Multi-layer perceptron for network intrusion detection
Résumé
The Internet connection is becoming ubiquitous in embedded systems, making them potential victims of intrusion. Although gaining popularity in recent years, deep learning based intrusion detection systems tend to produce worse results than those using traditional machine learning algorithms. On the contrary, we propose an end-to-end methodology allowing a neural network to outperform traditional machine learning algorithms. We demonstrate high performance score on CIC-IDS2017 data set, showing an accuracy greater than 99% and a false positive rate lower than 0.5%. Our results are compared to traditional machine learning algorithms and previous Then, we show that our approach can be successfully applied to CSE-CIC-IDS2018 data set, confirming that neural network can reach better scores than other machine learning algorithms. Our performance is compared to previous work on this data set. We further deployed our solution on a system-on-chip for automotive, allowing to characterize real-time performance aspect on an embedded system, both for feature extraction and inference. Finally, a discussion opens up on problems related to some attacks that are particularly difficult to detect with flow-based techniques and weaknesses found in the data sets.