TY  - JOUR
T1  - Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks
AU  - Kumar, Amit
AU  - Kumar, Vivek
AU  - Bhadauria, Abhay
PY  - 2025
DA  - July
Y2  - 2025
DO  - 10.31127/tuje.1630410
JF  - Turkish Journal of Engineering
JO  - TUJE
PB  - Murat YAKAR
WT  - DergiPark
SN  - 2587-1366
SP  - 600
EP  - 611
VL  - 9
IS  - 3
LA  - en
AB  - With the rapid advancement of network technology, attacks such as denial-of-service (DoS), distributed denial-of-service (DDoS), and unknown or emerging threats are becoming more complex and harder to detect using conventional methods. Traditional intrusion detection systems (IDS) often struggle to detect attacks in high-speed real-time networks due to their reliance on rule-based or signature-based detection methods and limited processing speed. Additionally, organizations and industries are facing significant challenges due to the expansion of electronic devices like the Internet of Things (IoT) and running computer applications. There is an urgent need to secure these sensitive IoT high-speed network traffic systems. To address these limitations, this study develops a reliable and effective Hybrid Machine Learning-based Real-time Intrusion Detection System (HMLRT-IDS) to detect DoS, DDoS, and emerging attacks in real-time network traffic. The study proposes a Python programming-based algorithm for feature extraction that sniffs data from real-time network traffic and extracts relevant data related to the features, enabling the successful identification of cyber threats in real-time network traffic. Moreover, the Real-time Network Intrusion Detection-23 (RTNID23) dataset is constructed to evaluate the proposed HMLRT-IDS. Experimental results demonstrate that HMLRT-IDS achieves a remarkable accuracy of 99.88% with a response time as low as 0.001 seconds, making it highly efficient for real-time applications. Additionally, the proposed system outperforms several existing methods in terms of accuracy, speed, and adaptability, offering a significant advancement in intrusion detection for high-speed IoT or edge computing networks. This model simplifies the analysis of attacks and aids in determining the appropriate response when an attack occurs.
KW  - Feature Extraction
KW  - Malware detection
KW  - Intrusion Detection System
KW  - Pipelines
KW  - Edge Computing
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UR  - https://doi.org/10.31127/tuje.1630410
L1  - https://dergipark.org.tr/en/download/article-file/4567394
ER  -