Research Area: Software Defined Networks
Security analysts and administrators face a lot of challenges to detect and prevent network intrusions in their organizations, and to prevent network breaches, detecting the breach on time is crucial. Challenges arise while detecting unforeseen attacks. This work includes a performance comparison of classical machine learning approaches that require vast feature engineering, vs integrated unsupervised feature learning and deep neural networks on the NSL-KDD dataset. Various trials of experiments were run to identify suitable hyperparameters and network configurations of machine learning models. The DNN using 15 features extracted using Principal Component analysis (PCA) was the most effective modeling method. The further analysis using the Software Defined Networking features also presented a good accuracy using Deep Neural network.
Keywords:
Intrusion detection systems
machine learning
unsupervised
feature learning
deep neural network
Author(s) Name: Shisrut Rawat, Aishwarya Srinivasan, Vinayakumar Ravi, Uttam Ghosh
Journal name: Internet Technology Letters
Conferrence name:
Publisher name: Wiley
DOI: 10.1002/itl2.232
Volume Information: Volume 5, Issue 1
