Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks
The philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/15/1/110 |
| _version_ | 1797436825008078848 |
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| author | Razvan Bocu Maksim Iavich |
| author_facet | Razvan Bocu Maksim Iavich |
| author_sort | Razvan Bocu |
| collection | DOAJ |
| description | The philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances in the improvement of the standards, which simulate 5G networks. This article evaluates the experience that the authors gained when implementing Vodafone Romania 5G network services, illustrates the experience gained in context by analyzing relevant peer-to-peer work and used technologies, and outlines the relevant research areas and challenges that are likely to affect the design and implementation of large 5G data networks. This paper presents a machine learning-based real-time intrusion detection system with the corresponding intrusion prevention system. The convolutional neural network (CNN) is used to train the model. The system was evaluated in the context of the 5G data network. The smart intrusion detection system (IDS) takes the creation of software-defined networks into account. It uses models based on artificial intelligence. The system is capable to reveal not previously detected intrusions using software components based on machine learning, using the convolutional neural network. The intrusion prevention system (IPS) blocks the malicious traffic. This system was evaluated, and the results confirmed that it provides higher efficiencies compared to less overhead-like approaches, allowing for real-time deployment in 5G networks. The offered system can be used for symmetric and asymmetric communication scenarios. |
| first_indexed | 2024-03-09T11:08:12Z |
| format | Article |
| id | doaj.art-7e9a50211e564850b6baef4cdef96fa1 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T11:08:12Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-7e9a50211e564850b6baef4cdef96fa12023-12-01T00:51:59ZengMDPI AGSymmetry2073-89942022-12-0115111010.3390/sym15010110Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined NetworksRazvan Bocu0Maksim Iavich1Department of Mathematics and Computer Science, Transilvania University of Brasov, 500036 Brașov, RomaniaSchool of Technologies, Caucasus University, 0102 Tbilisi, GeorgiaThe philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances in the improvement of the standards, which simulate 5G networks. This article evaluates the experience that the authors gained when implementing Vodafone Romania 5G network services, illustrates the experience gained in context by analyzing relevant peer-to-peer work and used technologies, and outlines the relevant research areas and challenges that are likely to affect the design and implementation of large 5G data networks. This paper presents a machine learning-based real-time intrusion detection system with the corresponding intrusion prevention system. The convolutional neural network (CNN) is used to train the model. The system was evaluated in the context of the 5G data network. The smart intrusion detection system (IDS) takes the creation of software-defined networks into account. It uses models based on artificial intelligence. The system is capable to reveal not previously detected intrusions using software components based on machine learning, using the convolutional neural network. The intrusion prevention system (IPS) blocks the malicious traffic. This system was evaluated, and the results confirmed that it provides higher efficiencies compared to less overhead-like approaches, allowing for real-time deployment in 5G networks. The offered system can be used for symmetric and asymmetric communication scenarios.https://www.mdpi.com/2073-8994/15/1/110intrusion detection system5G securitynetworksheterogeneous networksreal-time protection |
| spellingShingle | Razvan Bocu Maksim Iavich Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks Symmetry intrusion detection system 5G security networks heterogeneous networks real-time protection |
| title | Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks |
| title_full | Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks |
| title_fullStr | Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks |
| title_full_unstemmed | Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks |
| title_short | Real-Time Intrusion Detection and Prevention System for 5G and beyond Software-Defined Networks |
| title_sort | real time intrusion detection and prevention system for 5g and beyond software defined networks |
| topic | intrusion detection system 5G security networks heterogeneous networks real-time protection |
| url | https://www.mdpi.com/2073-8994/15/1/110 |
| work_keys_str_mv | AT razvanbocu realtimeintrusiondetectionandpreventionsystemfor5gandbeyondsoftwaredefinednetworks AT maksimiavich realtimeintrusiondetectionandpreventionsystemfor5gandbeyondsoftwaredefinednetworks |