Study on implementation of machine learning methods combination for improving attacks detection accuracy on Intrusion Detection System (IDS)

Abstract

Many computer-based devices are now connected to the internet technology. These devices are widely used to manage critical infrastructure such energy, aviation, mining, banking and transportation. The strategic value of the data and the information transmitted over the Internet infrastructure has a very high economic value. With the increasing value of the data and the information, the higher the threats and attacks on such data and information. Statistical data shows a significant increase in threats to cyber security. The Government is aware of the threats to cyber security and respond to cyber security system that can perform early detection of threats and attacks the internet. The success of a nation's cyber security system depends on the extent to which it is able to produce independently their cyber defense system. Independence is manifested in the form of the ability to process, analyze and create an action to prevent threats or attacks originating from within and outside the country. One of the systems can be developed independently is Intrusion Detection System (IDS) which is very useful for early detection of cyber threats and attacks. The advantages of an IDS is determined by its ability to detect cyber attacks with little false. This study learn how to implement a combination of various methods of machine-learning to the IDS to improve the accuracy in detecting attacks. This study is expected to produce a prototype IDS. This prototype IDS, will be equipped with a combination of machine-learning methods to improve the accuracy in detecting various attacks. The addition of machine-learning feature is expected to identify the specific characteristics of the attacks occurred in the Indonesian Internet network. Novel methods used and techniques in implementation and the national strategic value are becoming the unique value and advantages of this research.