Cyber attack detection in IOT-WSN devices with threat intelligence using hidden and connected layer based architectures
Abstract In this paper, cyber-attacks in IOT-WSN are detected through proposed optimized-Neural Network algorithms such as (i) Equilibrium Optimizer Neural Network (EO-NN), (ii) Particle Swarm Optimization (PSO-NN), (iii) Single Candidate Optimizer Neural Network (SCO-NN) and (iv) Single Candidate O...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-12-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-024-00722-9 |
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| Summary: | Abstract In this paper, cyber-attacks in IOT-WSN are detected through proposed optimized-Neural Network algorithms such as (i) Equilibrium Optimizer Neural Network (EO-NN), (ii) Particle Swarm Optimization (PSO-NN), (iii) Single Candidate Optimizer Neural Network (SCO-NN) and (iv) Single Candidate Optimizer Long Short-Term Memory (SCO-LSTM) with different connecting, hidden neural network layers and threat intelligence data. The proposed algorithms detect the attacker node, which frequently changes the behaviour such as attacker node/ normal node. Existing IDS system detects the attacks in WSN and unable to detect the changing behavior attacker nodes in IOT-WSN. The behaviour of attacker node changes from normal behaviour to attacker behaviour due to nodes connected to internet continuously. The classification accuracy rates of proposed SCO-LSTM algorithm without and with threat intelligence are about 99.7% and 99.89%, respectively. |
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| ISSN: | 2192-113X |