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This idea is the one we have adapted to the context of multi-step detection.
Our findings show that a detection algorithm based on routinely available data may be a useful first step in a multi-step detection procedure in CHP practice.
Apart from multi-step attack detection, it has been previously applied to anomaly detection in time series [60].
In response to this, we propose the architecture of an engineering system called OMMA, Operator-guided Monitoring of Multi-step Attacks, for integration of multi-step attack detection methods working with heterogeneous sets of events.
The ideas reflected on its design are based on three axes: our experience in security research, our knowledge about attack detection methods used in the industry, and the study of bibliography about multi-step attack detection.
We consider significant that most of the work in the field of multi-step attack detection do not directly analyze events but alerts generated by a signature-based Intrusion Detection System, looking for scenarios whose bricks are in turn composed of detected attacks.
This workflow utilizes multi-step hybrid detection and classification routine which makes use of a neural network based autopicker for initial picking and an evolutionary algorithm for pick refinement.
Event correlation is therefore a convenient approach for multi-step attack detection.
Hidden Markov Models (HMMs) have also been used for multi-step attack detection.
OMMA engineering system is oriented towards multi-step threat detection through event analysis.
Based on the TAM image, the multi-step shadow detection algorithm is previously proposed [1].
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