Personalized insulin dose manipulation attack and its detection using interval-based temporal patterns and machine learning algorithms

An optimal attention PLSTM‐based classification model to enhance the performance of IoMT attack detection in healthcare application

“…Manimurugan et al 19 used a Deep Belief Neural network (DBN) to detect attacks in the IoMT smart environment. Akhtar et al 16 suggested improved recurrent neural network (RNN) for monitoring the healthcare system based on a deep fusion strategy by modified vulture satiation‐based african vultures optimization algorithm (MVS‐AVOA), while Levy‐Loboda et al 17 proposed a machine learning‐based insulin dose modification and detection system. Imrana et al 18 proposed traditional machine learning techniques such as support vector machine (SVM), K‐nearest neighbor (KNN), random forest (RF), and naive bayes (NB), while Javaid et al 21 used APLSTM for continuous learning and anomaly detection in network systems.…”

“…[11][12][13][14][15] Physical layer security is obtained as a challenging strategy that helps to improve security in healthcare applications. [16][17][18] In the intrusion detection process, limited metaheuristic techniques are obtained to improve intrusion detection system (IDS) performance. Most studies in the existing literature have used IoT or wireless body area network (WBAN) datasets to develop security solutions for the IoT environment, but it varies the network congestion process frequently.…”

“…However, it cannot provide efficient security that provides a complex situation in the process of securing IoT devices 11–15 . Physical layer security is obtained as a challenging strategy that helps to improve security in healthcare applications 16–18 . In the intrusion detection process, limited metaheuristic techniques are obtained to improve intrusion detection system (IDS) performance.…”