Network Intrusion Detection Based on Conditional Wasserstein Generative Adversarial Network and Cost-Sensitive Stacked Autoencoder

Zhang, Guoling; Wang, Xiaodan; Li, Rui; Song, Yafei; He, Jiaxing; Lai, Jie

doi:10.1109/access.2020.3031892

Cited by 29 publications

(9 citation statements)

References 31 publications

(43 reference statements)

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“…For instance, SHIDS [36] used ISCX-UNB dataset for intrusion detection, hence it detects only trained anomalies using of single dataset it does not detect real-time anomalies because the attack behavior is changed frequently, but trained anomalies store only limited behavior thus leads to poor accuracy and less F-measure value. Table 9 illustrates the numerical analysis of F-score which represent the average value of F-score [42], [43]. The numerical analysis proved that the proposed 3LIDS-CGAN model achieves high F-score compared to existing models.…”

Section: Impact Of F-scorementioning

confidence: 96%

Three level intrusion detection system based on conditional generative adversarial network

Abdulameer¹,

Musa²,

Salim

2023

IJECE

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<span lang="EN-US">Security threat protection is important in the internet of things (IoT) applications since both the connected device and the captured data can be hacked or hijacked or both at the same time. To tackle the above-mentioned problem, we proposed three-level intrusion detection system conditional generative adversarial network (3LIDS-CGAN) model which includes four phases such as first-level intrusion detection system (IDS), second-level IDS, third-level IDS, and attack type classification. In first-level IDS, features of the incoming packets are extracted by the firewall. Based on the extracted features the packets are classified into three classes such as normal, malicious, and suspicious using support vector machine and golden eagle optimization. Suspicious packets are forwarded to the second-level IDS which classified the suspicious packets as normal or malicious. Here, signature-based intrusions are detected using attack history information, and anomaly-based intrusions are detected using event-based semantic mapping. In third-level IDS, adversary packets are detected using CGAN which automatically learns the adversarial environment and detects adversary packets accurately. Finally, proximal policy optimization is proposed to detect the attack type. Experiments are conducted using the NS-3.26 network simulator and performance is evaluated by various performance metrics which results that the proposed 3LIDS-CGAN model outperforming other existing works.</span>

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Section: Impact Of F-scorementioning

confidence: 96%

Three level intrusion detection system based on conditional generative adversarial network

Abdulameer¹,

Musa²,

Salim

2023

IJECE

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“…Compared with the above methods, the accuracy, accuracy, and F1 of CWGAN-CSSAE in this model are improved by 3.63%, 1.31%, and 1.34%, respectively. Therefore, the detection model proposed in this paper has good detection and recognition performance [7][8] .…”

Section: Compare With Other Modelsmentioning

confidence: 97%

The application of Internet of Things in intrusion detection model

Zhang

Zhu

2023

3rd International Conference on Internet of Things and Smart City (IoTSC 2023）

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With the continuous development of 5G technology and big data processing technology, the Internet of Things has been applied to every aspect of human life. At the same time, due to the deployment of a large number of intelligent terminal equipment, massive Internet of Things traffic is generated and sent to the cloud platform storage. In the Internet of Things network environment, there are many high-dimensional, complex data characteristics. These characteristics lead to the unsatisfactory application effect of traditional intrusion detection technology in IoT security protection. Therefore, establishing a complete IoT intrusion detection system has become essential to ensuring IoT security. Since deep learning has powerful data processing and feature learning capabilities, this paper uses deep learning technology to build the Internet of Things intrusion detection model to provide the regular operation of the Internet of Things. The main research work and innovation are aimed at the problems that the sparse stack encoder (SSAE) model is challenging to learn practical features and slow convergence due to its low sparsity in dimensionality reduction of high-dimensional data in the Internet of Things. This paper improves the SSAE model and proposes a BR-SSAE model.

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“…In [15], the LSTM and Autoencoder approach classify the attacks. In [16], an Adversarial autoencoder neural network is used for NIDS with the combination of GANs and various auto-encoders. GAN consists of two networks generator and a discriminator.…”

Section: Related Workmentioning

confidence: 99%

An IoT Environment Based Framework for Intelligent Intrusion Detection

Safwan¹,

Iqbal²,

Amin³

et al. 2023

Computers, Materials &Amp; Continua

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Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on Software Defined Networks is used in SDN. To obtain the best and most relevant features, a feature selection technique is used. Several experiments have revealed that the proposed solution is a superior method for detecting flow-based anomalies. The performance of our proposed model is also measured in terms of accuracy, recall, and precision. F1 rating and detection time Furthermore, a lightweight model for training is proposed, which selects fewer features while maintaining the model's performance. Experiments show that the adopted methodology outperforms existing models.

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Network Intrusion Detection Based on Conditional Wasserstein Generative Adversarial Network and Cost-Sensitive Stacked Autoencoder

Cited by 29 publications

References 31 publications

Three level intrusion detection system based on conditional generative adversarial network

Three level intrusion detection system based on conditional generative adversarial network

The application of Internet of Things in intrusion detection model

An IoT Environment Based Framework for Intelligent Intrusion Detection

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