Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks

Amit Kumar; Abhay Bhadauria

Makale detayları

Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks

Yazarlar:

Amit Kumar | Abhay Bhadauria

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Information Security Management

Dergi:

Turkish Journal of Engineering Vol.9 Issue 3, pp.600-611

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339469

pISSN:

2587-1366

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks

Yazarlar:

Amit Kumar | Abhay Bhadauria

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Information Security Management

Dergi:

Turkish Journal of Engineering Vol.9 Issue 3, pp.600-611

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339469

pISSN:

2587-1366

Feature Extraction

Malware detection

Intrusion Detection System

Pipelines

Edge Computing

Sayı

Makale detayları

Real-Time Hybrid Machine Learning-Based Next-Generation Intrusion Detection System for Edge Computing Networks

Yazarlar: Amit Kumar | Abhay Bhadauria

Yayıncı: Murat YAKAR

Ana Başlıklar: Information Security Management

Dergi: Turkish Journal of Engineering Vol.9 Issue 3, pp.600-611

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001339469

pISSN: 2587-1366

Feature Extraction

Malware detection

Intrusion Detection System

Pipelines

Edge Computing

Sayı

Yazarlar:

Amit Kumar | Abhay Bhadauria

Yayıncı:

Murat YAKAR

Ana Başlıklar:

Information Security Management

Dergi:

Turkish Journal of Engineering Vol.9 Issue 3, pp.600-611

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001339469

pISSN:

2587-1366