Enhancing Cybersecurity Through Adaptive Anomaly Detection Using Modern AI Techniques - Verma, Dhruv (2024)
This thesis explores how artificial intelligence (AI) techniques and machine learning (ML) algorithms can enhance adaptive anomaly detection frameworks while aiming to develop effective strategies for identifying and mitigating evolving cyberthreats. The main objective of this study is to create a unified framework that reduces manual inter-vention, minimizes false positives, and offers a robust and resilient approach to threat mitigation. To collect datasets for analyzing the behavior of cyberthreats, a number of cyber-attack simulations, including threats such as - malware, data breaches, and SQL injections, were created. Additionally, malicious datasets from Kaggle were utilized to provide a larger amount of data. After normalizing the datasets, several ML algorithms were utilized to train the data and establish a threshold mechanism. This mechanism dynamically adjusts parameters corresponding to specific cyberthreats, ensuring accurate identification and mitigation. This study demonstrates how modern AI techniques can transform anomaly detection, making it more effective, time-efficient, and resource-friendly.