An effective technique for detecting minority attacks in NIDS using deep learning and sampling approach

Harini, R.; Maheswari, N.; Ganapathy, Sannasi; Sivagami, M.

doi:10.1016/j.aej.2023.07.063

Cited by 9 publications

(3 citation statements)

References 26 publications

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“…Testing the suggested strategy in real-world settings is essential. Ensemble Boosted Trees achieve 99.1% accuracy on the NSL-KDD dataset, promising results for IoT network traffic security [32][33][34][35][36].…”

Section: Literature Reviewmentioning

confidence: 92%

“…Binary features (7,12,14,20,21,22) describe attributes with two states, while categorical features (2, 3, 4, 42) reflect qualitative variables with distinct categories. Discrete features (8,9,15,(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)43) are unique numeric variables, but continuous features (1,5,6,10,11,13,16,17,18,19) can take any real value within a range. The dataset's 'attack' label has 40 labels, categorizing attacks as revised, U2R, DoS, R2L, and probing.…”

Section: Datasetmentioning

confidence: 99%

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CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

Alrayes,

Zakariah,

Amin

et al. 2024

CMC

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Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highlyadaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label's "attack" and "level." It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy. Furthermore, maintaining operating efficiency while improving detection accuracy is the primary goal of this work. Moreover, typical NIDS examines both risky and typical behavior using a variety of techniques. On the NSL-KDD dataset, our CNN-based approach achieves an astounding 99.728% accuracy rate when paired with channel attention. Compared to previous approaches such as ensemble learning, CNN, RBM (Boltzmann machine), ANN, hybrid auto-encoders with CNN, MCNN, and ANN, and adaptive algorithms, our solution significantly improves intrusion detection performance. Moreover, the results highlight the effectiveness of our suggested method in improving intrusion detection precision, signifying a noteworthy advancement in this field. Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

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Section: Literature Reviewmentioning

confidence: 92%

Section: Datasetmentioning

confidence: 99%

CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

Alrayes,

Zakariah,

Amin

et al. 2024

CMC

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“…DL based NID methods do not rely on feature engineering due to their deep structure. As proposed in Harini, et al, (2023), an intrusion detection technology with a threelayer structure is proposed, which includes Weighted Deep Neural Network (WDNN), Long-Short Term Memory Network (LSTM), and XGBoost algorithm. At the same time, a single side selection undersampling algorithm is used to remove noise samples from most class attacks to improve the detection rate of minority class attacks.…”

Section: Related Researchmentioning

confidence: 99%

A Network Intrusion Detection Method for Information Systems Using Federated Learning and Improved Transformer

Zhou,

Wang

2023

International Journal on Semantic Web and Information Systems

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A network intrusion detection method for information systems using federated learning and improved transformer is proposed to address the problems of long detection time and low security and accuracy when analyzing massive data in most existing intrusion detection methods. Firstly, a network intrusion detection system is constructed based on a federated learning framework, and the transformer model is used as its universal detection model. Then, the dataset is divided and an improved generative adversarial network is used for data augmentation to generate a new sample set to overcome the influence of minority class samples. At the same time, the new samples are input into the transformer local model for network attack type detection and analysis. Finally, the authors aggregate the detection results of each local model and input them into the Softmax classifier to obtain the final classification prediction results.

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