New Efficient and Secured Authentication Protocol for Remote Healthcare Systems in Cloud-IoT

Recently, Internet of Things and cloud computing are known to be emerged technologies in digital evolution. The first one is a large network used to interconnect embedded devices, while the second one refers to the possibility of offering infrastructure that can be used from anywhere and anytime. Due to their ability to provide remote services, IoT and cloud computing are actually integrated in various areas especially in the healthcare domain. However, the user private data such as health data must be secured by enhancing the authentication methods. Recently, Sharma and Kalra projected an authentication scheme for distant healthcare service-based cloud-IoT. Then, authors demonstrated that the proposed scheme is secure against various attacks. However, we prove in this paper that Sharma and Kalra’s protocol is prone to password guessing and smart card stolen attacks. Besides, we show that it has some security issues. For that reason, we propose an efficient and secured authentication scheme for remote healthcare systems in cloud-IoT. Then, we prove informally that our projected authentication scheme is secure against multiple attacks. Furthermore, the experimental tests done using Scyther tool show that our proposed scheme can withstand against known attacks as it ensures security requirements

SIP Authentication Protocols Based On Elliptic Curve Cryptography: Survey and comparison

Session Initiation Protocol (SIP) is the most popular signaling protocol using in order to establish, modify and terminate the session multimedia between different participants. It was selected by the Third Generation Project Partnership (3GPP) as a multimedia application protocol in 3G mobile networks. SIP is the protocol currently used for signaling ToIP calls.  The security of SIP is becoming more and more important. Authentication is the most important security service required by SIP. To ensure a secured communication, many SIP authentication protocols have been proposed. This work provides an overview of the proposed schemes based on elliptic curve cryptography. Those proposed schemes are analyzed in security consideration and the computational cost

A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things

Integrating the internet of things (IoT) in medical applications has significantly improved healthcare operations and patient treatment activities. Real-time patient monitoring and remote diagnostics allow the physician to serve more patients and save human lives using internet of medical things (IoMT) technology. However, IoMT devices are prone to cyber attacks, and security and privacy have been a concern. The IoMT devices operate on low computing and low memory, and implementing security technology on IoMT devices is not feasible. In this article, we propose particle swarm optimization deep neural network (PSO-DNN) for implementing an effective and accurate intrusion detection system in IoMT. Our approach outperforms the state of the art with an accuracy of 96% to detect network intrusions using the combined network traffic and patient’s sensing dataset. We also present an extensive analysis of using various Machine Learning(ML) and Deep Learning (DL) techniques for network intrusion detection in IoMT and confirm that DL models perform slightly better than ML models

A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things

Integrating the internet of things (IoT) in medical applications has significantly improved healthcare operations and patient treatment activities. Real-time patient monitoring and remote diagnostics allow the physician to serve more patients and save human lives using internet of medical things (IoMT) technology. However, IoMT devices are prone to cyber attacks, and security and privacy have been a concern. The IoMT devices operate on low computing and low memory, and implementing security technology on IoMT devices is not feasible. In this article, we propose particle swarm optimization deep neural network (PSO-DNN) for implementing an effective and accurate intrusion detection system in IoMT. Our approach outperforms the state of the art with an accuracy of 96% to detect network intrusions using the combined network traffic and patient’s sensing dataset. We also present an extensive analysis of using various Machine Learning(ML) and Deep Learning (DL) techniques for network intrusion detection in IoMT and confirm that DL models perform slightly better than ML models