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Title: :  Malicious Attack Detector
PaperId: :  3712
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 3 Issue 1 2017
DUI:    16.0415/IJARIIE-3712
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
N.JayakanthanKumaraguru College of Technology
Dr.ManikantanKumaraguru College of Technology

Abstract

Computer Science and Engineering
Malware, Classifier, SVM
The taint URLs guide the client to suspicious websites which collects the users invaluable information and exploit their system. Here we propose an authentication based approach to detect such websites. We developed a tool called Analyzer an enhanced browser prevent the user from malicious attack. It analyze the website using EWLSVM (Ehnanced Weighted Least Square Twin Support Vector Machine) machine learning algorithm to find the website is malicious or not. The proposed approach is compared with existing approaches which reports low false positive and false negative. The experimental approach shows the proposed approach correctly detects all phishing and genuine website without any false positive and negatives. It overcomes many drawback of the existing signature based approaches

Citations

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IJARIIE N.Jayakanthan, and Dr.Manikantan. "Malicious Attack Detector" International Journal Of Advance Research And Innovative Ideas In Education Volume 3 Issue 1 2017 Page 557-560
MLA N.Jayakanthan, and Dr.Manikantan. "Malicious Attack Detector." International Journal Of Advance Research And Innovative Ideas In Education 3.1(2017) : 557-560.
APA N.Jayakanthan, & Dr.Manikantan. (2017). Malicious Attack Detector. International Journal Of Advance Research And Innovative Ideas In Education, 3(1), 557-560.
Chicago N.Jayakanthan, and Dr.Manikantan. "Malicious Attack Detector." International Journal Of Advance Research And Innovative Ideas In Education 3, no. 1 (2017) : 557-560.
Oxford N.Jayakanthan, and Dr.Manikantan. 'Malicious Attack Detector', International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 1, 2017, p. 557-560. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Malicious_Attack_Detector_ijariie3712.pdf (Accessed : 19 May 2025).
Harvard N.Jayakanthan, and Dr.Manikantan. (2017) 'Malicious Attack Detector', International Journal Of Advance Research And Innovative Ideas In Education, 3(1), pp. 557-560IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Malicious_Attack_Detector_ijariie3712.pdf (Accessed : 19 May 2025)
IEEE N.Jayakanthan, and Dr.Manikantan, "Malicious Attack Detector," International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 1, pp. 557-560, Jan-Feb 2017. [Online]. Available: http://ijariie.com/AdminUploadPdf/Malicious_Attack_Detector_ijariie3712.pdf [Accessed : 19 May 2025].
Turabian N.Jayakanthan, and Dr.Manikantan. "Malicious Attack Detector." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 3 number 1 (19 May 2025).
Vancouver N.Jayakanthan, and Dr.Manikantan. Malicious Attack Detector. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2017 [Cited : 19 May 2025]; 3(1) : 557-560. Available from: http://ijariie.com/AdminUploadPdf/Malicious_Attack_Detector_ijariie3712.pdf
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