Performance Analysis of Network Anomaly Detection Systems in Consumer Networks

2020 ◽  
pp. 207-218
Author(s):  
P. Darsh ◽  
R. Rahul
Keyword(s):  
Performance Analysis ◽  
Anomaly Detection ◽  
Detection Systems ◽  
Consumer Networks ◽  
Network Anomaly Detection

scholarly journals Machine Learning Based Network Anomaly Detection

2019 ◽  
Vol 8 (4) ◽  
pp. 542-548 ◽  
Keyword(s):  
Anomaly Detection ◽  
Dynamic Change ◽  
Model Performance ◽  
Accuracy Score ◽  
Traffic Data ◽  
Two Phase ◽  
Data Set ◽  
Detection Systems ◽  
Network Anomaly Detection ◽  
Tools And Techniques

Network Anomaly Detection Systems (NADSs) play prominent role in network security. Due to dynamic change of malware in network traffic data, traditional tools and techniques are failing to protect networks from attack penetration. In this paper we propose a two-phase model to detect and categorize anomalies. First, we selected Random Forest based on the highest accuracy-score out of eleven commonly used algorithms tested with the same set of data. The RF is used to detect anomalies and generate an extra feature named “attack-or-not”. Secondly we fed Neural Network with the data having “attack-or-not” feature to differentiate attack categories, which will help treating each type accordingly. The model performance was good, it scored 0.99 for both Precision and Recall in anomaly detection phase and 0.93 for Precision and 0.88 for Recall in attack categorization phase. We used UNSW-NB15 data set in our study.

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.

Accuracy improving guidelines for network anomaly detection systems

2009 ◽  
Vol 7 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Ayesha Binte Ashfaq ◽  
Muhammad Qasim Ali ◽  
Syed Ali Khayam
Keyword(s):  
Anomaly Detection ◽  
Detection Systems ◽  
Network Anomaly Detection

scholarly journals An Evaluation Design for Comparing Netflow Based Network Anomaly Detection Systems Using Synthetic Malicious Traffic

2021 ◽  
Author(s):  
Shuvo Bardhan ◽  
Mitsuhiro Hatada ◽  
James Filliben ◽  
Douglas Montgomery ◽  
Alexander Jia
Keyword(s):  
Anomaly Detection ◽  
Evaluation Design ◽  
Detection Systems ◽  
Network Anomaly Detection
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