Intruder Detection Using Deep Learning and Association Rule Mining
Asantha Thilina, Shakthi Attanayake, S. J. M. M. B. Samarakoon, Dahami Nawodya, Lakmal Rupasinghe, Nadith Pathirage, Tharindu Edirisinghe, Kesavan Krishnadeva · 2016
With the upsurge of internet popularity, nowadays there are millions of online transactions that are being processed per minute thus increasing the possibilities of intruder attacks over the recent times. There have been various intruder detection techniques such as using traditional machine learning based algorithms. These algorithms were widely used to identify and prevent intruder activities in the recent past. Furthermore, multilayer neural networks[5] were also used in this regard to perform the detection. Hence multi-layer neural networks inherit fundamental drawbacks due to its inability to perform training due the problems such as overfitting, etc. In contrast, deep learning algorithms were introduced to overcome these issues effectively. We propose a novel framework to perform intruder detection and analysis using deep learning nets and association rule mining. We utilize a recurrent network to predict intruder activities and FP-Growth to perform the analysis. Our results show the effectiveness of our framework in detail.