An Effective Mechanism to Mitigate Real-Time DDoS Attack
Computer networks are subject to an unprecedented number and variety of attack, the majority of which are distributed denial of service (DDoS). The nature and mechanisms employed in these DDoS attacks continually change, creating a significant challenge for detection and management. To address this...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9097187/ |
| _version_ | 1818935269909331968 |
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| author | Rana Abubakar Abdulaziz Aldegheishem Muhammad Faran Majeed Amjad Mehmood Hafsa Maryam Nabil Ali Alrajeh Carsten Maple Muhammad Jawad |
| author_facet | Rana Abubakar Abdulaziz Aldegheishem Muhammad Faran Majeed Amjad Mehmood Hafsa Maryam Nabil Ali Alrajeh Carsten Maple Muhammad Jawad |
| author_sort | Rana Abubakar |
| collection | DOAJ |
| description | Computer networks are subject to an unprecedented number and variety of attack, the majority of which are distributed denial of service (DDoS). The nature and mechanisms employed in these DDoS attacks continually change, creating a significant challenge for detection and management. To address this evolving nature of attacks, approaches are required that can effectively detect and mitigate emerging attacks. In this paper, we provide a mechanism that not only detects the presence of a DDoS attacks but also identifies the route of attack and commences a process of mitigation at the initial stage of identification. The proposed research involves an optimized SVM classification algorithm integrated with SNORT IPS to provide prevention mechanisms for the entire network when subject to DDoS attack. The proposed IPS method allows traffic identified as legitimate to pass through the network, whereas suspect traffic is flagged and has to go through an identification system. We present the algorithm with experimental results that show better performance than simple Snort IPS, Probabilistic Neural Network (PNN), Back Propagation (BP), Chi-square, and PSO-SVM in terms of accuracy, exposure and specificity. These results show that the average accuracy rate of our method is 97 percent. |
| first_indexed | 2024-12-20T05:17:30Z |
| format | Article |
| id | doaj.art-03e1142cd3554852a459b0bd7b7d9888 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:17:30Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-03e1142cd3554852a459b0bd7b7d98882022-12-21T19:52:07ZengIEEEIEEE Access2169-35362020-01-01812621512622710.1109/ACCESS.2020.29958209097187An Effective Mechanism to Mitigate Real-Time DDoS AttackRana Abubakar0https://orcid.org/0000-0002-4206-4999Abdulaziz Aldegheishem1https://orcid.org/0000-0003-3287-5357Muhammad Faran Majeed2Amjad Mehmood3https://orcid.org/0000-0003-3941-4617Hafsa Maryam4https://orcid.org/0000-0003-3336-4057Nabil Ali Alrajeh5https://orcid.org/0000-0002-1861-0582Carsten Maple6https://orcid.org/0000-0002-4715-212XMuhammad Jawad7https://orcid.org/0000-0002-9201-1297Department of Computer Science, University of Sahiwal, Sahiwal, PakistanUrban Planning Department, Traffic Safety Technologies Chair, College of Architecture and Planning, King Saud University, Riyadh, Saudi ArabiaDepartment of Computer Science, Shaheed Benazir Bhutto University Sheringal, Sheringal, PakistanWMG, University of Warwick, Coventry, U.K.Department of Computer Science, COMSATS University, Islamabad, PakistanBiomedical Technology Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi ArabiaWMG, University of Warwick, Coventry, U.K.Institute of Computing, Kohat University of Science and Technology, Kohat, PakistanComputer networks are subject to an unprecedented number and variety of attack, the majority of which are distributed denial of service (DDoS). The nature and mechanisms employed in these DDoS attacks continually change, creating a significant challenge for detection and management. To address this evolving nature of attacks, approaches are required that can effectively detect and mitigate emerging attacks. In this paper, we provide a mechanism that not only detects the presence of a DDoS attacks but also identifies the route of attack and commences a process of mitigation at the initial stage of identification. The proposed research involves an optimized SVM classification algorithm integrated with SNORT IPS to provide prevention mechanisms for the entire network when subject to DDoS attack. The proposed IPS method allows traffic identified as legitimate to pass through the network, whereas suspect traffic is flagged and has to go through an identification system. We present the algorithm with experimental results that show better performance than simple Snort IPS, Probabilistic Neural Network (PNN), Back Propagation (BP), Chi-square, and PSO-SVM in terms of accuracy, exposure and specificity. These results show that the average accuracy rate of our method is 97 percent.https://ieeexplore.ieee.org/document/9097187/DDoSnetwork attacksIP networkssecuritydataset |
| spellingShingle | Rana Abubakar Abdulaziz Aldegheishem Muhammad Faran Majeed Amjad Mehmood Hafsa Maryam Nabil Ali Alrajeh Carsten Maple Muhammad Jawad An Effective Mechanism to Mitigate Real-Time DDoS Attack IEEE Access DDoS network attacks IP networks security dataset |
| title | An Effective Mechanism to Mitigate Real-Time DDoS Attack |
| title_full | An Effective Mechanism to Mitigate Real-Time DDoS Attack |
| title_fullStr | An Effective Mechanism to Mitigate Real-Time DDoS Attack |
| title_full_unstemmed | An Effective Mechanism to Mitigate Real-Time DDoS Attack |
| title_short | An Effective Mechanism to Mitigate Real-Time DDoS Attack |
| title_sort | effective mechanism to mitigate real time ddos attack |
| topic | DDoS network attacks IP networks security dataset |
| url | https://ieeexplore.ieee.org/document/9097187/ |
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