Intrusion Detection: A Deep Learning Approach
Ishaan Shivhare Joy Purohit Vinay Jogani Samina Attari Dr. Madhav Chandane
Network intrusions are a significant problem in all industries today. A
critical part of the solution is being able to effectively detect intrusions.
With recent advances in artificial intelligence, current research has begun
adopting deep learning approaches for intrusion detection. Current approaches
for multi-class intrusion detection include the use of a deep neural network.
However, it fails to take into account spatial relationships between the data
objects and long term dependencies present in the dataset. The paper proposes a
novel architecture to combat intrusion detection that has a Convolutional
Neural Network (CNN) module, along with a Long Short Term Memory(LSTM) module
and with a Support Vector Machine (SVM) classification function. The analysis
is followed by a comparison of both conventional machine learning techniques
and deep learning methodologies, which highlights areas that could be further
explored.