Context-Aware Malware Scoring: A Multi-Model Ensemble Approach using Structured and Unstructured Data

Current malware threats are becoming challenging to detect through traditional identification methods and they appear faster than before. The present malware systems lack capability to analyze multiple context elements which include network behavior alongside file metadata and flags thus creating assessment issues with threat scoring. Mainstream aproaches including static and dynamic analysis struggle to handle the latest and advanced threats including ransomware and spyware along with advanced persistent threats.This paper presents Context-Aware Malware Scoring AI which involves transformers particularly BERT for text-based data and ensemble learning techniques used to boost malware threat assessment capabilities. A system combining numerical features like added, started, duration, id as well as target_size works with text data that receives BERT embedding processing. The system includes numeric feature forecasting by XGBoost, LightGBM, Random Forest and text feature processing by BERT with PCA and Ridge regression before using an ensemble model to merge all methods that increase prediction accuracy.The tests and analysis of various machine learning approaches demonstrate that XGBoost and Random Forest deliver their peak results with numerical inputs achieving R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> scores of 0.79 each. LightGBM, while computationally efficient, shows slightly lower predictive accuracy with an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.65. Making sense of textual embeddings with BERT + Ridge regression provides a prediction accuracy score of 0.13 since this approach requires optimization of the processing system for text inputs. The ensemble achieves excellent performance by obtaining 0.85 R2 and minimum errors which include 1.05 MAE, 3.59 MSE, 1.89 RMSE and 36.53% SMAPE. Hybrid detection methods prove effective for malware threat identification because numerical signals and textual indicators provide supporting evidence to each other.A solution combating growing malware threats emerged from combining transformers with ensemble learning to deliver superior accuracy and generalization over traditional detection systems according to the obtained results. Security defenses can achieve significant improvement through this method as it works toward combating complex evolving threats.