Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning Technique
The rapid increase in the adoption of 5G networks has revolutionized communication technologies, enabling high-speed data transmission and connectivity across various domains. However, the advent of 5G technology comes with an increased risk of cyber-attacks and security breaches, necessitating the...
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| Format: | Article |
| Language: | English |
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College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria
2024-09-01
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| Series: | ABUAD Journal of Engineering Research and Development |
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| Online Access: | https://journals.abuad.edu.ng/index.php/ajerd/article/view/765 |
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| _version_ | 1846099665557127168 |
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| author | Anthony Kwubeghari Lucy Ifeyinwa Ezigbo Francis Amaechi Okoye |
| author_facet | Anthony Kwubeghari Lucy Ifeyinwa Ezigbo Francis Amaechi Okoye |
| author_sort | Anthony Kwubeghari |
| collection | DOAJ |
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The rapid increase in the adoption of 5G networks has revolutionized communication technologies, enabling high-speed data transmission and connectivity across various domains. However, the advent of 5G technology comes with an increased risk of cyber-attacks and security breaches, necessitating the development of robust defence mechanisms to safeguard network infrastructure and mitigate potential threats. The work presents a novel approach for modelling a cyber-attack response system tailored specifically for 5G networks, leveraging machine learning techniques to enhance threat detection and response capabilities. The study introduced innovative methodologies, including the integration of standard backpropagation and dropout regularization technique. Furthermore, an intelligent cyber threat classification model that proactively detects and mitigates malware threats in 5G networks was developed. Additionally, a comprehensive cyber-attack response model designed to isolate threats from the network infrastructure and mitigate potential security risks was formulated. The result of testing the response algorithm with simulation, and considering quality of service such as throughput, latency and packet loss, showed 80.05%, 24.9ms and 4.09% respectively. During system integration of the model on 5G network with stimulated malware, the throughput reported 71.81%. Also, packet loss reported loss rate of 23.18%, while latency reported 178.98ms. Our findings contribute to the advancement of cybersecurity in 5G environments and lay the foundation for the development of robust cyber defence systems to safeguard critical network infrastructure against emerging threats.
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| format | Article |
| id | doaj-art-a862f81efe394fac95ac2e273bdc47c7 |
| institution | Kabale University |
| issn | 2756-6811 2645-2685 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria |
| record_format | Article |
| series | ABUAD Journal of Engineering Research and Development |
| spelling | doaj-art-a862f81efe394fac95ac2e273bdc47c72024-12-31T08:16:18ZengCollege of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, NigeriaABUAD Journal of Engineering Research and Development2756-68112645-26852024-09-017210.53982/ajerd.2024.0702.29-j639Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning TechniqueAnthony Kwubeghari0Lucy Ifeyinwa Ezigbo1Francis Amaechi Okoye2Computer Engineering, Enugu State University of Science and Technology, Agbani, Enugu State, NigeriaComputer Engineering, Enugu State University of Science and Technology, Agbani, Enugu State, NigeriaComputer Engineering, Enugu State University of Science and Technology, Agbani, Enugu State, Nigeria The rapid increase in the adoption of 5G networks has revolutionized communication technologies, enabling high-speed data transmission and connectivity across various domains. However, the advent of 5G technology comes with an increased risk of cyber-attacks and security breaches, necessitating the development of robust defence mechanisms to safeguard network infrastructure and mitigate potential threats. The work presents a novel approach for modelling a cyber-attack response system tailored specifically for 5G networks, leveraging machine learning techniques to enhance threat detection and response capabilities. The study introduced innovative methodologies, including the integration of standard backpropagation and dropout regularization technique. Furthermore, an intelligent cyber threat classification model that proactively detects and mitigates malware threats in 5G networks was developed. Additionally, a comprehensive cyber-attack response model designed to isolate threats from the network infrastructure and mitigate potential security risks was formulated. The result of testing the response algorithm with simulation, and considering quality of service such as throughput, latency and packet loss, showed 80.05%, 24.9ms and 4.09% respectively. During system integration of the model on 5G network with stimulated malware, the throughput reported 71.81%. Also, packet loss reported loss rate of 23.18%, while latency reported 178.98ms. Our findings contribute to the advancement of cybersecurity in 5G environments and lay the foundation for the development of robust cyber defence systems to safeguard critical network infrastructure against emerging threats. https://journals.abuad.edu.ng/index.php/ajerd/article/view/765Cyber Threat Response Algorithm (CTRA)Dropout AlgorithmBack PropagationMachine LearningArtificial Neural Network (ANN) |
| spellingShingle | Anthony Kwubeghari Lucy Ifeyinwa Ezigbo Francis Amaechi Okoye Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning Technique ABUAD Journal of Engineering Research and Development Cyber Threat Response Algorithm (CTRA) Dropout Algorithm Back Propagation Machine Learning Artificial Neural Network (ANN) |
| title | Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning Technique |
| title_full | Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning Technique |
| title_fullStr | Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning Technique |
| title_full_unstemmed | Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning Technique |
| title_short | Modelling of Cyber Attack Detection and Response System for 5G Network Using Machine Learning Technique |
| title_sort | modelling of cyber attack detection and response system for 5g network using machine learning technique |
| topic | Cyber Threat Response Algorithm (CTRA) Dropout Algorithm Back Propagation Machine Learning Artificial Neural Network (ANN) |
| url | https://journals.abuad.edu.ng/index.php/ajerd/article/view/765 |
| work_keys_str_mv | AT anthonykwubeghari modellingofcyberattackdetectionandresponsesystemfor5gnetworkusingmachinelearningtechnique AT lucyifeyinwaezigbo modellingofcyberattackdetectionandresponsesystemfor5gnetworkusingmachinelearningtechnique AT francisamaechiokoye modellingofcyberattackdetectionandresponsesystemfor5gnetworkusingmachinelearningtechnique |