This paper describes the use of a novel incremental learning algorithm for HMMs that allows the efficient acquisition of anomaly detection models. The new ...

A novel incremental learning algorithm for HMMs is described that allows the efficient acquisition of anomaly detection models and requires less memory and ...

A hidden Markov model (HMM) is a useful tool to model sequence information, an optimal modeling technique to minimize false-positive error while maximizing ...

This paper proposes an effective HMM-based intrusion detection system that improves the modeling time and performance by only considering the privilege ...

Oct 22, 2024 · Hidden Markov model is a technique which consists of number of states having initial transition of data and at each transition from one state to ...

Previous work into automated anomaly detection has focused primarily on methods that discretize high dimensional continuous spaces into discrete symbols.

Anomaly-based intrusion detection systems approach the problem of attack detection by detecting when traffic differs from a learned baseline. In the case of.

Examples of generative models are finite state automata (FSA), hidden markov models (HMM), and mixture of HMMs.

In discrete hidden Markov model, each state will only give out one observation, then state will transfer to another state or itself. But here in our model, each ...

Oct 12, 2020 · In this paper, we address the problem of anomaly detection for the discrete manufacturing systems with complicated processes, including parallel ...