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IJSDR
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: March 2024

Volume 9 | Issue 3

Impact factor: 8.15

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Paper Title: Detection of Cyber Attacks Using Artificial Intelligence
Authors Name: V. Yamuna , P. Harika , S.K. Javeed , S. Manindra , V. Saradhi
Unique Id: IJSDR2304174
Published In: Volume 8 Issue 4, April-2023
Abstract: Cyber-Physical Systems have made momentous evolution in many effective applications cause of the integration between physical entities, computational resources, and communication potentialities. Although, cyber-attacks are dominant intimidation to these systems. Cyber-attacks occur brilliantly and stealthy. A few of these attacks which are specifically called malware, deception attacks, Denial-of-service (DoS) attacks and also by compromising with some cyber components, manipulate data, or entering false information into the system. If the system is oblivious of the existence of those attacks, systems were unable to detect them, and performance of the systems may be disrupted or disabled completely. Hence, it is decisive to adapt algorithms to pinpoint these kind of attacks in these systems. It should be remembered that the information generated in these systems is produced in very large number, with so much variety, and high speed, thus it is essential to use machine learning algorithms to ease the analysis and evaluation of data and to identify hidden patterns. The proposed method in this study is to use various machine learning algorithms such as Support Vector, Decision Tree, Random Forest, Extra Tree Classifier, ad boost and Neural networks, Gradient boosting, K-means clustering, and Logistic regression techniques. After uploading the dataset, pre-processing by using will be done on the unstructured data to convert it to the structured data and then data is trained using the algorithms. Based on the accuracy of these algorithms, the cyber-attack is detected.
Keywords: Artificial Intelligence, Extra Trees Classifier, Cyber-Attack, Gradient Boosting, Random Forest Classifier.
Cite Article: "Detection of Cyber Attacks Using Artificial Intelligence", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.1048 - 1051, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304174.pdf
Downloads: 000336257
Publication Details: Published Paper ID: IJSDR2304174
Registration ID:205278
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 1048 - 1051
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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