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All Journal International Journal of Electrical and Computer Engineering
Nuggehalli Kasturirangan Srinath
Rashtreeya Vidyalaya College of Engineering
Computer Science & IT Electrical & Electronics Engineering

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Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset Karthikayini Thavasimani; Nuggehalli Kasturirangan Srinath
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4031-4041

Abstract

Hyperparameter optimization is one of the main challenges in deep learning despite its successful exploration in many areas such as image classification, speech recognition, natural language processing, and fraud detections. Hyperparameters are critical as they control the learning rate of a model and should be tuned to improve performance. Tuning the hyperparameters manually with default values is a challenging and time-intensive task. Though the time and efforts spent on tuning the hyperparameters are decreasing, it is always a burden when it comes to a new dataset or solving a new task or improving the existing model. In our paper, we propose a custom genetic algorithm to auto-tune the hyperparameters of the deep learning sequential model to classify benign and malicious traffic from internet of things-23 dataset captured by Czech Technical University, Czech Republic. The dataset is a collection of 30.85 million records of malicious and benign traffic. The experimental results show a promising outcome of 98.9% accuracy.
Co-Authors Karthikayini Thavasimani