Detection of Malicious Activities in Computer Network Using Soft Computing

Vol. 9  Issue 1
Year:2014
Issue:Jul-Sep
Title:Detection of Malicious Activities in Computer Network Using Soft Computing
Author Name:D.P. Gaikwad, Kiran Kale, Shabnoor Pathan, Lokesh Chandawar and Vivek Firake 
Synopsis:
With the increased use of internet, cyber threats have increased exponentially. To prevent our system from such threats, we need an anomaly detection system that will inspect all the network activities and identify any suspicious pattern that may indicate breach of security resulting in damage of computing resources. In this paper, the authors are introducing anomaly detection system that uses multilayer perceptron, a model of Artificial Neural Network (ANN). In this system, Multilayer Perceptron uses backpropagation learning algorithm. For training and testing purpose, they have used NSLKDD dataset. The trained model of Multilayer Perceptron is then used for real-time anomaly detection using tcpdump (packet sniffing tool in Linux). This system has successfully achieved a very low false-positive rate.