Network Intrusion Detection and Measuring the Data Set Performance by Machine Learning Technique (MLT)

Abstract

Intrusion Detection System (IDS) is the most mainstream approach to protect a computer network from different malicious activities to identify an intrusion. There have been a lot of attempts towards more exceptional performance specifically in IDSs which depends on Data Mining (DM) and Machine Learning Techniques (MLT). Though there is a destructive issue in that available assessment, DataSet (DS), called KDD DS, can't reflect current network circumstances and the most recent attack situations. As far as we could know, there is no possible assessment DS. We present a novel evaluation DS in this paper, called Kyoto, based on the 5 years of actual traffic information, which derived from different sorts of honey pots. This Kyoto DS is utilized for testing and assessing distinctive MLT has examined in this work. The attention was on unprocessed measurements True +ve (TrPo), False +ve (FaPo), True – ve (TrNa), and False – ve (FaNa) to assess execution and to improve the identification rate of IDS.