ARTP: Anomaly based real time prevention of Distributed Denial of Service attacks on the web using machine learning approach
Distributed Denial of Service (DDoS) attack is one of the most destructive internet network attacks, denying legitimate users access to resources and networks by maliciously blocking available computing resources. Intruders send a large number of packets to the network in order to create a crowding...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-01-01
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| Series: | International Journal of Intelligent Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666603022000380 |
| _version_ | 1827582003251249152 |
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| author | P. Krishna Kishore S. Ramamoorthy V.N. Rajavarman |
| author_facet | P. Krishna Kishore S. Ramamoorthy V.N. Rajavarman |
| author_sort | P. Krishna Kishore |
| collection | DOAJ |
| description | Distributed Denial of Service (DDoS) attack is one of the most destructive internet network attacks, denying legitimate users access to resources and networks by maliciously blocking available computing resources. Intruders send a large number of packets to the network in order to create a crowding effect. Unlike a Denial of Service (DoS) attack, where a single compromised source generates all of the traffic, a Distributed Denial of Service (DDoS) attack generates traffic from multiple compromised nodes spread across multiple geographies. To address the challenges posed by the Distributed Denial of Service (DDoS) attack, several researchers proposed a variety of solutions for early detection and prevention of the attack. Effective solutions for the prevention and early detection of Distributed Denial of Service (DDoS) attacks, on the other hand, have yet to be developed, and the problem remains a prominent research focus area. This paper tries to present a novel and optimal solution for detecting Distributed Denial of Service (DDoS) attacks on internet networks more quickly and accurately. The proposed model is an anomaly-based real-time prevention model for web networks. The model is based on machine learning principles and can effectively counter new types of Distributed Denial of Service (DDoS) attacks. To demonstrate the efficiency, accuracy, model robustness, and relative of the proposed model, a simulation study was run on an LLDOS session log, and the results indicated that the model performed better than benchmark models found in the literature. |
| first_indexed | 2024-03-08T22:44:23Z |
| format | Article |
| id | doaj.art-7f57232cae90420caa4a56ff575b1b59 |
| institution | Directory Open Access Journal |
| issn | 2666-6030 |
| language | English |
| last_indexed | 2024-03-08T22:44:23Z |
| publishDate | 2023-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Intelligent Networks |
| spelling | doaj.art-7f57232cae90420caa4a56ff575b1b592023-12-17T06:41:56ZengKeAi Communications Co., Ltd.International Journal of Intelligent Networks2666-60302023-01-0143845ARTP: Anomaly based real time prevention of Distributed Denial of Service attacks on the web using machine learning approachP. Krishna Kishore0S. Ramamoorthy1V.N. Rajavarman2Corresponding author.; Department of Computer Science and Engineering, Dr. M.G.R Educational and Research Institute, Chennai, IndiaDepartment of Computer Science and Engineering, Dr. M.G.R Educational and Research Institute, Chennai, IndiaDepartment of Computer Science and Engineering, Dr. M.G.R Educational and Research Institute, Chennai, IndiaDistributed Denial of Service (DDoS) attack is one of the most destructive internet network attacks, denying legitimate users access to resources and networks by maliciously blocking available computing resources. Intruders send a large number of packets to the network in order to create a crowding effect. Unlike a Denial of Service (DoS) attack, where a single compromised source generates all of the traffic, a Distributed Denial of Service (DDoS) attack generates traffic from multiple compromised nodes spread across multiple geographies. To address the challenges posed by the Distributed Denial of Service (DDoS) attack, several researchers proposed a variety of solutions for early detection and prevention of the attack. Effective solutions for the prevention and early detection of Distributed Denial of Service (DDoS) attacks, on the other hand, have yet to be developed, and the problem remains a prominent research focus area. This paper tries to present a novel and optimal solution for detecting Distributed Denial of Service (DDoS) attacks on internet networks more quickly and accurately. The proposed model is an anomaly-based real-time prevention model for web networks. The model is based on machine learning principles and can effectively counter new types of Distributed Denial of Service (DDoS) attacks. To demonstrate the efficiency, accuracy, model robustness, and relative of the proposed model, a simulation study was run on an LLDOS session log, and the results indicated that the model performed better than benchmark models found in the literature.http://www.sciencedirect.com/science/article/pii/S2666603022000380LLDoS data setDenial of Service (DoS) attackDistributed DoS (DDoS) attackDetection of App-DDoSApplication layer DDoS (App-DDoS) |
| spellingShingle | P. Krishna Kishore S. Ramamoorthy V.N. Rajavarman ARTP: Anomaly based real time prevention of Distributed Denial of Service attacks on the web using machine learning approach International Journal of Intelligent Networks LLDoS data set Denial of Service (DoS) attack Distributed DoS (DDoS) attack Detection of App-DDoS Application layer DDoS (App-DDoS) |
| title | ARTP: Anomaly based real time prevention of Distributed Denial of Service attacks on the web using machine learning approach |
| title_full | ARTP: Anomaly based real time prevention of Distributed Denial of Service attacks on the web using machine learning approach |
| title_fullStr | ARTP: Anomaly based real time prevention of Distributed Denial of Service attacks on the web using machine learning approach |
| title_full_unstemmed | ARTP: Anomaly based real time prevention of Distributed Denial of Service attacks on the web using machine learning approach |
| title_short | ARTP: Anomaly based real time prevention of Distributed Denial of Service attacks on the web using machine learning approach |
| title_sort | artp anomaly based real time prevention of distributed denial of service attacks on the web using machine learning approach |
| topic | LLDoS data set Denial of Service (DoS) attack Distributed DoS (DDoS) attack Detection of App-DDoS Application layer DDoS (App-DDoS) |
| url | http://www.sciencedirect.com/science/article/pii/S2666603022000380 |
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