HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network
Author:
Sharma Ankita1, Rani Shalli1ORCID, Sah Dipak Kumar2, Khan Zahid3, Boulila Wadii34ORCID
Affiliation:
1. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India 2. Department of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, India 3. Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia 4. RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2010, Tunisia
Abstract
The comparison of low-rank-based learning models for multi-label categorization of attacks for intrusion detection datasets is presented in this work. In particular, we investigate the performance of three low-rank-based machine learning (LR-SVM) and deep learning models (LR-CNN), (LR-CNN-MLP) for classifying intrusion detection data: Low Rank Representation (LRR) and Non-negative Low Rank Representation (NLR). We also look into how these models’ performance is affected by hyperparameter tweaking by using Guassian Bayes Optimization. The tests has been run on merging two intrusion detection datasets that are available to the public such as BoT-IoT and UNSW- NB15 and assess the models’ performance in terms of key evaluation criteria, including precision, recall, F1 score, and accuracy. Nevertheless, all three models perform noticeably better after hyperparameter modification. The selection of low-rank-based learning models and the significance of the hyperparameter tuning log for multi-label classification of intrusion detection data have been discussed in this work. A hybrid security dataset is used with low rank factorization in addition to SVM, CNN and CNN-MLP. The desired multilabel results have been obtained by considering binary and multi-class attack classification as well. Low rank CNN-MLP achieved suitable results in multilabel classification of attacks. Also, a Gaussian-based Bayesian optimization algorithm is used with CNN-MLP for hyperparametric tuning and the desired results have been achieved using c and γ for SVM and α and β for CNN and CNN-MLP on a hybrid dataset. The results show the label UDP is shared among analysis, DoS and shellcode. The accuracy of classifying UDP among three classes is 98.54%.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference46 articles.
1. Iwendi, C., Khan, S., Anajemba, J., Mittal, M., Alenezi, M., and Alazab, M. (2020). The use of ensemble models for multiple class and binary class classification for improving intrusion detection systems. Sensors, 20. 2. Intrusion detection system through advance machine learning for the internet of things networks;Saba;IT Prof.,2021 3. Churcher, A., Ullah, R., Ahmad, J., Ur Rehman, S., Masood, F., Gogate, M., Alqahtani, F., Nour, B., and Buchanan, W.J. (2021). An experimental analysis of attack classification using machine learning in IoT networks. Sensors, 21. 4. FedMicro-IDA: A federated learning and microservices-based framework for IoT data analytics;Atitallah;Internet Things,2023 5. Internet of things cyber attacks detection using machine learning;Alsamiri;Int. J. Adv. Comput. Sci. Appl.,2019
|
|