Adversarial Attacks on Healthcare Deep Learning Model: Vulnerabilities and Defenses
The aim of this work is to introduce a novel technique for enhancing hospital network security using a Deep Autoencoder (DAE).
The digitization of healthcare institutions increases the risk of cybercrime due to network security vulnerabilities.
In this work, we model a novel technique called DeepHeal framework that uses a robust Deep Autoencoder (DAE) for improved cybersecurity and protection against intruders in healthcare networks. Using the deep learning techniques named DAE, DeepHeal identifies and stops cyber dangers, including various hostile attacks, illegal access, and data breaches. Healthcare networks are susceptible to various cyberattacks and intrusions. The DAE architecture enables training with high-level representations to detect anomalies and traffic patterns. By carefully analyzing datasets from real healthcare networks, we demonstrate that DeepHeal effectively identifies and counters various cyber threats.
The research using the proposed method offers a great potential in terms of accuracy, scalability, and real-time threat detection. Moreover, by highlighting the specific elements and network characteristics that are behind the anomalies it discovers, DeepHeal makes comprehension of them easier.
The proposed DAE model combined with RNN achieves a higher accuracy and precision level of 98%. The proposed DAE model outperformed existing models, which offers its ability to identify and resolve cybersecurity problems in hospital networks.
The security threats on patient data is considered sensitive and it offers improved healthcare networks security and this is found essential for the reliability and confidentiality of patient data in healthcare networks.
The proposed method’s research offers great potential in terms of accuracy, scalability, and real-time threat detection.
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