A 16-factor structured prompt framework strengthens CoT reasoning in LLMs for security analysis, yielding up to 40% reasoning gains in smaller models and stable accuracy improvements validated by human raters with Cohen's k > 0.80.
In: 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET), pp
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Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework
A 16-factor structured prompt framework strengthens CoT reasoning in LLMs for security analysis, yielding up to 40% reasoning gains in smaller models and stable accuracy improvements validated by human raters with Cohen's k > 0.80.
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XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles
XGBoost with SHAP and statistical distribution analysis on UAVIDS-2025 identifies density support intersection as the cause of false predictions for Wormhole and Blackhole attacks in UAV intrusion detection.