scholarly journals Reducing false positives of network anomaly detection by local adaptive multivariate smoothing

2017 ◽  
Vol 83 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Martin Grill ◽  
Tomáš Pevný ◽  
Martin Rehak
Keyword(s):  
Anomaly Detection ◽  
False Positives ◽  
Multivariate Smoothing ◽  
Network Anomaly Detection

scholarly journals Visualization of Anomalies using Graph-Based Anomaly Detection

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Ramesh Paudel ◽  
Lauren Tharp ◽  
Dulce Kaiser ◽  
William Eberle ◽  
Gerald Gannod
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
False Positives ◽  
Visual Context ◽  
Traffic Flows ◽  
Security Analysts ◽  
Fast Analysis ◽  
Detection Systems ◽  
Potential Damage ◽  
Network Anomaly Detection

Network protocol analyzers such asWireshark are valuable for analyzing network traffic but pose a challenge in that it can be difficult to determine which behaviors are out of the ordinary due to the volume of data that must be analyzed. Network anomaly detection systems can provide vital insights to security analysts to supplement protocol analyzers, but this feedback can be difficult to interpret due to the complexity of the algorithms used and the lack of context to determine the reasoning for which an event was labeled as anomalous. We present an approach for visualizing anomalies using a graph-based anomaly detection methodology that aims to provide visual context to network traffic. We demonstrate the approach using network traffic flows as an approach for aiding in the investigation and triage of anomalous network events. The simplicity of a visual representation supports fast analysis of anomalous traffic to identify true positives from false positives and prevent further potential damage.

scholarly journals Network Anomaly Detection using Memory-augmented Deep Autoencoder

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Byeongjun Min ◽  
JiHoon Yoo ◽  
Sangsoo Kim ◽  
Dongil Shin ◽  
Dongkyoo Shin
Keyword(s):  
Anomaly Detection ◽  
Network Anomaly Detection
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