|
Abstract
|
The research presents a detailed investigation into enhancing cybersecurity through machine learning (ML). It highlights the increasing complexity and sophistication of cyber threats such as malware, phishing, and advanced persistent threats (APTs), which often bypass traditional rule-based systems. Traditional techniques struggle to adapt to novel threats, necessitating the development of more flexible and intelligent systems. The study emphasizes the capabilities of ML in identifying subtle, previously unknown anomalies in large datasets. It explores various ML models including supervised (e.g., Decision Trees, Random Forests), unsupervised (e.g., K-means, Isolation Forest), semi-supervised, and deep learning methods (e.g., LSTM, CNN), evaluating their effectiveness in detecting anomalies in different environments like IoT and industrial control systems. The proposal also addresses ethical challenges such as data privacy and algorithmic bias, recommending the use of anonymization and fairness audits to mitigate these concerns.
The research adopts a mixed-methods approach combining quantitative evaluation of ML algorithms with qualitative analysis of ethical and practical considerations. Publicly available datasets like CICIDS-2017, UNSW-NB15, and ICS Cyberattack datasets will be used for model training and testing. The proposal underscores the importance of balancing human oversight with machine automation to enhance decision-making. The expected contributions include identifying the most effective ML algorithms for cybersecurity, developing hybrid human-in-the-loop systems, and improving real-time threat detection capabilities. Through comparative analysis, the study aims to demonstrate that hybrid and ML-based approaches outperform traditional techniques in accuracy, adaptability, and resilience. This research stands to make significant advancements in securing digital infrastructures by optimizing ML for anomaly detection, contributing to both academic literatu
|