Healthcare security in cloud-based wireless sensor networks: Botnet attack detection via autoencoder-aided goal-based artificial intelligent agent

This paper presents a new method in the field of healthcare security that specifically targets cloud-based wireless sensor networks (WSNs). The suggested method integrates a goal-based artificial intelligent agent (GAIA) with an autoencoder (AE) architecture, yielding an autoencoder-based agent (AE-A). The main goal of this integrated system is to improve the efficiency of identifying botnet assaults, with a specific emphasis on the evolving security threats related to cloud computing. Our concept is around creating a meticulously calibrated, goal-driven AI agent tailored explicitly for healthcare applications. The agent meticulously analyses network data and proficiently integrates autoencoder-enhanced anomaly detection techniques to uncover intricate patterns indicative of botnet activities. The adaptability of the goal-based AI agent is improved by ongoing real-time learning, guaranteeing that its responses are in line with the primary goal of neutralizing threats. The autoencoder serves a vital role in the system by functioning as a tool for extracting features. This approach enables the AI Agent to navigate complex information and derive significant insights efficiently. Cloud computing resources greatly enhance the functionalities of a system, enabling scalability, real-time analysis, and improved responsiveness. Utilizing goal-driven AI and autoencoder together proves to be a successful strategy in safeguarding healthcare-oriented WSNs against botnet attacks. This technique takes a proactive stance in ensuring the security of sensitive medical data. The suggested model is evaluated against various models, including the bidirectional long short-term memory (BLSTM) method, the hybrid BLSTM with recurrent neural network (BLSTM-RNN) algorithm, and the Random Forest algorithm. The models are evaluated using metrics such as Matthews correlation coefficient (MCC), prediction rate, accuracy, recall, precision, and F1 score analysis. The investigation demonstrates that the suggested model achieved the most significant values of 93% MCC, 94% prediction rate, 91% accuracy, 98% recall, 98% precision, and 98% F1 score, respectively when compared to the existing models.