Online Transition-Based Feature Generation for Anomaly Detection in Concurrent Data Streams
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Abstract (arXiv)
In this paper, we introduce the transition-based feature generator (TFGen) technique, which reads general activity data with attributes and generates step-by-step generated data. The activity data may consist of network activity from packets, system calls from processes or classified activity from surveillance cameras. TFGen processes data online and will generate data with encoded historical data for each incoming activity with high computational efficiency. The input activities may concurrently originate from distinct traces or channels. The technique aims to address issues such as domain-independent applicability, the ability to discover global process structures, the encoding of time-series data, and online processing capability.
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References · 5
- 2018Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detectioncited by 121
- 2017A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Datacited by 8
- 2019Anomaly-based Intrusion Detection in Industrial Data with SVM and Random Forestscited by 4
- 2022Can process mining help in anomaly-based intrusion detection?cited by 2
- 2022Process Mining Algorithm for Online Intrusion Detection Systemcited by 1
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