15,379 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
ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
Security of computers and the networks that connect them is increasingly
becoming of great significance. Computer security is defined as the protection
of computing systems against threats to confidentiality, integrity, and
availability. There are two types of intruders: the external intruders who are
unauthorized users of the machines they attack, and internal intruders, who
have permission to access the system with some restrictions. Due to the fact
that it is more and more improbable to a system administrator to recognize and
manually intervene to stop an attack, there is an increasing recognition that
ID systems should have a lot to earn on following its basic principles on the
behavior of complex natural systems, namely in what refers to
self-organization, allowing for a real distributed and collective perception of
this phenomena. With that aim in mind, the present work presents a
self-organized ant colony based intrusion detection system (ANTIDS) to detect
intrusions in a network infrastructure. The performance is compared among
conventional soft computing paradigms like Decision Trees, Support Vector
Machines and Linear Genetic Programming to model fast, online and efficient
intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special
track at WSTST 2005, Muroran, JAPA
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average
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