Data stream classification Detection System Using Genetic Algorithm

Vol.6 No.1

Year: 2011

Issue: July-September

Title: Data stream classification Detection System Using Genetic Algorithm   

Author Name: Jeya S, S. Muthu Perumal Pillai   

Synopsis:   

The transmission of data over the Internet increases, the need to protect connected systems also increases. Although the field of IDSs is still developing, the systems that do exist are still not complete, in the sense that they are not able to detect all types of intrusions. Some attacks which are detected by various tools available today cannot be detected by other products, depending on the types and methods that they are built on. Data stream classification (DSC) Detection System Using Genetic Algorithm (GA) is the latest technology used for this purpose. The behavior of the genetic algorithm, a popular approach to search and optimization problems, is known to depend, among other factors, on the fitness function formula, the recombination operator, and the mutation operator. In real-world data stream classification problems, such as intrusion detection, text classification, and fault detection novel classes may appear at any time in the stream. Traditional data stream classification techniques would be unable to detect intrusion until the classification models are trained with Genetic algorithm. We applied this technique in network traffic. The network intrusion detection system should be adaptable to all type of critical situations arise in network. This is helpful for identification of complex anomalous behaviors. This work is focused on the TCP/IP network protocols. Data stream classification pose many challenges, some of which have not been addressed yet.