The Role of CNN for Intrusion Detection Systems: An Improved CNN Learning Approach for SDNs

Elsayed, Mahmoud Said; Jahromi, Hamed Z.; Nazir, Mohsin; Jurcut, Anca Delia

doi:10.1007/978-3-030-78459-1_7

Cited by 18 publications

(9 citation statements)

References 26 publications

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“…According to the experimental results, CNNs perform better at detection than RNNs. Mahmoud et al [25] used two popular regularization techniques to solve the overfitting problem in IDS, resulting in improved accuracy. The InSDN dataset was used to train and evaluate the performance, and the results show that regularization can improve the accuracy of anomaly detection models using CNN.…”

Section: Related Workmentioning

confidence: 99%

An Attention-Based Convolutional Neural Network for Intrusion Detection Model

Wang

Ghaleb

2023

IEEE Access

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Network technology has had a distinctive impact on the entire human civilization and has become an important factor of production in many countries and regions. However, with the widespread popularity of network technology, security flaws have been scattered in various fields, and potential crises may break out by attackers at any time. Therefore, it is crucial to establish a traffic monitoring mechanism for network systems. Some researchers have already implemented intrusion detection models by convolutional neural networks (CNNs) combined with attention mechanisms and achieved good results. However, few attempts have been made to improve the computational efficiency of the model by organizing the appropriate image structure, and the integration of attention mechanisms could be further enhanced. In this study, an attention-based CNN intrusion detection model has been proposed. Together with the image generation methods described in this paper, an efficient and accurate processing flow is formed. To optimize the use of the feature information in the experiments, the feature fields in the experimental images were arranged according to the results of their importance analysis. And a more integrated attention mechanism has been applied to the CNN for building the detection model. A series of comparative experiments were conducted on a subset of the CSE-CIC-IDS2018 dataset, and the results show that the detection process and model proposed in this paper can swiftly complete the detection procedure while maintaining high accuracy.

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Section: Related Workmentioning

confidence: 99%

An Attention-Based Convolutional Neural Network for Intrusion Detection Model

Wang

Ghaleb

2023

IEEE Access

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“…Ref. [20] reiterated [21] by demonstrating that integrating a deep learning algorithm enhanced IDS systems. Ref.…”

Section: Hybrid Deep Learning Techniquesmentioning

confidence: 99%

Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks

Elubeyd

Yiltas-Kaplan

2023

Applied Sciences

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This paper proposes a hybrid deep learning algorithm for detecting and defending against DoS/DDoS attacks in software-defined networks (SDNs). SDNs are becoming increasingly popular due to their centralized control and flexibility, but this also makes them a target for cyberattacks. Detecting DoS/DDoS attacks in SDNs is a challenging task due to the complex nature of the network traffic. To address this problem, we developed a hybrid deep learning approach that combines three types of deep learning algorithms. Our approach achieved high accuracy rates of 99.81% and 99.88% on two different datasets, as demonstrated through both reference-based analysis and practical experiments. Our work provides a significant contribution to the field of network security, particularly in the area of SDN. The proposed algorithm has the potential to enhance the security of SDNs and prevent DoS/DDoS attacks. This is important because SDNs are becoming increasingly important in today’s network infrastructure, and protecting them from attacks is crucial to maintaining the integrity and availability of network resources. Overall, our study demonstrates the effectiveness of a hybrid deep learning approach for detecting DoS/DDoS attacks in SDNs and provides a promising direction for future research in this area.

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“…Controlling parameter values can reduce overfitting and improve model performance on unknown data. For example, the L2 regularization technique involves applying a penalty on the square of the weight coefficient values [25]. As a result, the big weights become near zero.…”

Section: Overfitting Prevention and Regularizationmentioning

confidence: 99%

Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks

et al. 2022

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The fast expansion of the Internet of Things (IoT) in the technology and communication industries necessitates a continuously updated cyber-security mechanism to keep protecting the systems' users from any possible attack that might target their data and privacy. Botnets pose a severe risk to the IoT, they use malicious nodes in order to compromise other nodes inside the network to launch several types of attacks causing service disruption. Examples of these attacks are Denial of Service (DoS), Distributed Denial of Service (DDoS), Service Scan, and OS Fingerprint. DoS and DDoS attacks are the most severe attacks in IoT launched from Botnets. Where the Botnet commands previously compromised single or multiple nodes in the network to launch network traffic towards a specific node or service. This leads to computational, power, or network bandwidth draining, which causes specific services to shutdown or behave unexpectedly. In this paper, we aim to verify the detection approach reliability when it encounters an attack that it was not trained on before. Therefore, we evaluate the performance of Convolutional Neural Networks (CNN) classifier in order to detect the malicious attack traffic especially the attacks that never reported before in the network i.e. Zero-Day attacks. Different regularization techniques i.e. L1 and L2 have been used to address the problem of overfitting and to control the complexity of the classifier. The experimental results show that using the regularization methods gives a higher performance in all the evaluation metrics compared to the standard CNN model. In addition, the enhanced CNN technique improves the capability of IDSs in detection of unseen intrusion events.

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The Role of CNN for Intrusion Detection Systems: An Improved CNN Learning Approach for SDNs

Cited by 18 publications

References 26 publications

An Attention-Based Convolutional Neural Network for Intrusion Detection Model

An Attention-Based Convolutional Neural Network for Intrusion Detection Model

Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks

Anomaly Detection Based on CNN and Regularization Techniques Against Zero-Day Attacks in IoT Networks

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