K-nearst Neigbour (Knn) untuk Mendeteksi Gangguan Jaringan Komputer pada Intrusion Detection Dataset

Internet increasing is also exponentially increasing intrusion or attacks by crackers exploit vulnerabilitiesin Internet protocols, operating systems and software applications. Intrusion or attacks against computernet works, especially the Internet has increased from year to year. Intrusion detection systems into the main stream in the information security. The main purpose of intrusion detection system is a computer system to help deal with the attack. This study presents k-nearest neigbour algorithm to detect computer network intrusions. Performance is measured based on the level of accuracy, sensitivity, precision and spesificity. Dataset used in this study is a dataset KDD99 intrusion detection system. Dataset is composed of two training data and testing data. From the experimental results obtained by the accuracy of k-nearest neigbour algorithm is about 79,36%

Hierarchical wireless framework for real-time collaborative generation and distribution of telemetry data

This project introduces a novel multidisciplinary approach combining Vehicular Ad Hoc Networks and Granular Computing, to the data processing and information generation problem in large urban traffic systems. It addresses the challenge of realtime information generation and dissemination in such systems by designing and investigating a hierarchical real-time information framework. The research work is complemented by designing and developing a simulator for such a system, which provides a simulation environment for the model developed. The proposed multidisciplinary hierarchical real-time information processing and dissemination system framework utilises results from two different areas of study, which are Vehicular Ad Hoc Networks (VANETS) and Granular Computing concepts. Furthermore, a new geographically constrained VANET topology for information generation is proposed, simulated and investigated