Network Intrusion Detection System Using Deep Learning Method with KDD Cup'99 Dataset

Tanimu, Jesse Jeremiah; Hamada, Mohamed; Robert, Patience; Mahendran, Anand

doi:10.1109/mcsoc57363.2022.00047

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Using fewer computer resources, they effectively detected threats using a hybrid principal component analysis technique, and analyzed the NSL-KDD dataset using various ML techniques for IDS. The authors in [9] developed a novel model for IDS by combining the various categorization capabilities of NN and fuzzy logic. Adaptive IDS using naive Bayesian and boosted classifiers was proposed as a new learning method [10].…”

Section: Literature Reviewmentioning

confidence: 99%

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Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

Zakariah,

AlQahtani,

Alawwad

et al. 2023

CMC

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Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based traffic. By consuming time and resources, intrusive traffic hampers the efficient operation of network infrastructure. An effective strategy for preventing, detecting, and mitigating intrusion incidents will increase productivity. A crucial element of secure network traffic is Intrusion Detection System (IDS). An IDS system may be host-based or network-based to monitor intrusive network activity. Finding unusual internet traffic has become a severe security risk for intelligent devices. These systems are negatively impacted by several attacks, which are slowing computation. In addition, networked communication anomalies and breaches must be detected using Machine Learning (ML). This paper uses the NSL-KDD data set to propose a novel IDS based on Artificial Neural Networks (ANNs). As a result, the ML model generalizes sufficiently to perform well on untried data. The NSL-KDD dataset shall be utilized for both training and testing. In this paper, we present a custom ANN model architecture using the Keras open-source software package. The specific arrangement of nodes and layers, along with the activation functions, enhances the model's ability to capture intricate patterns in network data. The performance of the ANN is carefully tested and evaluated, resulting in the identification of a maximum detection accuracy of 97.5%. We thoroughly compared our suggested model to industry-recognized benchmark methods, such as decision classifier combinations and ML classifiers like k-Nearest Neighbors (KNN), Deep Learning (DL), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and ANN. It is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all evaluations. This result underlines the effectiveness of the suggested methodology by demonstrating the ANN's capacity to accurately assess the effectiveness of the developed strategy in identifying and categorizing instances of network intrusion.

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Section: Literature Reviewmentioning

confidence: 99%

“…The authors of [7] presented ANN-based threat discovery using the UNSW15 dataset to address verification issues in IoT contexts and achieved 84 percent detection accuracy. Nwakanma et al [9] represented two ANN models using the NSL-KDD dataset and discovered identification accuracy of 93.98% and 84.05%, respectively.…”

Section: Introductionmentioning

confidence: 99%