Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

2019 ◽  
Author(s):  
Laisen Nie ◽  
Huizhi Wang ◽  
Shimin Gong ◽  
Zhaolong Ning ◽  
Mohammad S. Obaidat ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Spatio Temporal ◽  
Sparse Features

scholarly journals Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 177954-177964
Author(s):  
Laisen Nie ◽  
Yixuan Wu ◽  
Huizhi Wang ◽  
yongkang li
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Spatio Temporal ◽  
Sparse Features

Hubble: An End to End Approach for Anomaly Detection in Network Traffic

2021 ◽  
Author(s):  
Shiwei Wang ◽  
Haizhou Du ◽  
Lin Liu ◽  
Zhenyu Lin
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
End To End

scholarly journals Entropy-based network traffic anomaly classification method resilient to deception

2021 ◽  
pp. 45-45
Author(s):  
Juma Ibrahim ◽  
Slavko Gajin
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Real Life ◽  
Classification Method ◽  
Practical Implementation ◽  
Network Environment ◽  
Detection Techniques ◽  
Basic Set ◽  
Anomaly Classification ◽  
Traffic Anomaly

Entropy-based network traffic anomaly detection techniques are attractive due to their simplicity and applicability in a real-time network environment. Even though flow data provide only a basic set of information about network communications, they are suitable for efficient entropy-based anomaly detection techniques. However, a recent work reported a serious weakness of the general entropy-based anomaly detection related to its susceptibility to deception by adding spoofed data that camouflage the anomaly. Moreover, techniques for further classification of the anomalies mostly rely on machine learning, which involves additional complexity. We address these issues by providing two novel approaches. Firstly, we propose an efficient protection mechanism against entropy deception, which is based on the analysis of changes in different entropy types, namely Shannon, R?nyi, and Tsallis entropies, and monitoring the number of distinct elements in a feature distribution as a new detection metric. The proposed approach makes the entropy techniques more reliable. Secondly, we have extended the existing entropy-based anomaly detection approach with the anomaly classification method. Based on a multivariate analysis of the entropy changes of multiple features as well as aggregation by complex feature combinations, entropy-based anomaly classification rules were proposed and successfully verified through experiments. Experimental results are provided to validate the feasibility of the proposed approach for practical implementation of efficient anomaly detection and classification method in the general real-life network environment.

scholarly journals Anomaly Detection Method Using Entropy of Network Traffic Distributions

2006 ◽  
Vol 13C (3) ◽  
pp. 283-294
Author(s):  
Koo-Hong Kang ◽  
Jin-Tae Oh ◽  
Jong-Soo Jang
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Detection Method
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