Identifying the User As Genuine/Malign Based on Search Logs and Search History
D. Satya Bhavani1, P. Rajya Lakshmi Sobha Pavani2
1D. Satya Bhavani, Assistant Professor Department of Computer Science and Engineering and Engineering Mahatma Gandhi Institute of Technology of Technology Hyderabad, India.
2P. Rajya Lakshmi Sobha Pavani, IV/IV B.Tech Department of Computer Science and Engineering and Engineering Mahatma Gandhi Institute of Technology of Technology Hyderabad, India.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2046-2048 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2752059120/2020©BEIESP | DOI: 10.35940/ijrte.A2752.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: One of the major challenges a developer may face is security issues/threats on the labelled data. The labelled data comprises of system logs, network traffic or any other enriched data with threat/not threat classification. . There were few studies which categorized the URLs to a specific category like Arts, Technology, etc. In this paper the main research is on the classification of users based on the search logs(URLs). Manually it is difficult to differentiate the user based on search logs. So, we train a machine learning model that takes raw data as input and classifies the user to genuine or malign. This model helps in intrusion detection/suspicious activity detection. For this first we gather data of past malicious URLS as training set for Naïve Bayes algorithm to detect the malicious users. By implementing KNN algorithm effectively we can detect the malign users up to an accuracy of 94.28%. With the help of Machine Learning algorithms like Naïve Bayes, KNN, Random Forest classifiers we can classify the malign and genuine users.
Keywords: URL, Malign, Naïve Bayes, KNN, Random Forest intrusion detection.
Scope of the Article: Forest Genomics and Informatics