The application of Baum-Welch algorithm in multistep attack.

Area of Science:

• Cybersecurity
• Machine Learning
• Network Security

Background:

• Determining observations for hidden Markov models in multistep attacks is challenging and subjective.
• Existing research on observation determination for HMMs in cybersecurity lacks comprehensive methods.

Purpose of the Study:

• To develop a novel method for forecasting multistep attacks by integrating attack intentions with hidden Markov models (HMMs).
• To enhance the accuracy of multistep attack recognition and prediction using trained HMMs.

Main Methods:

• Trained existing hidden Markov models using the Baum-Welch algorithm.
• Recognized attack scenarios using the HMM Forward algorithm.
• Forecasted potential attack sequences with the HMM Viterbi algorithm.

Main Results:

• Simulation experiments demonstrated superior performance of trained HMMs over untrained models.
• The proposed method showed improved accuracy in recognizing and predicting multistep attack sequences.
• Integration of attack intentions enhanced the HMM's predictive capabilities.

Conclusions:

• The developed method effectively forecasts multistep attacks by leveraging trained HMMs and attack intentions.
• Trained hidden Markov models offer a more objective and accurate approach to cybersecurity threat prediction.
• This research addresses the subjectivity issue in HMM observation determination for cyberattack analysis.