Transparent Peer Review By Scholar9
MACHINE LEARNING AND DEEP LEARNING TECHNIQUES FOR WIRELESS NETWORKS SECURITY: A SURVEY
Abstract
Ever since internet became a common medium and source of communication, the wireless networks have grown rapidly. The data transfers across multiple devices over the networks, access to multiple devices made wireless networks an important choice and high in demand. The increasing importance and applications of these networks made them highly susceptible to security attacks and threats. The traditional security methods available are not sufficient to address these ever increasing threats and attacks. In this survey we presented the drawbacks of traditional security measures, reflected on various security attacks on the wireless networks. Further we reviewed the work presented by various researchers on the efficiencies, drawbacks of various machine learning and deep learning methods used in wireless security. By conducting a systematic review of the available literature we concluded ML and DL techniques are very effective in securing wireless networks. In the process we identified the research gaps and identified areas of possible future research.
Saurabh Ashwinikumar Dave Reviewer
11 Oct 2024 05:26 PM
Approved
Relevance and Originality
The research article is highly relevant in the current landscape, as it addresses the significant and growing concern of security in wireless networks. Given the rapid expansion of internet use and the increasing sophistication of security threats, the exploration of modern techniques like ML and DL in this domain is timely. The originality of the paper is reflected in its systematic review approach, which synthesizes existing research and highlights the gaps in the current literature. To enhance originality, the authors could consider proposing innovative ML or DL frameworks specifically tailored for wireless security, based on their findings.
Methodology
The article employs a systematic review methodology to analyze existing literature on wireless network security and the effectiveness of ML and DL techniques. While the systematic approach is appropriate, the authors should clearly outline the criteria for selecting studies included in the review, such as publication dates, relevance, or methodological rigor. This transparency would strengthen the methodology section. Additionally, discussing the specific databases searched and the search terms used would provide a clearer understanding of the review process and the comprehensiveness of the literature surveyed.
Validity & Reliability
The conclusions drawn in the article about the effectiveness of ML and DL techniques in enhancing wireless network security appear valid, supported by a review of relevant studies. However, the reliability of the findings could be improved by ensuring that a diverse range of studies are included, representing various methods and applications. Additionally, discussing potential biases in the selected literature and how they may affect the conclusions drawn would add to the reliability of the research. Including empirical data or specific examples from the reviewed studies would further substantiate the claims made.
Clarity and Structure
The article is generally well-structured, with a clear progression from the introduction of the problem to the analysis of existing research. However, certain sections may benefit from more detailed subheadings and clearer distinctions between the various types of attacks and security measures discussed. Simplifying complex technical language and providing definitions for key terms would enhance accessibility for readers unfamiliar with the topic. Visual aids, such as tables or figures summarizing key findings, could also improve clarity and help readers grasp the main points more effectively.
Result Analysis
The analysis presented in the article successfully identifies the strengths of ML and DL techniques in securing wireless networks while also acknowledging the limitations of traditional security methods. However, a more detailed discussion of specific case studies or examples of successful applications of ML and DL in wireless security would enrich the analysis. Furthermore, the identification of research gaps is a valuable contribution, but the paper could benefit from proposing concrete future research directions based on these gaps. This would provide a clearer roadmap for researchers and practitioners interested in advancing security measures in wireless networks.
IJ Publication Publisher
done sir
Saurabh Ashwinikumar Dave Reviewer