Open AccessIdentifying the User As Genuine/Malign Based on Search Logs and Search History
Author(s) -
D. Satya Bhavani,
P. RajyaLakshmi Sobha Pavani
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a2752.059120
Subject(s) - computer science , naive bayes classifier , random forest , intrusion detection system , machine learning , set (abstract data type) , data mining , artificial intelligence , bayesian network , data set , information retrieval , support vector machine , programming language
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