Intrusion detection model using gene expression programming to optimize parameters of convolutional neural network for energy internet

Deng, Song; Yuan, Xinya; Li, Qianliang; Zhang, Jie; Sun, Mengfei; Fu, Xiong; Yang, Lin

doi:10.1016/j.asoc.2022.109960

Cited by 11 publications

(2 citation statements)

References 41 publications

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“…Thus, the dimensionality of the data features needs to be reduced further.KNN + XGBoost [ 64 ] combines the XGBoost algorithm with KNN, which achieves satisfactory results in some cases, but the performance may be degraded in multiclassification prediction. GCNN [ 65 ] optimizes intrusion detection models by improving traditional CNN structures such as Gene Expression Programming (GEP) and fitness functions. Despite some progress, there is still room for improvement in detection accuracy.…”

Section: Resultsmentioning

confidence: 99%

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

Zuo,

Zhang,

et al. 2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

Zuo,

Zhang,

et al. 2024

Heliyon

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“…Networks like CNN have a set of parameters in intrusion detection, and if these parameters are not chosen optimally, then the modeling error increases. In other words, by optimizing deep learning parameters, attacks are detected with less error [28,29]. Another challenge in detecting attacks in SDN networks is that data such as blacklists is exchanged between network switches with methods such as blockchain [30].…”

Section: Introductionmentioning

confidence: 99%

Detection of DDoS attacks in SDN with Siberian Tiger Optimization algorithm and deep learning

Al-Sarray,

Rahebi,

Demirhan

2024

Preprint

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The Software-defined Networking (SDN) system plays a crucial role in efficiently overseeing the Internet network by segregating the control and data planes. In the SDN, the controller manages and determines the policy in sending data and setting the SDN switches. Despite the significant advantages, the SDN network has security challenges. DDoS attacks are the main challenge in SDN networks. DDoS attacks primarily target the SDN controller to disrupt network performance. Intrusion detection systems in SDN networks need confidential methods for message exchange and coordination of controllers so that they can exchange the blacklist of attacking addresses with each other. In this manuscript, we introduce an approach utilizing 1D CNN and LSTM networks for detecting attacks in the SDN network, incorporating blacklist information hidden in images. In the first stage, game theory and deep learning based on GAN are used to increase attack detection accuracy in each SDN controller to balance the data set. In the second stage, each controller uses 1D CNN to extract the primary features, and the Siberian tiger optimization (STO) algorithm is applied to enhance the efficiency of this deep learning network. In the third step, the STO algorithm selects the optimal features. Finally, the LSTM network classifies traffic by receiving the selected features. SDN controllers use image encryption to increase privacy and security for exchanging and sharing blacklists. The tests performed in Python and on datasets UNSW-NB15, CIC-IDS2017, and NSL-KDD are 99.49%, 99.86%, and 99.91%. The proposed method GAN-CL-STO demonstrates higher accuracy in detecting network attacks compared to the CNN-LSTM, HODNN+CRF, 1D CNN, PSO-1D CNN, and 1D CNN+BiLSTM methods. The suggested approach for identifying attacks in SDN is more accurate than WOA, HHO, and COA feature selection methods.

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An efficient DDoS attack detection mechanism in SDN environment

Hnamte

Hussain

2023

Int. j. inf. tecnol.

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As cyberattacks get more sophisticated, it becomes more difficult to identify advanced attacks in a wide variety of industries, including business, national defense, and healthcare. Traditional intrusion detection systems are insufficient to identify these sophisticated attempts with unpredictable patterns. Attackers evade recognized signatures and spoof legitimate users. While Software-Defined Network (SDN) provides greater innovation to the design of future networks, it is also more susceptible to network attacks. We present a system for the detection and protection of network attacks in the SDN environment using Deep Learning (DL) for addressing challenges. We use InSDN, the most recent IDS dataset specially developed for the SDN environment. InSDN includes sophisticated DoS and network attacks and, it is a publicly accessible dataset. Additionally, we compare the model performance trained using CIC-IDS2017 and CIC-DDoS2019 datasets respectively. We focus on the DDoS attack category in this study and create an intrusion detection DL model for SDN. We construct our model using a Convolutional Neural Network (CNN) and assess its performance. Additionally, we propose the optimized CNN design for improved performance based on various evaluations.

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Intrusion detection model using gene expression programming to optimize parameters of convolutional neural network for energy internet

Cited by 11 publications

References 41 publications

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

GSOOA-1DDRSN: Network traffic anomaly detection based on deep residual shrinkage networks

Detection of DDoS attacks in SDN with Siberian Tiger Optimization algorithm and deep learning

An efficient DDoS attack detection mechanism in SDN environment

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