AI Vs. Traditional IDS: Comparative Analysis of Real-World Detection Capabilities

Abstract

Artificial intelligence has transformed cybersecurity, particularly intrusion detection systems. Conduct a comparative analysis of AI-driven and conventional Intrusion Detection Systems (IDS). Enhance the identification of intricate cyber intrusions. AI-driven Intrusion Detection Systems enhance detection accuracy, reduce false positives, and optimize response times with Machine Learning and Deep Learning.

Intrusion Detection Systems (IDS) identify intrusion trends in historical data utilizing decision trees, support vector machines (SVMs), and ensemble methods. These algorithms enhance IDS detection models by adapting to evolving data to identify emerging threats. RNNs and CNNs more effectively manage high-dimensional network and system logs.

The essay thoroughly contrasts AI-driven Intrusion Detection Systems with signature-based and anomaly-based Intrusion Detection Systems. Conventional Intrusion Detection Systems employ signatures and algorithms to identify threats. They identify attack patterns but not intricate assaults. AI-driven Intrusion Detection Systems identify novel attack vectors inside extensive datasets via sophisticated algorithms.

Assault scenarios and datasets evaluate actual performance in real-world contexts. AI-driven Intrusion Detection Systems identify DDoS attacks, malware, and insider risks. The efficacy of AI-powered systems is contingent upon detection accuracy, false positive rates, and response times.

Integrating AI with Intrusion Detection Systems is challenging. The essay addresses the training and validation of AI models, encompassing overfitting and the necessity of extensive, representative datasets. AI-driven Intrusion Detection Systems on extensive networks may require substantial computational resources and infrastructure, so scalability is considered. Trust and accountability in cybersecurity necessitate the interpretability and openness of AI models.

IDS monitors artificial intelligence technologies. Research indicates that a hybrid AI-based Intrusion Detection System may be more effective. It also examines how XAI enhances the interpretability of AI-powered IDS and facilitates corporate adoption.

An extensive study on AI-powered Intrusion Detection Systems reveals performance metrics, implementation obstacles, and potential opportunities. The results indicate that the transformation of cybersecurity by AI necessitates additional investigation. AI-enhanced Intrusion Detection Systems mitigate the limitations of traditional IDS to enhance cybersecurity.