Introduction to Artificial Neural Network for Misuse Detection:

IDS (Intrusion Detection System) used host based IDs, network based IDS and vulnerability based IDS for detection of misuse.  It contains information source that’s provide a record of all events, and analysis the signs of intrusion, and also responses according to outcome of analysis engine.

Neural network consist different levels and every level has nodes… every node id connected to upper level of all the nodes and the number of nodes in each level keep on increasing. Neural network is used for detection of computer attacks, computer viruses, and malicious software in the computer.

Neural engine: it is based on intrusion detection, which establish the user profile watch their behavior. But it requires assumptions. For detection of intrusion training is required for every user once. Then they compare present data with historical data.  Every new data is filtered or checked. It should be kept on updating regularly for the new data might be introduced. When the new data is received and if it found doubtful then it is send to intrusion response system.

There are different levels of processing the data:

–          First level, all the elements of data are collected from protocol ID, source port, ICMP type, and ICMP code as raw data.

–          Second, converting them to numerical representation

–          Third, converting the result data into ASCII format that is used by neural network.

Advantages: nice speed, analyze incomplete distorted data.

Disadvantages: require accurate system for training, various nodes of network get frozen after they achieve level of success.

Conclusion: these networks have worked successfully and in future it can be used which may involve refinement for full scale demonstration of system.

Download Artificial Neural Network for Misuse Detection PPT.