| Title | DDoS Intrusion Detection Through Machine Learning Ensemble |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Das, Saikat, Mahfouz, Ahmed M., Venugopal, Deepak, Shiva, Sajjan |
| Conference Name | 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C) |
| Keywords | Artificial neural networks, composability, Computer crime, computer network security, Data models, DDoS, DDoS attack detection, DDoS Attacks, DDoS Intrusion Detection, distributed denial of service attacks, domain-knowledge, emerging DDoS attack patterns, Ensemble Machine Learning, ensemble models, feature extraction, Human Behavior, Intrusion detection, intrusion detection system, learning (artificial intelligence), machine learning, machine learning ensemble, Metrics, network intrusion detection system, NIDS approach, NSL-KDD dataset, prominent attacks, pubcrawl, reduced feature set, Resiliency, robust defense mechanism, Support vector machines |
| Abstract | Distributed Denial of Service (DDoS) attacks have been the prominent attacks over the last decade. A Network Intrusion Detection System (NIDS) should seamlessly configure to fight against these attackers' new approaches and patterns of DDoS attack. In this paper, we propose a NIDS which can detect existing as well as new types of DDoS attacks. The key feature of our NIDS is that it combines different classifiers using ensemble models, with the idea that each classifier can target specific aspects/types of intrusions, and in doing so provides a more robust defense mechanism against new intrusions. Further, we perform a detailed analysis of DDoS attacks, and based on this domain-knowledge verify the reduced feature set [27, 28] to significantly improve accuracy. We experiment with and analyze NSL-KDD dataset with reduced feature set and our proposed NIDS can detect 99.1% of DDoS attacks successfully. We compare our results with other existing approaches. Our NIDS approach has the learning capability to keep up with new and emerging DDoS attack patterns. |
| DOI | 10.1109/QRS-C.2019.00090 |
| Citation Key | das_ddos_2019 |
DDoS Intrusion Detection Through Machine Learning Ensemble