# HCAP: Hybrid cyber attack prediction model for securing healthcare applications

## TL;DR

This paper introduces HCAP, a hybrid cyber attack prediction model that improves cybersecurity in healthcare by using machine learning techniques to detect threats more accurately and efficiently.

## Contribution

The novel HCAP model combines feature selection and optimization techniques to enhance cyberattack prediction in IoMT environments.

## Key findings

- HCAP achieved 98% accuracy in detecting cyberattacks in IoMT environments.
- The model reduced false positive and false negative rates by 25% and 20%, respectively.
- Computational efficiency improved by 30% compared to existing models.

## Abstract

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, increasing the 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 to protect the dynamic IoMT ecosystem from evolving threats. This research focuses on enhancing cyberattack prediction and prevention in IoMT environments through innovative Machine-learning techniques to improve healthcare data security and resilience. 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 Principal Component-Recursive Feature Elimination (PC-RFE), which eliminates the irrelevant features. The extracted features are fed into the lion-optimization technique to fine-tune 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 various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. The results demonstrated that the proposed HCAP model achieved 98% accuracy in detecting cyberattacks and outperformed existing models, reducing the false positive rate by 25%. The false negative rate by 20% and a 30% improvement in computational efficiency enhances the reliability of IoMT threat detection in healthcare applications.