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Title	Sdn-Enabled Intrusion Detection System Using Machine Learning And Neural Network Schemes
ID_Doc	47485
Authors	Tawfik A.T.; Abdullah S.H.; Nori A.S.; Rakha M.A.
Year	2025
Published	Smart Applications of Artificial Intelligence and Big Data
DOI	http://dx.doi.org/10.1201/9781032664293-23
Abstract	Recently, software-defined network (SDN) has been an emerging technology where security is still an open challenge. New techniques and developments are growing rapidly, but traditional algorithms do not provide efficient solutions. These days, deep learning, machine learning, and artificial intelligence techniques are put out to offer improved cybersecurity solutions. SDN has many applications in civil and military fields. However, SDN can be applied in many fields like the smart grid, health, farming, transportation, and smart cities. The intrusion detection system (IDS) in SDN based on AI, ML, and neural network techniques is the focus of this chapter. During simulation, two benchmark datasets CICIDS2017 and NF-ToN-IoT are used. Random forest achieves better results in comparison with other classifiers. More interestingly, this chapter evaluates the performance of around ten popular machine learning, neural network, and optimization techniques using two advanced datasets. Using random forest, gradient boosting, long short-term memory, k-neighbors, multi-layer perceptrons, linear and quadratic discriminant analysis, stochastic gradient descent (SGD), Naïve Bayes multinomial, Naïve Bayes Gaussian, and gradient boosting, a thorough comparative analysis is carried out. Performance evaluation encompasses metrics such as recall, accuracy, precision, F1-score, confusion matrix, and correlation matrix. In addition, we have assessed overall performance using two publicly available datasets: CICIDS2017 and NF-ToN-IoT. © CRC Press 2025. All rights reserved.
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