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Detection of insider threats using user behaviour analytics

 Department:Computer Science  
 By:usericon Muhammnnad1  

 Project ID: 9341
 Rating:  (3.6) votes: 7
   Price:₦5000
Abstract
Insider threats are a major cybersecurity concern because legitimate users with authorized access can intentionally or unintentionally perform activities that compromise the confidentiality, integrity, and availability of organizational information systems. Traditional security mechanisms, such as authentication, authorization, predefined security rules, and access controls, are often designed to prevent unauthorized access but may have difficulty identifying suspicious activities performed through valid accounts. This project therefore focused on the design and implementation of an insider-threat detection system using User Behaviour Analytics (UBA). The main purpose of the study was to develop a system capable of analysing user activities, establishing normal behavioural patterns, detecting deviations from those patterns, assigning risk levels, and presenting potential threats to security administrators. The proposed system uses a structured process involving user activity collection, data preprocessing, behavioural feature extraction, behavioural profiling, anomaly detection, risk scoring, alert generation, visualization, and reporting. Relevant behavioural characteristics considered by the system include login frequency, login time, session duration, file-access frequency, network activity, data-transfer volume, and access to sensitive resources. Machine-learning techniques, particularly unsupervised anomaly-detection approaches such as Isolation Forest, One-Class Support Vector Machine, and Local Outlier Factor, were considered to identify activities that significantly differ from established behavioural patterns. The implementation of the proposed system demonstrated that User Behaviour Analytics can assist in identifying potentially suspicious activities by comparing current user behaviour with previously established behavioural baselines. The system supported important functions including data loading, preprocessing, feature extraction, behavioural profiling, anomaly detection, risk scoring, security-alert generation, dashboard visualization, reporting, and administrator authentication. ...
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