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Advanced Abnormal Logins Detection System using ML Algorithms
Alowlaqi, Habeba Almufadda, Flowra
Alowlaqi, Habeba
Almufadda, Flowra
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Abstract
In recent years, technology and computers have evolved rapidly, allowing tasks to be performed with minimal complexity and effort, but it has also led to an increase in cyber-attacks, such as data breaches and identity theft. This highlights the necessity of effective methods for detecting abnormal logins and protecting sensitive information. The objective of this study is to develop an advanced abnormal logins detection system using machine learning algorithms. To address the limitations of existing approaches, the proposed algorithm incorporates additional contextual information and adopts a hybrid approach that combines machine learning and network analysis. The CERT r4.2 dataset is utilized for evaluation purposes. The performance of the developed algorithm is assessed through experiments, showcasing a high accuracy rate and effective detection of various insider threat scenarios. Comparative analysis demonstrates its superiority over existing approaches, underscoring its potential in enhancing security measures in web applications. Furthermore, this project’s findings highlight its significance in safeguarding sensitive data, mitigating identity theft risks, and promoting resilient infrastructure. The project aligns with the United Nations Sustainable Development Goals, specifically SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities), as well as the Saudi Vision 2030. In conclusion, the advanced abnormal logins detection system showcases the potential of machine learning algorithms and hybrid methodologies in addressing cybersecurity challenges. The project’s implications extend to enhancing data protection, mitigating identity theft risks, and contributing to the sustainable development goals outlined by international and national initiatives.