Network Traffic Anomaly Detection Techniques and Systems

2017 ◽  
pp. 115-169 ◽  
Author(s):  
Monowar H. Bhuyan ◽  
Dhruba K. Bhattacharyya ◽  
Jugal K. Kalita
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Detection Techniques ◽  
Traffic Anomaly ◽  
Traffic Anomaly Detection

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.

Evaluating Statistical Models for Network Traffic Anomaly Detection

2019 ◽  
Author(s):  
Peter Kromkowski ◽  
Shaoran Li ◽  
Wenxi Zhao ◽  
Brendan Abraham ◽  
Austin Osborne ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Statistical Models ◽  
Traffic Anomaly ◽  
Traffic Anomaly Detection

Network traffic anomaly detection algorithm using mahout classifier

2019 ◽  
Vol 37 (1) ◽  
pp. 137-144 ◽  
Author(s):  
Hua Peng ◽  
Liang Liu ◽  
Jiayong Liu ◽  
Johnwb R. Lewis
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Detection Algorithm ◽  
Traffic Anomaly ◽  
Traffic Anomaly Detection
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