Classification of BGP anomalies using decision trees and fuzzy rough sets
Abstract
Border Gateway Protocol (BGP) is the core component of the Internet's routing infrastructure. Abnormal routing behavior impairs global Internet connectivity and stability. Hence, designing and implementing anomaly detection algorithms is important for improving performance of routing protocols. While various machine learning techniques may be employed to detect BGP anomalies, their performance strongly depends on the employed learning algorithms. These techniques have multiple variants that often work well for detecting a particular anomaly. In this paper, we use the decision tree and fuzzy rough set methods for feature selection. Decision tree and extreme learning machine classification techniques are then used to maximize the accuracy of detecting BGP anomalies. The proposed techniques are tested using Internet traffic traces.
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There are security challenges for the IoT network because of the complex structure of WSN, which is composed of resource-constrained sensor nodes.
“There are security challenges for the IoT network because of the complex structure of WSN, which is composed of resource-constrained sensor nodes [139] [98].”
From Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree (2023) · cites this paper as [139]
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- Authors
- Li, Y., Xing, H.-J., Hua, Q., Wang, X.-Z., Batta, P., Haeri, S., & Trajkovic, L.
- Affiliations
- Hebei University (CN)
- Simon Fraser University (CA)
- Venue
- 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
- Publisher
- IEEE
- Published
- 2014-10
- Pages
- 1312-1317
- ISSN
- 1062-922X (ISSN-L)
- OpenAlex
- W1998632008
- Cited by
- 36
- References
- 30
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 4 · 2022: 6 · 2021: 5 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 2 · 2016: 3 · 2015: 2
- FWCI
- 1.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Keywords
- anomaly detection, BGP, secure routing, decision tree, fuzzy rough sets, feature selection, extreme learning machine
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