Machine Learning for a Network-based Intrusion Detection System : An application using Zeek and the CICIDS2017 dataset
Sammanfattning: Cyber security is an emerging ﬁeld in the IT-sector. As more devices are connected to the internet, the attack surface for hackers is steadily increasing. Network-based Intrusion Detection Systems (NIDS) can be used to detect malicious traﬃc in networks and Machine Learning is an up and coming approach for improving the detection rate. In this thesis the NIDS Zeek is used to extract features based on time and data size from network traﬃc. The features are then analyzed with Machine Learning in Scikit-Learn in order to detect malicious trafﬁc. A 98.58% Bayesian detection rate was achieved for the CICIDS2017 which is about the same level as the results from previous works on CICIDS2017 (without Zeek). The best performing algorithms were K-Nearest Neighbors, Random Forest and Decision Tree.
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