![]() One of the mechanisms at the forefront of attack detection are Intrusion Detection Systems (IDS). This is why the development of appropriate mechanisms to defend against hackers and malware is crucial. Furthermore, attacks on critical infrastructure, such as water treatment plants or power stations, can have dire consequences. Increasingly frequent leaks cause users to lose confidence in whether their data is being kept secure. ![]() The gathered dataset is part of an effort to bring security against novel cyberthreats and was completed in the SIMARGL project. To investigate the stability of the obtained models, cross-validation was performed, and an array of detection metrics were reported. In this work, the capability of the dataset for formulating machine-learning-based models was experimentally evaluated. The dataset contains 44 network features and an unbalanced distribution of classes. Real-life traffic was collected and, after performing a series of attacks, a dataset was assembled. The main contribution of this work is a dataset coming from a real-world, academic network. This paper introduces the effects of using machine-learning-based intrusion detection methods in network traffic coming from a real-life architecture. This situation creates a constant need for novel, realistic cybersecurity datasets. ![]() Cybersecurity is an arms race, with both the security and the adversaries attempting to outsmart one another, coming up with new attacks, new ways to defend against those attacks, and again with new ways to circumvent those defences. ![]()
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