AN INTELLIGENT APPROACH TO DETECT INTRUSION OVER WIRELESS?

We use computers for banking and investing to shopping and communicating with others through email or chat programs. Computer and network of computer become the very important part of companies, organization and government sector. A lot of important information is stored in computers and transferred across networks and the internet. Unauthorized users may try to break into systems to have access to private information. This brings the need of a system that can detect and prevent those harmful activities. Intrusion detection systems (IDSs) monitor networks and/or systems to detect malicious activities. That helps us to re-act and stop intruders. There are two types of IDSs, networkbased IDSs and host-based IDSs. A network-based IDS monitor’s network traffic and activities to find attacks, and a host-based IDS monitors activities in a computer system to detect malicious actions. This thesis is a research on using Bayesian techniques in implementing a network-based IDS that can tell us a computer process is normal (harmless) or abnormal (harmful). We combine three techniques to build a IDS. In our system k2 algorithm is applied which main purpose is to incrementally add a node to a network, it means start with a single node than add another node to complete a network. Bayesian methods utilize a search-and-source procedure to search the space of DAGs, and use the posterior density as a scoring function and finally to construct a data structure called a junction tree which can be used to calculate any query through message passing on the tree. In a past a lot of research is done on a IDS but for a wireless network is little. We have combine three techniques to make sure that network is safe from unauthorized access and attacks.