In this contemporary technology-motivated era, shielding our private information from being accessed by unauthorized users is becoming more intricate, vastly confidential information are becoming more accessible by public databases, because we are more interconnected than ever. Thus, our information is available for almost anyone to filter due to this interconnectivity, and this creates a pessimistic mindset that the use of technology is hazardous, unpredictable and highly unprotective because virtually anyone can access one’s private information for an outlay. The weaknesses discovered from the previous work are the key motivation for this research work. These includes: The work done on Network Intrusion Detection using Association Rules which generated an incomprehensive set of attack rules due to the small percentage of KDD’99 data set used for training set, proposed wrapper method for feature selection in multiple class data set using a sequential backward elimination method which is more computationally expensive and time consuming, and Development of a Denial of Service attack detection using machine learning technique in which the Significant features of data set were not extracted, and the extraction was done using only one extraction technique which results in high level of FAR (False Alarm Rate) due to poor detection of attacks. This research makes use of NSL-KDD and UNSW-NB15 data set, with filter and wrapper method as the feature selection techniques. In addition, an intrusion detection model was developed based association rule and support vector machine and performance of the model was evaluated.

Intrusion detection Machine learning security Data analysis Data Science.