Name
Abstract | ||
|---|---|---|
Attack vectors are continuously evolving in order to evade Intrusion Detection systems. Internet of Things (IoT) environments, while beneficial for the IT ecosystem, suffer from inherent hardware limitations, which restrict their ability to implement comprehensive security measures and increase their exposure to vulnerability attacks. This paper proposes a novel Network Intrusion Prevention System that utilises a Self-Organizing Incremental Neural Network along with a Support Vector Machine. Due to its structure, the proposed system provides a security solution that does not rely on signatures or rules and is capable to mitigate known and unknown attacks in real-time with high accuracy. Based on our experimental results with the NSL KDD dataset, the proposed framework can achieve on-line updated incremental learning, making it suitable for efficient and scalable industrial applications. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/NTMS.2019.8763842 | 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS) |
Keywords | Field | DocType |
Intrusion Detection,Machine Learning,Self-Organizing Incremental Neural Network,Support Vector Machine,Distributed Denial of Service,Online Incremental Learning | Denial-of-service attack,Computer science,Incremental learning,Support vector machine,Artificial neural network,Intrusion detection system,restrict,Distributed computing,Scalability,Vulnerability | Conference |
ISSN | ISBN | Citations |
2157-4952 | 978-1-7281-1543-6 | 1 |
PageRank | References | Authors |
0.37 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christos Constantinides | 1 | 1 | 0.37 |
| Stavros Shiaeles | 2 | 2 | 1.07 |
| B. V. Ghita | 3 | 73 | 24.16 |
| Nicholas Kolokotronis | 4 | 67 | 18.23 |