mohali), Punjab, India.
Manuscript received on September 12, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3370-3374 | Volume-8 Issue-12, October 2019. | Retrieval Number: L25831081219/2019©BEIESP | DOI: 10.35940/ijitee.L2583.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Intrusion detection scheme (IDS) is software applications that are used for monitoring of the network to recognize the malicious activity in the system. With the advent in technology and internet, the incident of the intruder activity on the system has been increased. Hence, the protection and security of the system become an essential approach because attackers utilize different kinds of attack methods to hack the useful information. However, various intrusion detection methods and algorithm have been developed to detect various types of the attacks. Some issues in existing research are excesses of information with different volumetric data. It became difficult to detect intrusion in large amount of data in a computer network.. Hence, the proposed research on different machine learning approach is used for network intrusion detection. In addition, clustering method implemented to segment the features using k mean clustering and k-medoids clustering algorithm. Moreover, implement an enhanced Fuzzy k medoids clustering approach for recognition of intrusion and faults on the network. Fuzzy k-mediod clustering helps in the evaluation of the maximum degree matrix. Experimental analysis is done by evaluating and comparing the parameters using Precision, Recall, Accuracy ,FAR and FRR.
Keywords: Clustering Process, Malicious Attack, Machine learning Approach, Fuzzy k-mediod Clustering.
Scope of the Article: Clustering