HCAP: Hybridized Cyber Attack Prediction Model to handle the cyber-attacks in the Healthcare Applications

The rapid development and integration of interconnected healthcare devices, and communication networks, within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabilities, leading to an increased risk of cybersecurity attacks. These attacks threaten the confidentiality, integrity, and availability of sensitive healthcare data, raising concerns about the reliability of IoMT infrastructure. Addressing these challenges requires advanced cybersecurity measures capable of protecting the dynamic IoMT ecosystem from evolving threats. This research focuses on enhancing cyberattack prediction and prevention in IoMT environments through innovative Machine learning techniques improving the security and resilience of healthcare data. However, the existing model's efficiency depends on the diversity of data, which leads to computational complexity issues. In addition, the conventional model faces overfitting issues in training data, which causes prediction inaccuracies. Thus, the research introduces the hybridized cyber attack prediction model (HCAP) and analyzes various IoMT data source information to address the limitations of dataset availability issues. The gathered information is processed with the help of the Principal Component-Recursive Feature Elimination (PC-RFE), which eliminates the irrelevant features. The extracted features are fed into the lion-optimization technique to finetune the hyperparameters of the recurrent neural networks, enhancing the model's ability to efficiently predict cybersecurity threats with a maximum recognition rate in IoMT environments. The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python and different metrics such as false positive rate, false negative rates, improved accuracy, precision, recall and minimum computational efficiency. The results demonstrated that the proposed HCAP model achieved 98% accuracy in detecting cyberattacks and outperforms the existing models, reducing the false positive rate by 25% and the false negative rate by 20% and 30% improvement in computational efficiency enhances the reliability of IoMT threat detection in healthcare applications.