.png){kind=icon}
.png){kind=icon}
Return
Ensemble based collaborative and distributed intrusion detection systems: A survey
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jnca.2016.03.011.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Modern network intrusion detection systems must be able to handle large and fast changing data, often also taking into account real-time requirements. Ensemble-based data mining algorithms and their distributed implementations are a promising approach to these issues. Therefore, this work presents the current state of the art of the ensemble-based methods used in modern intrusion detection systems, with a particular attention to distributed approaches and implementations. This review also consider supervised and unsupervised data mining algorithms, more suitable to work in an environment that requires the analysis of data streams in real-time. Sharing knowledge across multiple nodes is another of the key points in designing appropriate NIDSs and for this reason, collaborative IDS were also included in this work. Finally, we discuss some open issues and lessons learned from this review, which can help researchers to design more efficient NIDSs. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
RANDOM-FORESTS
CATEGORIZATION
INFORMATION
ALGORITHM
SUPPORT
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8
Papers:
3.6K
Citations:
1.1W