Probabilistic reasoning mobile agent system for network testing

Telecommunication networks undergo continuous testing such as trouble shooting, fault isolation, vulnerability assessment, and intrusion detection, to ensure the proper operation of the network. Current testing schemes can sometimes overload the network, with increased bandwidth and resource usage. Networks have a dynamic nature, where the state of the network undergoes constant change, and the available set of tests to perform can be too large to be applied to the network simultaneously. In addition, certain testing schemes such as vulnerability and intrusion detection systems are considered as inefficient [2], lack a generic structured building strategy [12], and are difficult to upgrade [2].; We present an intelligent mobile agent system for testing telecommunication networks. The system is a general-purpose method that can be used for any type of network testing, for example, vulnerability assessment, and intrusion detection. The system consists of mobile agents equipped with tests (indices to tests) to be performed on targets in a network. The tests and the targets are selected using probabilistic reasoning in a manner that maximizes the probability of selection of problematic nodes in the network. The algorithm takes into consideration the fact that problems such as vulnerabilities and intrusions for example, tend to spread once they have entered the network. Thus once the algorithm detects a problematic node, it selects the most vulnerable nodes within the neighborhood of the selected node and applies the appropriate set of tests to them.; Each mobile agent carries a pointer to the set of tests to be performed on a specific node in the network. The nodes selected for testing constitute a subset of the available nodes in the network. The number of mobile agents deployed and the number of tests per agent are kept within the maximum allowable as dictated by the bandwidth and resource availability of the network. This is crucial in networks were bandwidth and resource limitations prohibit the application of large pools of tests simultaneously. Thus our method selects tests and targets in an optimum manner that ensures detection of any problems within the network in a timely and efficient manner, without overloading the network.