Classification Denial of Service (DOS) Attack using Artificial Neural Network Learning Vector Quantization (LVQ)

Reza Firsandaya Malik (Computer Engineering Department Faculty of Computer Science, Sriwijaya University)
Verlly Puspita (Computer Engineering Department Faculty of Computer Science, Sriwijaya University)

Proceeding of the Electrical Engineering Computer Science and Informatics
Vol 1: EECSI 2014

Network security is an important aspect in computer network defense. There are many threats find vulnerabilities and exploits for launching attacks. Threats that purpose to prevent users get the service of the system is Denial of Service (DoS). One of software application that can detect intrusion on is an Intrusion Detection System (IDS). IDS is a defense system to detect suspicious activity on the network. IDS has ability to categorize the various types of attack and not attack. In this research, Learning Vector Quantization (LVQ) neural network is used to classify the type of attacks. LVQ is a method to study the competitive supervised layer. If two input vectors approximately equal, then the competitive layers will put both the input vector into the same class. The results show IDS able to classify PING and UDP Floods are 100%.