Citation

Mohana Kumar, Anoop and Emerson Raja, Joseph and Senthilpari, Chinnaiyan (2025) ADVANCED INTRUSION DETECTION AND CLASSIFICATION USING TRANSFER LEARNING WITH SQUEEZE-AND-EXCITATION NETWORK AND ADAPTIVE OPTIMIZATION IN BIG DATA. International journal of Computer Networks & Communications, 17 (6). pp. 93-113. ISSN 0975-2293

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Abstract

The rapidly growing number of inter-networked computer devices and upsurge of big data in cyber domain have made cyberattacks, particularly Denial-of-Service (DoS) attacks, a very serious threat. Traditional intrusion detection systems face scaling issues with increasing complexity of big data, while extracting local and global features causes redundancy. This research bridges this gap by integrating transfer learning and Squeeze-and-Excitation Network (SENet) within the proposed eXplainable Artificial Intelligence-driven Intrusion Detection Model (XIIDM). The feature extraction process is through a correlated univariate-elimination-based autoencoder, to preserve local and global features of input data and eliminate all redundant information. SENet further enhances representational power of proposed model by recalibrating channel-wise feature responses, leading to improved DoS classification accuracy. An adaptive partial reinforcement optimizer dynamically adjusts model parameters during training, thus optimizing precision and reducing time complexity. Moreover, incorporation of explainable artificial intelligence units makes the outcome of XIIDM transparent and accountable. The proposed XIIDM is then rigorously evaluated on five benchmark datasets: CSE-CIC-IDS2018, CIC-DDoS2019, NSL-KDD, KDD Cup-99, and UNSW-NB15, achieving 99.988% accuracy, 99.934% precision, and 99.932% recall, with 0.00014% error rate. This research further justifies the robustness and generalization capacity of the proposed model by performing k-fold cross-validation and ablation experiments, confirming its high performance and reliability.

Item Type:	Article
Uncontrolled Keywords:	Transfer learning, cyber threats, explainable artificial intelligence
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA329-348 Engineering mathematics. Engineering analysis
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	22 Dec 2025 01:53
Last Modified:	26 Dec 2025 02:59
URII:	http://shdl.mmu.edu.my/id/eprint/15080

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