The Long-Short-Term-Memory (LSTM) models obtained by training on the CPTC data enable the assessment of the differentiability of attack behaviors across teams ...

This paper investigates the uses of LSTM-based neural networks to learn predictive models based on potentially non-stationary cyberattack data. This research ...

The use of RNNs to model penetration behaviors exhibited by ten teams in the 2017 Collegiate Penetration Testing Competition are presented and the ...

This kind of method uses representation learning in Euclidean space and non-Euclidean space to form the high-dimensional representation of the relationship ...

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Jul 1, 2024 · The classification of these features is done using the gated attention dual long short term memory (LSTM), which helps to remember the attacks ...

The prediction of load behavior is accurate globally along with local details as presented in the experiments, which verify the effectiveness of the proposed ...

Apr 19, 2024 · Their model demonstrates the LSTM's capability to adapt to different network behaviors and effectively identify potential security threats (Khan ...

32 , in their study on differentiating and predicting cyberattack behaviours applied LSTM-RNNs using the 2017 Collegiate Penetration Testing. Competition (CPTC) ...

Oct 7, 2023 · We propose an AI model-based DL and different machine and ensemble learning classifiers to detect cyber-attacks on the IoT with SMOTE (Synthetic ...