ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks
Traditional security mechanisms find difficulties in dealing with intelligent assaults in cyber-physical systems (CPSs) despite modern information and communication technologies. Furthermore, resource consumption in software-defined networks (SDNs) in industrial organizations is usually on a larger...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/18/6958 |
| _version_ | 1797482534844497920 |
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| author | Sagar Ramani Rutvij H. Jhaveri |
| author_facet | Sagar Ramani Rutvij H. Jhaveri |
| author_sort | Sagar Ramani |
| collection | DOAJ |
| description | Traditional security mechanisms find difficulties in dealing with intelligent assaults in cyber-physical systems (CPSs) despite modern information and communication technologies. Furthermore, resource consumption in software-defined networks (SDNs) in industrial organizations is usually on a larger scale, and the present routing algorithms fail to address this issue. In this paper, we present a real-time delay attack detection and isolation scheme for fault-tolerant software-defined industrial networks. The primary goal of the delay attack is to lower the resilience of our previously proposed scheme, SDN-resilience manager (SDN-RM). The attacker compromises the OpenFlow switch and launches an attack by delaying the link layer discovery protocol (LLDP) packets. As a result, the performance of SDN-RM is degraded and the success rate decreases significantly. In this work, we developed a machine learning (ML)-based attack detection and isolation mechanism, which extends our previous work, SDN-RM. Predicting and labeling malicious switches in an SDN-enabled network is a challenge that can be successfully addressed by integrating ML with network resilience solutions. Therefore, we propose a delay-based attack detection and isolation scheme (DA-DIS), which avoids malicious switches from entering the routes by combining an ML mechanism along with a route-handoff mechanism. DA-DIS increases network resilience by increasing success rate and network throughput. |
| first_indexed | 2024-03-09T22:33:46Z |
| format | Article |
| id | doaj.art-58c7da00511448d588325c5d41783485 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:33:46Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-58c7da00511448d588325c5d417834852023-11-23T18:52:12ZengMDPI AGSensors1424-82202022-09-012218695810.3390/s22186958ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial NetworksSagar Ramani0Rutvij H. Jhaveri1Department of Computer Engineering, Gujarat Technological University, Ahmedabad 382424, IndiaDepartment of Computer Science & Engineering, Pandit Deendayal Energy University, Gandhinagar 382007, IndiaTraditional security mechanisms find difficulties in dealing with intelligent assaults in cyber-physical systems (CPSs) despite modern information and communication technologies. Furthermore, resource consumption in software-defined networks (SDNs) in industrial organizations is usually on a larger scale, and the present routing algorithms fail to address this issue. In this paper, we present a real-time delay attack detection and isolation scheme for fault-tolerant software-defined industrial networks. The primary goal of the delay attack is to lower the resilience of our previously proposed scheme, SDN-resilience manager (SDN-RM). The attacker compromises the OpenFlow switch and launches an attack by delaying the link layer discovery protocol (LLDP) packets. As a result, the performance of SDN-RM is degraded and the success rate decreases significantly. In this work, we developed a machine learning (ML)-based attack detection and isolation mechanism, which extends our previous work, SDN-RM. Predicting and labeling malicious switches in an SDN-enabled network is a challenge that can be successfully addressed by integrating ML with network resilience solutions. Therefore, we propose a delay-based attack detection and isolation scheme (DA-DIS), which avoids malicious switches from entering the routes by combining an ML mechanism along with a route-handoff mechanism. DA-DIS increases network resilience by increasing success rate and network throughput.https://www.mdpi.com/1424-8220/22/18/6958SDNdelay attacksecuritymachine learningindustrial networksCPS |
| spellingShingle | Sagar Ramani Rutvij H. Jhaveri ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks Sensors SDN delay attack security machine learning industrial networks CPS |
| title | ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks |
| title_full | ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks |
| title_fullStr | ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks |
| title_full_unstemmed | ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks |
| title_short | ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks |
| title_sort | ml based delay attack detection and isolation for fault tolerant software defined industrial networks |
| topic | SDN delay attack security machine learning industrial networks CPS |
| url | https://www.mdpi.com/1424-8220/22/18/6958 |
| work_keys_str_mv | AT sagarramani mlbaseddelayattackdetectionandisolationforfaulttolerantsoftwaredefinedindustrialnetworks AT rutvijhjhaveri mlbaseddelayattackdetectionandisolationforfaulttolerantsoftwaredefinedindustrialnetworks |