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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Network Intrusion Detection System For Feature Selection - Based Machine Learning Technique Using ANN & SVM |
| Authors Name: | Dr.A.Mummoorthy , Emani Mounika , Gaddamidi Sri Sai Charan , Gaddam Sri Pavani |
| Download E-Certificate: | Download |
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IJNRD_214842
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| Published Paper Id: | IJNRD2403193 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | This study evaluates performance of two supervised machine learning algorithms such as SVM (Support Vector Machine) and ANN (Artificial Neural Networks). Machine learning algorithms will be used to detect whether request data contains normal or attack (anomaly) signatures. Now-a-days all services are available on internet and malicious users can attack client or server machines through this internet and to avoid such attack request IDS (Network Intrusion Detection System) will be used, IDS will monitor request data and then check if its contains normal or attack signatures, if contains attack signatures then request will be dropped. IDS will be trained with all possible attacks signatures with machine learning algorithms and then generate train model, whenever new request signatures arrived then this model applied on new request to determine whether it contains normal or attack signatures. In this paper we are evaluating performance of two machine learning algorithms such as SVM and ANN and through experiment we conclude that ANN outperform existing SVM in terms of accuracy. To avoid all attacks IDS systems has developed which process each incoming request to detect such attacks and if request is coming from genuine users then only it will forward to server for processing, if request contains attack signatures then IDS will drop that request and log such request data into dataset for future detection purpose. To detect such attacks IDS will be prior train with all possible attacks signatures coming from malicious user’s request and then generate a training model. Upon receiving new request IDS will apply that request on that train model to predict it class whether request belongs to normal class or attack class. To train such models and prediction various data mining classification or prediction algorithms will be used. In this study we are evaluating performance of SVM and ANN. In this algorithms we have applied Correlation Based and Chi-Square Based feature selection algorithms to reduce dataset size, this feature selection algorithms removed irrelevant data from dataset and then used model with important features, due to this features selection algorithms dataset size will reduce and accuracy of prediction will increase. |
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| Cite Article: | "Network Intrusion Detection System For Feature Selection - Based Machine Learning Technique Using ANN & SVM", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.b877-b882, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403193.pdf |
| Downloads: | 00048 |
| 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 |
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Published Paper ID:IJNRD2403193 Registration ID: 214842 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: b877-b882 Country: Ranga Reddy, Telangana, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403193 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403193 |
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ISSN: 2456-4184
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