Detection and Elimination of Network Anomaly in SDN using Trust Analysis

Detection and Elimination of Network Anomaly in SDN using Trust Analysis

Registration ID: IJNRD_195077

Published ID: IJNRD2305335

DOI: http://doi.one/10.1729/Journal.34207

Santhosh Rajan , Shanmugam

SDN, Random Forest Classifier, Intrusion Detection System, KNN Clustering

The necessity for networking and data exchange has dramatically increased in the modern world. Network security is necessary given the rapid development and globalization of information technology. Although they may offer some amount of security, firewalls never warn administrators of impending assaults. A trustworthy detection system is required to locate such aberrant network packet behavior in order to increase efficiency and accuracy. As a result of how quickly today's network environment is evolving, the network is constantly at risk from new sorts of attacks. Therefore, regular updates to the network administration system are required for upgrading the security level. Intrusion Detection Systems is one of the network packet monitoring systems (IDS). The proposed model was created using a machine learning approach to identify malicious network packet activity. KDD-99 dataset is utilized for that. The dataset is first normalized to reduce calculation complexity, and then further features are reduced using a Deep Neural Network technique. Only effective features can be employed for harmful behavior identification, according to the reduced features. According to the results analysis, DNN works best when choosing more than 15 features, whereas co-relation performs best when choosing less than 15. The k-mean clustering algorithm is used to accomplish data clustering after feature reduction. Deep Neural Network, are designed for classification of dataset into five attack categories i.e. DOS, U2R, R2L, Probe and Normal. As compared to some other multilevel classifier work the proposed algorithm proves its efficiency in terms of high accuracy, high detection rate and False Alarm Rate (FAR).

Published Paper   E-Certificate

"Detection and Elimination of Network Anomaly in SDN using Trust Analysis", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 5, page no.d247-d252, May-2023, Available :https://ijnrd.org/papers/IJNRD2305335.pdf

Volume 8 Issue 5, May-2023

Pages : d247-d252

Paper Reg. ID: IJNRD_195077

Published Paper Id: IJNRD2305335

Downloads: 000121130

Research Area: Computer Science & Technology 

Country: Cuddalore, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305335.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305335

DOI: http://doi.one/10.1729/Journal.34207

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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