IMPLEMENTASI BAYESIAN BELIEF NETWORK UNTUK SISTEM MANAJEMEN KEAMANAN JARINGAN DI JURUSAN TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI FT UGM

Data stored in the agencies is very potential to be infected with the threats. Department of Electrical Engineering and Information Technology (JTETI) is an institution which has a local area network, with a wired or wireless medium. Security systems have been built, but it does not rule out the possibility that the network at JTETI will resistant to the threats. Given the importance of data in the network in JTETI, conducted research related to the monitoring of existing network security system. This research is engaged as a networks security field, implementing Bayesian Belief Network (BBN) as the method. BBN accommodate quantitative and qualitative calculations that can be used to improve the accuracy of the assessment of probability value (or possibility) of an IP address is a threat to the network. Input from the calculation are the results of monitoring by Network Intrusion Detection System (NIDS) installed at the back-end network and conducted in a period of time. As for the qualitative part will be modeled using Directed Acyclic Graph (DAG). After monitoring, it was found that the incoming attack can divided into four categories, namely: TCP attacks, UDP attacks, ICMP attacks, and Portscanning. For modeling using BBN, Eight supporting variables are made as a results given by the attack monitoring categories. These variables consist of 7 predictor variables and 1 response variables. Threshold determined that an IP address is a threat positive that its generate ThreatPositive values above 0.6. After doing modeling and calculations, it was found that the variable Portscanning and Ping are the most influential variable on the status of threats of an IP Address. When Portscanning or Ping is not enabled (or their evidence on the variable state is not observed) the probability value of a ThreatPositive state of IP addresses is below the threat�s threshold (0.6). Keywords: Network Security, threats, NIDS, Bayesian Belief Networ