Machine Learning Enhances Security by Analyzing User Access Patterns and Identifying Anomalous Behavior that May Indicate Unauthorized Access Attempts

Padmaja Pulivarthy

doi:10.71097/IJAIDR.v13.i2.1439

Machine Learning Enhances Security by Analyzing User Access Patterns and Identifying Anomalous Behavior that May Indicate Unauthorized Access Attempts

Author(s)	Padmaja Pulivarthy
Country	United States
Abstract	Conventional security systems find it difficult to identify and handle advanced illegal access attempts as cyber threats keep changing in complexity and frequency. With data-driven techniques that can identify minute anomalies in user behavior patterns, the introduction of machine learning (ML) has brought a transforming approach to cybersecurity. This study investigates how machine learning methods improve security by means of user access pattern analysis and anomaly identification that can point to illegal activity like attempts at unauthorized access, insider threats, or compromised accounts. From supervised learning models like Support Vector Machines (SVM) to unsupervised techniques like Isolation Forests and deep learning methods like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM), we offer a thorough study of the several ML algorithms that help to support this process. We also underline important real-world applications of ML-driven security systems in domains including corporate IT, healthcare, and finance where access control is crucial. This work attempts to give a thorough picture of how artificial intelligence is changing the field of digital security by concentrating on both the possibilities and the constraints of machine learning in cybersecurity. Moreover, included are the difficulties related to data privacy, algorithmic transparency, scalability, and adversarial attack risk to offer a fair assessment of machine learning acceptance in access security. By means of a mix of theoretical analysis and real-world case studies, we show that although machine learning-based security systems have encouraging prospects, continuous improvement and careful application are needed to realize their full potential.
Field	Engineering
Published In	Volume 13, Issue 2, July-December 2022
Published On	2022-08-06
Cite This	Machine Learning Enhances Security by Analyzing User Access Patterns and Identifying Anomalous Behavior that May Indicate Unauthorized Access Attempts - Padmaja Pulivarthy - IJAIDR Volume 13, Issue 2, July-December 2022. DOI 10.71097/IJAIDR.v13.i2.1439
DOI	https://doi.org/10.71097/IJAIDR.v13.i2.1439
Short DOI	https://doi.org/g9m7q6

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Machine Learning Enhances Security by Analyzing User Access Patterns and Identifying Anomalous Behavior that May Indicate Unauthorized Access Attempts

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