90,731 research outputs found
An overview to Software Architecture in Intrusion Detection System
Today by growing network systems, security is a key feature of each network
infrastructure. Network Intrusion Detection Systems (IDS) provide defense model
for all security threats which are harmful to any network. The IDS could detect
and block attack-related network traffic. The network control is a complex
model. Implementation of an IDS could make delay in the network. Several
software-based network intrusion detection systems are developed. However, the
model has a problem with high speed traffic. This paper reviews of many type of
software architecture in intrusion detection systems and describes the design
and implementation of a high-performance network intrusion detection system
that combines the use of software-based network intrusion detection sensors and
a network processor board. The network processor which is a hardware-based
model could acts as a customized load balancing splitter. This model cooperates
with a set of modified content-based network intrusion detection sensors rather
than IDS in processing network traffic and controls the high-speed.Comment: 8 Pages, International Journal of Soft Computing and Software
Engineering [JSCSE]. arXiv admin note: text overlap with arXiv:1101.0241 by
other author
CONDOR: A Hybrid IDS to Offer Improved Intrusion Detection
Intrusion Detection Systems are an accepted and very
useful option to monitor, and detect malicious activities.
However, Intrusion Detection Systems have inherent limitations which lead to false positives and false negatives; we propose that combining signature and anomaly based IDSs should be examined. This paper contrasts signature and anomaly-based IDSs, and critiques some proposals about hybrid IDSs with signature and heuristic capabilities, before considering some of their contributions in order to include them as main features of a new hybrid IDS named CONDOR (COmbined Network intrusion Detection ORientate), which is designed to offer superior pattern analysis and anomaly detection by reducing false positive rates and administrator intervention
A taxonomy of malicious traffic for intrusion detection systems
With the increasing number of network threats it is essential to have a knowledge of existing and new network threats to design better intrusion detection systems. In this paper we propose a taxonomy for classifying network attacks in a consistent way, allowing security researchers to focus their efforts on creating accurate intrusion detection systems and targeted datasets
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