An intelligent ransomware based cyberthreat detection model using multi head attention-based recurrent neural networks with optimization algorithm in IoT environment.

The rapid growth of the Internet of Things (IoT) and its extensive use in many regions, such as smart homes, healthcare, and vehicles, have made IoT security increasingly critical. Ransomware is an advanced and adjustable threat influencing users globally, limiting admittance to their data or systems over models like file encryption or screen locking. Traditional ransomware detection methods frequently drop, deprived of the ability to combat these threats successfully. Therefore, an effective and reliable mechanism is needed for ransomware detection. Deep learning (DL) and machine learning (ML) methods are very efficient and enhance model efficacy, offering burgeoning research paths, mainly in the ransomware detection realm, and presenting advantageous possibilities for new solutions. This study proposes a novel Multi-head Attention-Based Recurrent Neural Network with Enhanced Gorilla Troops Optimization for Cybersecurity Ransomware Detection (MHARNN-EGTOCRD) approach. The main goal of the MHARNN-EGTOCRD approach is to detect and classify ransomware attacks using advanced hybrid and optimization models in IoT environments. In the data normalization stage, the min-max normalization transforms input data into a suitable format. The dung beetle optimization (DBO) model is employed for the feature selection procedure to eliminate irrelevant, redundant, or noisy features. In addition, the proposed MHARNN-EGTOCRD model also implements a multi-head attention mechanism hybrid with a long short-term memory (MHA-LSTM) model for detecting ransomware. Finally, the hyperparameter selection of the MHA-LSTM model is performed by utilizing the EGTO model. The experimental analysis of the MHARNN-EGTOCRD technique is established on a ransomware detection dataset. The experimental validation of the MHARNN-EGTOCRD technique portrayed a superior accuracy value of 98.53% over existing models.