Review of AI driven Intrusion Detection System on Network based attacks

Approved

Relevance and Originality:

This review paper addresses a significant and timely topic in cybersecurity by exploring the integration of Artificial Intelligence (AI) in Intrusion Detection Systems (IDS). As cyber threats become increasingly sophisticated, the relevance of incorporating AI to enhance the effectiveness and efficiency of IDS is paramount. The originality of the work lies in its comprehensive examination of both traditional IDS methods and advanced AI techniques, such as machine learning and deep learning. This broad perspective not only highlights the transformative impact of AI on IDS but also emphasizes the potential for innovative solutions in combating cyber threats, making it a valuable contribution to the field.

Methodology:

The methodology of this paper is robust, utilizing a systematic review approach to analyze the evolution of IDS and the role of AI in enhancing these systems. By comparing various AI-based techniques with traditional methods, the paper provides a thorough assessment of strengths and weaknesses across different cybersecurity contexts. However, the paper could benefit from more explicit criteria for the selection of studies included in the review, as well as a clearer description of how the comparative analysis was conducted. Providing a framework for categorizing the various AI methods would enhance clarity and allow readers to better understand the nuances of each approach.

Validity & Reliability:

The validity of the paper's findings is supported by its comprehensive coverage of AI applications in IDS. The analysis of various techniques, including their strengths and weaknesses, offers a balanced view of the topic. To improve reliability, the paper could include empirical data or case studies demonstrating the effectiveness of AI-enhanced IDS in real-world scenarios. Additionally, discussing any biases or limitations in the reviewed studies would strengthen the overall credibility of the findings. Addressing the challenges faced in implementing AI techniques within IDS could also enhance the paper’s robustness.

Clarity and Structure:

The paper is well-structured, with a logical flow that guides the reader through the evolution of IDS and the impact of AI on these systems. Clear headings and subheadings facilitate navigation and comprehension. However, some technical jargon related to AI and IDS could be defined or simplified for readers who may not have a background in cybersecurity. Summarizing key points at the end of each section would also reinforce understanding and help emphasize the main findings.

Result Analysis:

While the paper provides a thorough overview of the integration of AI in IDS, the analysis of results could be enhanced by incorporating specific examples or case studies demonstrating successful implementations of AI-based techniques. Discussing the practical implications of these technologies, including potential challenges and limitations in real-world applications, would provide a more comprehensive understanding of the topic. Furthermore, highlighting future research directions with specific recommendations for the advancement of AI in IDS could inspire further exploration and innovation in this critical area of cybersecurity.