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Sureswaran Ramadass

Publications and source records attributed to Sureswaran Ramadass.

3 recordsLinked to original sources
arXiv2013

A Hybrid Rule Based Fuzzy-Neural Expert System For Passive Network Monitoring

An enhanced approach for network monitoring is to create a network monitoring tool that has artificial intelligence characteristics. There are a number of approaches available. One such approach is by the use of a combination of rule based, fuzzy logic and neural networks to create a hybrid ANFIS system. Such system will have a dual knowledge database approach. One containing membership function values to compare to and do deductive reasoning and another database with rules deductively formulated by an expert (a network administrator). The knowledge database will be updated continuously with newly acquired patterns. In short, the system will be composed of 2 parts, learning from data sets and fine-tuning the knowledge-base using neural network and the use of fuzzy logic in making decision based on the rules and membership functions inside the knowledge base. This paper will discuss the idea, steps and issues involved in creating such a system.

cs.AI↗
arXiv2009

Detecting Botnet Activities Based on Abnormal DNS traffic

IThe botnet is considered as a critical issue of the Internet due to its fast growing mechanism and affect. Recently, Botnets have utilized the DNS and query DNS server just like any legitimate hosts. In this case, it is difficult to distinguish between the legitimate DNS traffic and illegitimate DNS traffic. It is important to build a suitable solution for botnet detection in the DNS traffic and consequently protect the network from the malicious Botnets activities. In this paper, a simple mechanism is proposed to monitors the DNS traffic and detects the abnormal DNS traffic issued by the botnet based on the fact that botnets appear as a group of hosts periodically. The proposed mechanism is also able to classify the DNS traffic requested by group of hosts (group behavior) and single hosts (individual behavior), consequently detect the abnormal domain name issued by the malicious Botnets. Finally, the experimental results proved that the proposed mechanism is robust and able to classify DNS traffic, and efficiently detects the botnet activity with average detection rate of 89 percent.

cs.NI↗