The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need for intelligent systems to accurately and proactively identify and prevent malware infections. This study presents a new hybrid context-aware malware detection framework(HCAMDF) based on artificial intelligence (AI), which combines static file analysis, dynamic behavioural analysis, and contextual metadata to provide more accurate and timely detection. HCADMF has a multi-layer architecture, which consists of lightweight static classifiers such as Long Short Term Memory (LSTM) for real-time behavioral analysis, and an ensemble risk scoring through the integration of multiple layers of prediction. Experimental evaluations of the new/methodology with benchmark datasets, EMBER and CIC-MalMem2022, showed that the new approach provides superior performances with an accuracy of 97.3%, only a 1.5% false positive rate and minimal detection delay compared to several existing machine learning(ML) and deep learning(DL) established methods in the same fields. The results show strong evidence that hybrid AI can detect both existing and novel malware variants, and lay the foundation on intelligent security systems that can enable real-time detection and adapt to a rapidly evolving threat landscape.

翻译：恶意软件攻击频率与复杂性的持续攀升被视为现代数字基础设施面临的重大威胁，这意味着传统基于特征签名的检测方法正日益失效。随着网络威胁的不断演进，亟需智能系统能够准确、主动地识别并预防恶意软件感染。本研究提出一种基于人工智能（AI）的新型混合上下文感知恶意软件检测框架（HCAMDF），该框架融合静态文件分析、动态行为分析与上下文元数据，以实现更精准、更及时的检测。HCAMDF采用多层架构，包含用于实时行为分析的轻量级静态分类器（如长短期记忆网络LSTM），并通过整合多层预测结果实现集成风险评分。基于基准数据集EMBER与CIC-MalMem2022的实验评估表明，相较于同领域多种现有机器学习（ML）与深度学习（DL）成熟方法，新方法展现出更优性能：准确率达97.3%，误报率仅为1.5%，且检测延迟极低。研究结果有力证明，混合人工智能技术能够有效检测已知及新型恶意软件变体，为构建具备实时检测能力、可适应快速演变威胁态势的智能安全系统奠定基础。