Title
Fates: A Granular Approach to Real-Time Anomaly Detection
Abstract
Anomaly-based intrusion detection systems have the ability of detecting novel attacks, but in real-time detection, they face the challenges of producing many false alarms and failing to contend with the high speed of modern networks due to their computationally demanding algorithms. In this paper, we present Fates, an anomaly-based NIDS designed to alleviate the two challenges. Fates views the monitored network as a collection of individual hosts instead of as a single autonomous entity and uses dynamic, individual threshold for each monitored host, such that it can differentiate between characteristics of individual hosts and independently assess their threat to the network. Each packet to and from a monitored host is analyzed with an adaptive and efficient charging scheme that considers the packet's type, number of occurrences, source, and destination. The resulting charge is applied to the individual hosts' threat assessment, providing pinpointed analysis of anomalous activities. We use various datasets to validate Fates's ability to distinguish scanning behavior from benign traffic in real time.
Year
DOI
Venue
2007
10.1109/ICCCN.2007.4317884
ICCCN
Keywords
Field
DocType
anomaly-based detection,network host monitoring,network traffic,network-based intrusion detection system,computer network management,real-time anomaly detection,fates real-time anomaly-based network intrusion detection system,telecommunication security,telecommunication traffic,index terms—network-based intrusion detection system,real-time systems,anomaly detection,real time,indexing terms,real time systems,intrusion detection system
Computer network management,Anomaly detection,Computer science,Network packet,Computer network,Telecommunication security,Real-time computing,Threat assessment,Intrusion detection system
Conference
ISSN
ISBN
Citations 
1095-2055 E-ISBN : 978-1-4244-1251-8
978-1-4244-1251-8
1
PageRank 
References 
Authors
0.35
13
2
Name
Order
Citations
PageRank
Jeff Janies1918.24
Chin-Tser Huang228545.72