Residual Convolutional Network for Detecting Attacks on Intrusion Detection Systems in Smart Grid

Khoei, Tala Talaei; Wen, Hu; Kaabouch, Naima

doi:10.1109/eit53891.2022.9813983

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Tala et al [73] have also used Resnet 50 to propose an IDS system for smart grids. They used the technique of deep insight to convert the numerical datasets into images.…”

Section: Related Workmentioning

confidence: 99%

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Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Sana,

Nazir,

Yang

et al. 2024

IEEE Access

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Background and Motivation:The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Methods: Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. Proposed Approach: This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. Results: The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. Conclusion: Our findings demonstrate the potential of the proposed methods for enhanced attack and intrusion detection. This improved detection capability can be instrumental in safeguarding system integrity, identifying fraudulent activity, and optimizing system performance in IoT networks.

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“…Tala et al [73] have also used Resnet 50 to propose an IDS system for smart grids. They used the technique of deep insight to convert the numerical datasets into images.…”

Section: Related Workmentioning

confidence: 99%

“…Results showed that their proposed model provided better accuracy in the detection of denial-of-service attacks. [73] However, they did not discuss the detection of other types of attacks.…”

Section: Related Workmentioning

confidence: 99%

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Sana,

Nazir,

Yang

et al. 2024

IEEE Access

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“…This approach primarily utilizes the Balanced Iterative Reducing and Clustering utilizing the Hierarchies technique (BIRCH) for pre-clustering the anomalous network traffic data and, after examining AE, to make the identification method in unsupervised learning depends on clustering subsets. Khoei et al [17] present a CNN-based approach, a ResNEt with 50 layers. In this method, the tabular information is modified to images for enhancing the model performance.…”

Section: Related Workmentioning

confidence: 99%

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment

Almuqren,

Aljameel,

Alqahtani

et al. 2023

Sensors

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Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

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“…Deep learning techniques have also been used to detect cyber-attacks targeting smart grid infrastructure. For instance, in [115], the authors propose ensemble deep learning techniques, using deep neural network (DNN) and decision tree. The proposed model is evaluated based on the 10-fold cross validation.…”

Section: Ai-based Techniquesmentioning

confidence: 99%

Cyber-Security of Smart Grids: Attacks, Detection, Countermeasure Techniques, and Future Directions

Khoei¹,

Slimane²,

Kaabouch³

2022

Self Cite

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One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions.

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Residual Convolutional Network for Detecting Attacks on Intrusion Detection Systems in Smart Grid

Cited by 8 publications

References 21 publications

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment

Cyber-Security of Smart Grids: Attacks, Detection, Countermeasure Techniques, and Future Directions

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