Abstract
The most fascinating task in the present world for the creation of online applications is network security. Additionally, since the number of cyberattacks on the internet has risen, new ways for more effective attack identification and avoidance must be established. This may be accomplished by suggesting fresh intrusion methods for identification. IDSs that use machine learning techniques are precise and successful in identifying networks assaults. The presence of redundant or unimportant characteristics in a big dataset may substantially decrease the effectiveness of machine learning models, leading to a sharp decline in quality. Thus, it is imperative to come up with a suitable feature removal technique which will cut down a few of the characteristics that do not have an important effect on the technique of classification. Additionally, when the models are trained on extremely unbalanced datasets, a lot of ML-based IDSs experience a rise in error rates as well as poor detection rate. In this article, we offer a study employing the DTOHE technique along with multiple feature selection methods, including GA, ACO, and PSO. We focused on binary classification configurations in our experiments, and the outcomes showed that the DTOHE with PSO feature selection approach elevated the evaluation precision for the binary categorization scheme from 88.13 to 90.85%.
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Data Availability
The dataset produced and analyzed in this study can be obtained from the corresponding author upon request, subject to reasonable conditions.
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The authors acknowledged the Quaid-E-Millath Government College for Women (Autonomous), Chennai, Tamilnadu, India for supporting the research work by providing the facilities.
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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.
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Samraj, J., Abirami, K. Attack Detection with Optimal PSO Feature Selection with DTOHE Model. SN COMPUT. SCI. 5, 562 (2024). https://doi.org/10.1007/s42979-024-02911-4
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DOI: https://doi.org/10.1007/s42979-024-02911-4