A Generalizable Deep Neural Network Method for Detecting Attacks in Industrial Cyber-Physical Systems

Sakhnini, Jacob; Dehghantanha, Ali; Karimipour, Hadis; Yazdinejad, Abbas; Gadekallu, Thippa Reddy; Victor, Nancy; Islam, Anik

doi:10.1109/jsyst.2023.3286375

Cited by 20 publications

(3 citation statements)

References 31 publications

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“…Compute the cost of monitoring nodes and select monitoring nodes by selecting monitoring nodes algorithm for stealing complex network attacks [ 15 ]. In the stealing complex network, the network topology connection is firstly analyzed, which is the carrier of the monitoring node of network attack, namely: …”

Section: Stealing Complex Network Attack Monitoring Considering Secur...mentioning

confidence: 99%

Stealing complex network attack detection method considering security situation awareness

Xi,

Liu,

Hou

et al. 2024

PLoS ONE

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Tracking and detection have brought great challenges to network security. Therefore, this paper proposes a monitoring method of stealthy complex network attacks considering security situation awareness. By constructing a tracking model of invisible complex network attacks, public monitoring nodes are selected for monitoring. The cost of a single monitoring node is calculated by the algorithm, and the monitoring node is determined by the monitoring node algorithm, so as to reduce the resource occupancy rate of the monitoring node and improve the monitoring accuracy. The simulation results show that this method is stable in the range of 1000 to 4000 nodes, and can effectively monitor the complex network attacks of stealing secrets.

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Section: Stealing Complex Network Attack Monitoring Considering Secur...mentioning

confidence: 99%

Stealing complex network attack detection method considering security situation awareness

Xi,

Liu,

Hou

et al. 2024

PLoS ONE

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“…The various challenges and limitations of large language models are shown in the figure 3. Bias and Ethical Concerns: One of the most prominent issues associated with LLMs is the danger of replicating and amplifying biases present in their training data [38], [39]. Since these models are often trained on data from the internet, which can include text that is sexist, racist, or otherwise offensive, there is a significant risk of these models generating biased or harmful content.…”

Section: Challenges and Limitationsmentioning

confidence: 99%

Large Language Models Training, Challenges, Applications, and Development

Taghandiki,

Mohammadi

2024

Preprint

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“…Federated learning has been used for network data security [4,30], healthcare data security [31], game theory [32], vehicular data [33,34], and smart city applications [35]. Studies have found that data poisoning attacks and other cyberattacks can also compromise this technique [36][37][38][39]. Furthermore, traditional machine learning approaches have a limited processing complexity and need help discovering complicated non-linear relationships in large datasets.…”

Section: Introductionmentioning

confidence: 99%

A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection

Bibi,

Sampedro,

Almadhor

et al. 2023

Technologies

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Given the increasing frequency of network attacks, there is an urgent need for more effective network security measures. While traditional approaches such as firewalls and data encryption have been implemented, there is still room for improvement in their effectiveness. To effectively address this concern, it is essential to integrate Artificial Intelligence (AI)-based solutions into historical methods. However, AI-driven approaches often encounter challenges, including lower detection rates and the complexity of feature engineering requirements. Finding solutions to overcome these hurdles is critical for enhancing the effectiveness of intrusion detection systems. This research paper introduces a deep learning-based approach for network intrusion detection to overcome these challenges. The proposed approach utilizes various classification algorithms, including the AutoEncoder (AE), Long-short-term-memory (LSTM), Multi-Layer Perceptron (MLP), Linear Support Vector Machine (L-SVM), Quantum Support Vector Machine (Q-SVM), Linear Discriminant Analysis (LDA), and Quadratic Discriminant Analysis (QDA). To validate the effectiveness of the proposed approach, three datasets, namely IOT23, CICIDS2017, and NSL KDD, are used for experimentation. The results demonstrate impressive accuracy, particularly with the LSTM algorithm, achieving a 97.7% accuracy rate on the NSL KDD dataset, 99% accuracy rate on the CICIDS2017 dataset, and 98.7% accuracy on the IOT23 dataset. These findings highlight the potential of deep learning algorithms in enhancing network intrusion detection. By providing network administrators with robust security measures for accurate and timely intrusion detection, the proposed approach contributes to network safety and helps mitigate the impact of network attacks.

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A Generalizable Deep Neural Network Method for Detecting Attacks in Industrial Cyber-Physical Systems

Cited by 20 publications

References 31 publications

Stealing complex network attack detection method considering security situation awareness

Stealing complex network attack detection method considering security situation awareness

Large Language Models Training, Challenges, Applications, and Development

A Hypertuned Lightweight and Scalable LSTM Model for Hybrid Network Intrusion Detection

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