Recurrent nonsymmetric deep auto encoder approach for network intrusion detection system

Yadav, Madhuri; Kalpana, R.

doi:10.1016/j.measen.2022.100527

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Another intrusion detection method is introduced in [35]. This study employs a recurrent non-symmetric deep auto-encoder (RNDAE) for unsupervised feature learning, as well as a DL classification model based on LightGBM RNDAEs.…”

Section: Related Workmentioning

confidence: 99%

DCNN: A Novel Binary and Multi-Class Network Intrusion Detection Model via Deep Convolutional Neural Network

Shebl,

Elsedimy,

Ismail

et al. 2024

Preprint

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Network security has become imperative in the context of our interconnected networks and everyday communications. Recently, many deep learning models have been proposed to tackle the problem of predicting intrusions and malicious activities in interconnected systems. However, they solely focus on binary classification and lack reporting on individual class performance in case of multi-class classification. Therefore, the need for an efficient and accurate network intrusion detection system has reached a pivotal point. In this paper, we propose a novel intelligent detection system based on convolutional neural network, namely DCNN. The proposed model can be utilized to analyze and detect attacks and intrusions in intelligent network systems. The DCNN model is applied against two benchmark datasets and compared with state-of-the-art models. Experimental results show that the proposed model improved resilience to intrusions and malicious activities for binary as well as multi-class classification. Furthermore, our DCNN model outperforms similar intrusion detection systems in terms of positive predicted value, true positive rate, F1 measure, and accuracy. The scores obtained for binary and multi-class classifications on the CICIoT2023 dataset are 99.50% and 99.25%, respectively. Additionally, for the CICIDS-2017 dataset, DCNN attains a score of 99.96% for both binary and multi-class classifications.

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Section: Related Workmentioning

confidence: 99%

DCNN: A Novel Binary and Multi-Class Network Intrusion Detection Model via Deep Convolutional Neural Network

Shebl,

Elsedimy,

Ismail

et al. 2024

Preprint

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show abstract

“…Several researchers devised the detection of intrusion in the network based on machine learning and deep learning. The deep learning (DL) methods can learn the data and make the generalization more effective while performing network intrusion detection using the raw data [14][15][16][17]. The most utilized unsupervised model for detecting intrusions in the network is the convolutional auto encoder (CAE), which offers promising performance through the learning criteria.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

2023

IJIES

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The intrusion detection system (IDS) plays an imperative role in defending the network from attacks. But, the IDS data is imbalanced, making the process complex for detecting the attacks accurately. According to these problems, this study proposes a network intrusion detection system based on an enhanced synthetic minority oversampling technique (SMOTE) and lightweight hybrid CAE (convolutional auto encoder)-ELM (extreme learning machine) model. Initially, the normalization of the original data is performed to avoid the influence of the maximum and minimum values. Secondly, an enhanced SMOTE is employed for solving the issues of the less detection rate of minority-attacks because of less training data. Here, the war strategy optimization (WSO) is incorporated with the SMOTE to design a novel WSO-based SMOTE technique for balancing the dataset. The three major steps utilized in WSO-based SMOTE are WSO based clustering, filtering and over-sampling. Then, the Information gain and fisher score based features extraction is employed for the reduction of computational complexity prior to the intrusion detection. Finally, the lightweight hybrid CAE-ELM model is executed for attack detection. CAEs are one of the most commonly used learning models because of their ability to construct a higher-level feature representation from the input data. Another model used in intrusion detection is the ELM, which provides an acceptable discrimination performance as well as a fast speed of learning. The performance of the proposed NIDS model is tested on the two benchmark datasets and achieved better accuracies of 99.21% and 99.15% on the UNSW-NB15 and CSE-CIC-IDS2018 datasets.

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Deep learning based network intrusion detection system: a systematic literature review and future scopes

Yogesh,

Goyal

2024

Int. J. Inf. Secur.

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Recurrent nonsymmetric deep auto encoder approach for network intrusion detection system

Cited by 8 publications

References 15 publications

DCNN: A Novel Binary and Multi-Class Network Intrusion Detection Model via Deep Convolutional Neural Network

DCNN: A Novel Binary and Multi-Class Network Intrusion Detection Model via Deep Convolutional Neural Network

Lightweight Hybrid CAE-ELM and Enhanced Smote Based Intrusion Detection for Networks

Deep learning based network intrusion detection system: a systematic literature review and future scopes

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