Improving malware detection performance using hybrid deep representation learning with heuristic search algorithms

Today, smartphones are used by the majority of internet users worldwide, and Android has become the most popular smartphone operating system on the market. The growth in the use of smartphones in general, and the Android system specifically, results in a stronger requirement to successfully protect Android, as malware developers aim to create advanced and sophisticated malware applications. Cybercriminals utilize fraudulent attack tactics, namely obfuscation or dynamic code triggering, to evade the system. A standard static investigation method failed to recognize such attacks. Mitigating a wide variety of evasive attacks requires a refined, dynamic, and analytical framework. Conventional artificial intelligence (AI), particularly machine learning (ML) methodologies, are no longer effective in detecting all new and complex malware types. A deep learning (DL) model, which is very different from conventional ML models, has a possible solution to the detection issue of each version of malware. In this manuscript, an Approach for Improving Malware Detection Performance Using a Hybrid Deep Learning Framework (IMDP-HDL) is proposed. The primary objective of the IMDP-HDL methodology is to ensure the effective and scalable deployment of malware detection in real-world cybersecurity environments. Initially, the Z-score standardization is utilized to ensure consistent feature scaling and model performance. For the malware detection process, a hybrid model combining a convolutional neural network, bi-directional long short-term memory, and self-attention mechanism (CBiLSTM-SA) is employed. A broad range of experimentation with the IMDP-HDL model is performed using the Android malware dataset. The comparison analysis of the IMDP-HDL model demonstrated a superior accuracy value of 99.22% over existing techniques.