Real-Time Connection Monitoring of Ubiquitous Networks for Intrusion Prediction: A Sequential KNN Voting Approach

In the ubiquitous network environment where numerous devices are connecting each other, it is believed that security will play an important role in overall network management. And the wireless sensor network (WSN) is commonly considered to be one of such networks prone to a wide range of attacks due to its inherent characteristics. For the sound operation of WSN, it is important to block malicious connections from the network as early as possible. This paper proposes a novel approach to real-time monitoring of network by using the sequential K NN voting. When connection data is sequentially recorded on the log, the final result of ongoing behavior is predicted probabilistically with only partial data, which iterates consecutively as additional connection data are accumulated to the log. Once this predicted probability reaches certain preset threshold value for possible network intrusion, then we can do some preventive actions for this ongoing connection. The value of this research lies in that the eventualities are predicted at the early stage of connection with partial information available. Since the prediction uses sequential K NN voting, the accuracy of our approach can be even more enhanced as with the volume of log grows.