Abstract
With the exponential growth of the internet among users worldwide, network engineers pose a great challenge in network security to identify intrusion activities. Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) are the tools used to defend against these intrusion activities. IDS has sometimes been prone to false alarms. Therefore, to improve IDS, machine learning method is used. The largest number of IDS data sets are available till date where some are unreliable to use, whereas some are out of date and some does not cover common updated attacks. CICIDS-2017 data set overcome above major flaws []. Consequently, this paper assesses the performance of CICIDS-2017 data set by applying various machine learning algorithms such as Convolution Neural Network (CNN), Naive Bayes (NB) and Random Forest (RF), RF with highly ranked features, RF with feature reduction techniques (PCA and SVD). Then the comparison study is done which shows Random Forest gives good result when compared with other algorithms.
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