Variance Fractal Dimension Feature Selection for Detection of Cyber Security Attacks

In an era where machine learning algorithms are widely used in order to improve the performance of network intrusion detection system, the complexity and big volume of data available in the network are also on the rise. The cyber networks frequently encounter high-dimensional, unreliable, and redundant data that are often too large to process. An efficient feature selection can therefore remove the redundant and irrelevant attributes and select relevant attributes that can significantly improve the overall system performance. This research provides a variance fractal dimension feature selection method to explore the significant features of cyber security attack dataset. A complexity analysis was done to find out the cognitive discriminative features of UNSW-NB15 dataset. A performance comparison is also provided using our proposed methodology for an artificial neural network, and a comparative analysis was also done that shows the proposed method helps improve the detection performance in network system. The resultant discriminative features not only consume less resource but also speed up the training and testing process while maintaining good detection rates.