Reliable cyber-attack detection system using genetic hesitant fuzzy-based deep transform classifier
Girubagari N1, Ravi T N2 and Panneer Arokiaraj S3
Associate Professor, Department of Computer Science,Cauvery College for Women (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli,Tamil Nadu,India1
Associate Professor, Department of Computer Science,Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli,Tamil Nadu,India2
Associate Professor, Department of Computer Science,Thanthai Periyar Government Arts and Science College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli,Tamil Nadu,India3
Associate Professor, Department of Computer Science,Jamal Mohamed College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli,Tamil Nadu,India2
Associate Professor, Department of Computer Science,Thanthai Periyar Government Arts and Science College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli,Tamil Nadu,India3
Corresponding Author : Girubagari N
Recieved : 25-November-2024; Revised : 15-April-2026; Accepted : 16-April-2026
Abstract
Rapid advancements in network technologies, along with the increasing size and volume of data transmitted across networks, necessitate innovative security solutions. In this context, the complexity and severity of cyber threats and attacks have grown significantly. Deep learning (DL) provides effective mechanisms for automating cyber-attack detection and enabling rapid identification of attack types. To address this challenge, this study proposes a method called knapsack genetic hesitant fuzzy (KGHF)-class compatible deep transform learning (CCDTL) for cyber-attack detection. The proposed KGHF-CCDTL framework consists of two main stages: feature engineering and classification. Using the benchmark dataset NSL-KDD as input, the feature engineering stage applies the KGHF model for preprocessing and feature selection. The selected features are then fed into the CCDTL classifier for accurate cyber-attack detection. Experimental evaluation based on quantitative metrics such as precision, recall, accuracy, F-score, and false alarm rate (FAR) demonstrates the effectiveness of the proposed approach. Furthermore, the KGHF-CCDTL method shows strong potential for securing internet of things (IoT) networks against cyber-threats, as evidenced by its superior performance on the real-time dataset CICIoT2023. Overall, this research contributes to the development of an intelligent cyber-attack detection system, enhancing the security and integrity of smart city environments.
Keywords
Cyber-attack detection, Deep learning, Feature engineering, NSL-KDD, Internet of things (IoT) security, CICIoT2023.
Cite this article
N G, TN Ravi, S PA. Reliable cyber-attack detection system using genetic hesitant fuzzy-based deep transform classifier. International Journal of Advanced Technology and Engineering Exploration. 2026;13(137):490-511. DOI : 10.19101/IJATEE.2024.111102088
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