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Future security perspectives for visible light communication (VLC) in remote patient monitoring systems (RPMS) present unique opportunities and challenges. As an emerging technology for data transmission, VLC offers advantages in physical security and interference resistance, but also requires robust solutions to protect sensitive medical information in a healthcare setting. Some of the key security perspectives for VLC in RPMS include:
Integrating deep learning techniques for intrusion and anomaly detection can also be applied to VLC. AI systems could monitor the behavior of VLC communications in real time and identify interception attempts or attacks such as denial of service (DoS) attacks. These systems would learn from normal data traffic patterns to quickly identify suspicious behavior and neutralize potential threats.
In hybrid systems combining RF and VLC, secure management of both types of channels becomes essential. VLC can be used for sensitive data transmission, while RF is used to provide network coverage in areas without direct lighting. It is therefore crucial to implement secure switching policies between these two types of communications, ensuring that sensitive data is always transmitted over the most secure channel. Encryption and key management protocols should also adapt to dynamic changes between RF and VLC channels. Deep learning algorithms can strengthen encryption mechanisms in hybrid RF/VLC networks by dynamically adjusting encryption protocols based on detected threats. In addition, deep learning can be used to monitor RF and VLC communications in real time and identify intrusions or anomalies. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can learn from normal data patterns to detect abnormal behavior, such as signal interception attempts or denial of service (DoS) attacks on either channel.
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IEEE/ICACT20250082 Slide.20
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