ISSN 2394-5125
 

Research Article 


Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms

Anupama B S, Kandepi Bhavani, Nirmitha M V.

Abstract
Computer Network is a group of computers connected to a single network which works on common communication protocols for sharing resources. As both rapidly increase in internet usage and computer network, we can see a greater number of attacks happening. In this paper, the intrusions are detected using the KDD dataset and file transform system. In first and second step the KDD dataset features are reduced based on feature selection which helps in better identification of the intrusion. We will capture the real time data through Wi-Fi, Ethernet using Jpcap and convert them to the KDD dataset format. The datasets are trained by making use of the J48 algorithm and Nave Bayes algorithm to identify the intrusion. Comparison is made between the j48 algorithm and Nave Bayes which helps to determine the best algorithm based on time efficiency and band width. By using file transferring system network intrusions are detected. If there is no intrusion inject an intrusion and identify the type of attacks present in it. Types of attacks identified are IP Spoofing Attack, Dos Attack and Invalid Port Spam Attack.

Key words: Intrusion, KDD Dataset, Nave Bayes Algorithm, J48 Algorithm, Feature Selection, Machine Learning, Types of Attacks.


 
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How to Cite this Article
Pubmed Style

Anupama B S, Kandepi Bhavani, Nirmitha M V. Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms. JCR. 2020; 7(17): 2008-2022. doi:10.31838/jcr.07.17.249


Web Style

Anupama B S, Kandepi Bhavani, Nirmitha M V. Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms. http://www.jcreview.com/?mno=40669 [Access: April 10, 2021]. doi:10.31838/jcr.07.17.249


AMA (American Medical Association) Style

Anupama B S, Kandepi Bhavani, Nirmitha M V. Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms. JCR. 2020; 7(17): 2008-2022. doi:10.31838/jcr.07.17.249



Vancouver/ICMJE Style

Anupama B S, Kandepi Bhavani, Nirmitha M V. Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms. JCR. (2020), [cited April 10, 2021]; 7(17): 2008-2022. doi:10.31838/jcr.07.17.249



Harvard Style

Anupama B S, Kandepi Bhavani, Nirmitha M V (2020) Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms. JCR, 7 (17), 2008-2022. doi:10.31838/jcr.07.17.249



Turabian Style

Anupama B S, Kandepi Bhavani, Nirmitha M V. 2020. Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms. Journal of Critical Reviews, 7 (17), 2008-2022. doi:10.31838/jcr.07.17.249



Chicago Style

Anupama B S, Kandepi Bhavani, Nirmitha M V. "Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms." Journal of Critical Reviews 7 (2020), 2008-2022. doi:10.31838/jcr.07.17.249



MLA (The Modern Language Association) Style

Anupama B S, Kandepi Bhavani, Nirmitha M V. "Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms." Journal of Critical Reviews 7.17 (2020), 2008-2022. Print. doi:10.31838/jcr.07.17.249



APA (American Psychological Association) Style

Anupama B S, Kandepi Bhavani, Nirmitha M V (2020) Performance Analysis By Extracting Features In Network Intrusion Detection System Using Supervised Machine Learning Algorithms. Journal of Critical Reviews, 7 (17), 2008-2022. doi:10.31838/jcr.07.17.249