%0 Journal Article %1 Abraham:2007:JNCS %A Abraham, Ajith %A Jain, Ravi %A Thomas, Johnson %A Hana, Sang Yong %D 2007 %J Journal of Network and Computer Applications %K algorithms, genetic programming %N 1 %P 81--98 %R doi:10.1016/j.jnca.2005.06.001 %T D-SCIDS: Distributed soft computing intrusion detection system %V 30 %X An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. A Distributed IDS (DIDS) consists of several IDS over a large network (s), all of which communicate with each other, or with a central server that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using co-operative intelligent agents distributed across the network(s). This paper evaluates three fuzzy rule-based classifiers to detect intrusions in a network. Results are then compared with other machine learning techniques like decision trees, support vector machines and linear genetic programming. Further, we modelled Distributed Soft Computing-based IDS (D-SCIDS) as a combination of different classifiers to model lightweight and more accurate (heavy weight) IDS. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.

@article{Abraham:2007:JNCS, abstract = {An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. A Distributed IDS (DIDS) consists of several IDS over a large network (s), all of which communicate with each other, or with a central server that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using co-operative intelligent agents distributed across the network(s). This paper evaluates three fuzzy rule-based classifiers to detect intrusions in a network. Results are then compared with other machine learning techniques like decision trees, support vector machines and linear genetic programming. Further, we modelled Distributed Soft Computing-based IDS (D-SCIDS) as a combination of different classifiers to model lightweight and more accurate (heavy weight) IDS. Empirical results clearly show that soft computing approach could play a major role for intrusion detection.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Abraham, Ajith and Jain, Ravi and Thomas, Johnson and Hana, Sang Yong}, biburl = {https://www.bibsonomy.org/bibtex/24f4921d97e062cec8c64b14a733020aa/brazovayeye}, doi = {doi:10.1016/j.jnca.2005.06.001}, interhash = {6de869a153a8b4d15aaee83013303305}, intrahash = {4f4921d97e062cec8c64b14a733020aa}, journal = {Journal of Network and Computer Applications}, keywords = {algorithms, genetic programming}, month = {January}, number = 1, pages = {81--98}, timestamp = {2008-06-19T17:35:12.000+0200}, title = {{D}-{SCIDS}: Distributed soft computing intrusion detection system}, volume = 30, year = 2007 }