Develop a Hybrid Improved Residual Attention with Efficient Net with Mosquito-Cuckoo Search Optimization to Detect the Attack during Network Traffic
Develop a Hybrid Improved Residual Attention with Efficient Net with Mosquito-Cuckoo Search Optimization to Detect the Attack during Network Traffic |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-11 |
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| Year of Publication : 2024 | ||
| Author : A. Senthilkumar, L. Kathirvelkumaran, K. Kavitha, Nithyanantham Sampathkumar, S. Sureshkumar |
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| DOI : 10.14445/22315381/IJETT-V72I11P106 | ||
How to Cite?
A. Senthilkumar, L. Kathirvelkumaran, K. Kavitha, Nithyanantham Sampathkumar, S. Sureshkumar, "Develop a Hybrid Improved Residual Attention with Efficient Net with Mosquito-Cuckoo Search Optimization to Detect the Attack during Network Traffic," International Journal of Engineering Trends and Technology, vol. 72, no. 11, pp. 45-54, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I11P106
Abstract
Network attacks encompass all forms of unapproved entry into a network, encompassing any endeavor to damage and interfere with the network, usually resulting in serious consequences. The internet facilitates communication and connection, but attackers who aim to breach and harm network security and connections may also violate and endanger the integrity and confidentiality of these relationships and data transfers. The evolution of various network assaults and growth in data interchange between the devices brings the need to safeguard networks and computing devices. The hybrid Improved Residual Attention with EfficientNet (IRAEN) with Mosquito-Cuckoo Search Optimization (MCSO) is proposed for efficient feature extraction from the network traffic. The attention mechanism focuses on something in particular and notes its specific importance. IRAEN-MCSO solves the problem by extracting more features and classifying the attacks accurately. The proposed IRAEN-MCSO model obtains an accuracy of 99.63%, which is higher than the other algorithms such as Gradient-Boost Decision Tree (GBDT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), AE-RL and LSTM. Also, the specificity values 99.59%, precision 97.63%, recall 97.78%, and F1 score 97.97% are more efficient than the other existing algorithms.
Keywords
Improved residual attention with EfficientNet, Mosquito-cuckoo search optimization, Transfer learning, Feature extraction, Security, Network traffic detection, Performance measures, Machine learning.
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