Advancing Cybersecurity

Abstract

This chapter presents an innovative approach to cybersecurity by applying anomaly detection techniques to network and system data. The study uses a comprehensive dataset from simulated network environments to analyze various attack scenarios and evaluate classification algorithms. The approach uses an ensemble model to achieve superior detection accuracy and integrates feature importance analysis. The findings show that the proposed anomaly detection framework not only identifies known attack types but also detects novel threats, underscoring its potential as a pivotal tool in cybersecurity. This research paves the way for a new era in cybersecurity. These findings reveal that the proposed anomaly detection framework not only achieves high accuracy in identifying known attack types but also exhibits robustness in detecting novel threats, thereby underscoring its potential as a pivotal tool in the cybersecurity arsenal. This chapter advocates for a paradigm shift towards proactive threat identification, emphasizing the critical role of anomaly detection in fortifying network defenses against the ever-increasing sophistication of cyber-attacks.