- Tytuł:
- Explainable spark-based psoclustering for intrusion detection
- Autorzy:
-
Ben Ncir, Chiheb-Eddine
Ben HajKacem, Mohamed Aymen
Alattas, Mohammed - Tematy:
-
Intrusion Detection System
IDS
Artificial Intelligence
AI
Explainable AI
XAI
particle swarm optimization
PSO
Spark framework - Pokaż więcej
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Powiązania:
- https://bibliotekanauki.pl/articles/58810105.pdf Link otwiera się w nowym oknie
- Opis:
- Given the exponential growth of available data in large networks, the existence of rapid, transparent, and explainable intrusion detection systems has become of highly necessity to effectively discover attacks in such huge networks. To deal with this challenge, we propose a novel explainable intrusion detection system based on Spark, Particle Swarm Optimization (PSO) clustering, and eXplainable Artificial Intelligence (XAI) techniques. Spark is used as a parallel processing model for the effective processing of large-scale data, PSO is integrated to improve the quality of the intrusion detection system by avoiding sensitive initialization and premature convergence of the clustering algorithm and finally, XAI techniques are used to enhance interpretability and explainability of intrusion recommendations by providing both micro and macro explanations of detected intrusions. Experiments are conducted on large collections of real datasets to show the effectiveness of the proposed intrusion detection system in terms of explainability, scalability, and accuracy. The proposed system has shown high transparency in assisting security experts and decision-makers to understand and interpret attack behavior.
- Dostawca treści:
- Biblioteka Nauki
Artykuł