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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19455
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
Total Countries : 144
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| Paper Title: | INTRUSION DETECTION SYSTEM FOR DOS ATTACKS USING MACHINE LEARNING |
| Authors Name: | Dr. PARDEEP KUMAR , K LAXMI KANTH REDDY , D PAVAN KUMAR NAYAK , SHAIK ISHAQ |
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IJRTI_189713
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| Published Paper Id: | IJRTI2405005 |
| Published In: | Volume 9 Issue 5, May-2024 |
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| Abstract: | This paper presents a novel approach network traffic is at risk from hackers with various passive and aggressive attacks, compromising security. A strong Intrusion Detection System is crucial for swift and accurate attack identification by analysing each packet in real-time. Utilizing machine learning enhances network security, demanding substantial data to uncover complex patterns effectively. Thus, We focus on the NSL-KDD datasets, which eliminate certain redundant and more frequent records from the 1999 KDD Cup dataset that can still be used in machine learning techniques that are the primary tools for network traffic analysis and anomaly detection. Initially, features to be extracted from network traffic are pre-processed, which often involves the use of numerous mathematical techniques like removing unnecessary or undesired features to assemble the data for a machine learning model. Then the selected features are used for training the proposed models and a binary classification technique is used for prediction of normal or attack type. Eventually, the overall performance accuracy and error rate of our model is evaluated. Thus, network traffic analysis will be used to expose invasions and forbid network attacks. |
| Keywords: | Denial Of Service Attack, Deep Learning, Intrusion Detection System, Intrusion Prevention System |
| Cite Article: | "INTRUSION DETECTION SYSTEM FOR DOS ATTACKS USING MACHINE LEARNING ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.29 - 35, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405005.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 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: IJRTI2405005
Registration ID:189713
Published In: Volume 9 Issue 5, May-2024
DOI (Digital Object Identifier):
Page No: 29 - 35 Country: Medchal, Telangana, india Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2405005 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2405005 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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