A novel online incremental learning intrusion prevention system
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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.
| Original language | English |
|---|---|
| Title of host publication | 2019 10th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2019 - Proceedings and Workshop |
| Publisher | IEEE |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-7281-1542-9, 978-1-7281-1541-2 |
| ISBN (Print) | 978-1-7281-1543-6 |
| DOIs | |
| Publication status | Published - 15 Jul 2019 |
| Event | 10th IFIP International Conference on New Technologies, Mobility and Security - Canary Islands, Spain Duration: 24 Jun 2019 → 26 Jun 2019 |
Publication series
| Name | 2019 10th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2019 - Proceedings and Workshop |
|---|---|
| Publisher | IEEE |
| ISSN (Print) | 2157-4952 |
Conference
| Conference | 10th IFIP International Conference on New Technologies, Mobility and Security |
|---|---|
| Abbreviated title | NTMS 2019 |
| Country | Spain |
| City | Canary Islands |
| Period | 24/06/19 → 26/06/19 |
Related information
ID: 15934675
