Combating Network Intrusions using Machine Learning Techniques with Multilevel Feature Selection Method

Olayinka, Tosin C.; Ugwu, Chukwuemeka Christian; Okhuoya, Omoibu Joseph; Adetunmbi, Adebayo Olusola; Popoọla, Olugbemiga Solomon

doi:10.1109/nigercon54645.2022.9803098

Cited by 2 publications

(2 citation statements)

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“…Over the past decade, researchers have proposed various ML- [19] and DL-based techniques to improve the efficacy of NIDS in identifying malicious threats. Nevertheless, the significant increase in network traffic and associated attack vectors has created challenges for NIDS systems to identify malicious intrusions.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, the significant increase in network traffic and associated attack vectors has created challenges for NIDS systems to identify malicious intrusions. Tosin et al [19] applied four machine learning models in parallel, K-Nearest Neighbor (K-NN), Parsimonious Bayes (NB), Logistic Regression (LR), and Artificial Neural Network (ANN) with multilevel feature selection methods, and compared multiple metrics to determine which model has the best detection capability in terms of accuracy. Desale et al [20] used the Genetic Algorithm (GA) to optimize a feature analysis, reducing the number of features selected and the cost of feature maintenance.…”

Section: Related Workmentioning

confidence: 99%

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A Combined Multi-Classification Network Intrusion Detection System Based on Feature Selection and Neural Network Improvement

Wang¹,

Liu

Zheng

et al. 2023

Applied Sciences

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Feature loss in IoT scenarios is a common problem. This situation poses a greater challenge in terms of real-time and accuracy for the security of intelligent edge computing systems, which also includes network security intrusion detection systems (NIDS). Losing some packet information can easily confuse NIDS and cause an oversight of security systems. We propose a novel network intrusion detection framework based on an improved neural network. The new framework uses 23 subframes and a mixer for multi-classification work, which improves the parallelism of NIDS and is more adaptable to edge networks. We also incorporate the K-Nearest Neighbors (KNN) algorithm and Genetic Algorithm (GA) for feature selection, reducing parameters, communication, and memory overhead. We named the above system as Combinatorial Multi-Classification-NIDS (CM-NIDS). Experiments demonstrate that our framework can be more flexible in terms of the parameters of binary classification, has a fairly high accuracy in multi-classification, and is less affected by feature loss.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%