Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing
Joint Authors
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-25
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
In this paper, a network intrusion detection system is proposed using Bayesian topic model latent Dirichlet allocation (LDA) for mobile edge computing (MEC).
The method employs tcpdump packets and extracts multiple features from the packet headers.
The tcpdump packets are transferred into documents based on the features.
A topic model is trained using only attack-free traffic in order to learn the behavior patterns of normal traffic.
Then, the test traffic is analyzed against the learned behavior patterns to measure the extent to which the test traffic resembles the normal traffic.
A threshold is defined in the training phase as the minimum likelihood of a host.
In the test phase, when a host’s test traffic has a likelihood lower than the host’s threshold, the traffic is labeled as an intrusion.
The intrusion detection system is validated using DARPA 1999 dataset.
Experiment shows that our method is suitable to protect the security of MEC.
American Psychological Association (APA)
Cao, Xuefei& Fu, Yulong& Chen, Bo. 2020. Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208771
Modern Language Association (MLA)
Cao, Xuefei…[et al.]. Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing. Security and Communication Networks No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1208771
American Medical Association (AMA)
Cao, Xuefei& Fu, Yulong& Chen, Bo. Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208771
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208771