A Novel Online Incremental Learning Intrusion Prevention System
| Autor: | Nicholas Kolokotronis, Bogdan Ghita, Christos Constantinides, Stavros Shiaeles |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Structure (mathematical logic) Computer Science - Cryptography and Security Artificial neural network Computer science Computer Science - Artificial Intelligence Distributed computing 020206 networking & telecommunications 02 engineering and technology Intrusion detection system Support vector machine Artificial Intelligence (cs.AI) restrict Scalability Incremental learning 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Cryptography and Security (cs.CR) Vulnerability (computing) |
| Zdroj: | 2019 10th IFIP International Conference on New Technologies, Mobility and Security (NTMS) |
| Popis: | 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 SelfOrganizing 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. 6 pages |
| Databáze: | OpenAIRE |
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