Review of AI driven Intrusion Detection System on Network based attacks

Approved

Relevance and Originality

This review paper addresses a critical area in cybersecurity by exploring the integration of Artificial Intelligence (AI) in Intrusion Detection Systems (IDS). Given the rising sophistication of cyber threats, the relevance of this study is significant, as it discusses how AI can enhance both the effectiveness and efficiency of IDS. The exploration of traditional versus advanced AI-based techniques provides an original perspective on the evolution of IDS, which is valuable for researchers and practitioners in the field. The insights into future research directions and the potential transformative impact of AI further enhance the paper’s relevance and originality.

Methodology

The paper presents a comprehensive review methodology, synthesizing existing literature on AI integration in IDS. It effectively organizes the discussion around the evolution of IDS and provides a comparative analysis of various AI techniques, including machine learning and deep learning. However, the methodology could be enhanced by specifying the criteria for selecting the literature reviewed and the processes used for analysis. Incorporating a systematic review framework or outlining the databases searched would add transparency to the research process and strengthen the credibility of the findings.

Validity & Reliability

The validity of the paper is supported by its thorough examination of a wide range of AI methodologies and their applications in IDS. By comparing the strengths and weaknesses of these methods in different cybersecurity contexts, the paper offers valuable insights that contribute to understanding the effectiveness of AI in this domain. To enhance reliability, the paper should consider incorporating empirical data or case studies that illustrate the practical applications of AI in IDS. Discussing any limitations or challenges faced during the review process would also improve the reliability of the conclusions drawn.

Clarity and Structure

The paper is well-structured, with a clear progression from the introduction of IDS to the analysis of AI techniques. Each section logically builds upon the previous one, aiding reader comprehension. However, some technical terminology may pose challenges for readers unfamiliar with AI or cybersecurity concepts. Simplifying language where possible and providing definitions for key terms would improve accessibility. Additionally, visual elements like charts or diagrams could effectively summarize complex information and enhance the reader's understanding of the comparative analyses presented.

Result Analysis

While the paper discusses the transformative impact of AI on IDS, it could benefit from a more detailed result analysis that quantifies the improvements achieved through AI integration. Including specific metrics or outcomes related to detection rates, false positives, and system efficiency would provide concrete evidence of the effectiveness of AI-enhanced IDS. A thorough discussion of the implications of these findings for practitioners in the cybersecurity field would also enrich the analysis. Furthermore, identifying specific areas for future research based on the current findings could guide further exploration and development in the integration of AI in IDS.