%0 Journal Article %1 samarjeetborah2013intrusion %A Samarjeet Borah, Anindita Chakraborty %D 2013 %E and Fabricio Moraes de Almeida, %J SCIENCE PARK %K Intrusion Neural Self detection map networks organizing system %N 2 %T Intrusion Detection System using Self Organizing Map (SOM): A Review %U http://www.scienceparks.in/ArticlePDF/9.pdf %V 1 %X As there is a rapid expansion in computer usage and its networks the security of computer system has become very essential. Everyday there are new kinds of attacks which are faced by the industries. Many algorithms are been proposed for the intrusion detection system development using artificial intelligence technique. One such algorithm is Self Organizing Map. The aim of intrusion detection system is to identify attacks with a high detection rate and low false alarm rate. The neural network which are capable of supervised learning after the characteristics of the user behavior are able to identify the abnormalities present, but they have their own disadvantages –they are not able to detect new intrusions. Consequently unsupervised learning methods were given a closer look. In the field of intrusion detection system the anomaly detection aspects is very important and thus there are many approaches that are addressing these security issues. The usage of Self Organizing Map (SOM) along with its different SOM algorithm is applied to the problem of host based intrusion detection networks.

@article{samarjeetborah2013intrusion, abstract = {As there is a rapid expansion in computer usage and its networks the security of computer system has become very essential. Everyday there are new kinds of attacks which are faced by the industries. Many algorithms are been proposed for the intrusion detection system development using artificial intelligence technique. One such algorithm is Self Organizing Map. The aim of intrusion detection system is to identify attacks with a high detection rate and low false alarm rate. The neural network which are capable of supervised learning after the characteristics of the user behavior are able to identify the abnormalities present, but they have their own disadvantages –they are not able to detect new intrusions. Consequently unsupervised learning methods were given a closer look. In the field of intrusion detection system the anomaly detection aspects is very important and thus there are many approaches that are addressing these security issues. The usage of Self Organizing Map (SOM) along with its different SOM algorithm is applied to the problem of host based intrusion detection networks. }, added-at = {2014-08-15T11:11:55.000+0200}, author = {Samarjeet Borah, Anindita Chakraborty}, biburl = {https://www.bibsonomy.org/bibtex/2125ad3ada730c41494220d446bbf064b/sciencejournal}, editor = {and Fabricio Moraes de Almeida}, interhash = {a7f54853a8a066bb74558b39afc381e2}, intrahash = {125ad3ada730c41494220d446bbf064b}, journal = { SCIENCE PARK}, keywords = {Intrusion Neural Self detection map networks organizing system}, month = {Augst}, number = 2, timestamp = {2014-08-15T11:11:55.000+0200}, title = { Intrusion Detection System using Self Organizing Map (SOM): A Review}, url = {http://www.scienceparks.in/ArticlePDF/9.pdf}, volume = 1, year = 2013 }