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.

Internet of Things (IoT) refers to a vast network that provides an interconnection between various objects and intelligent devices. The three important components of IoT are sensing, processing, and transmission of data. Nowadays, the new IoT technology is used in many different sectors, including the domestic, healthcare, telecommunications, environment, industry, construction, water management, and energy. IoT technology, involving the usage of embedded devices, differs from computers, laptops, and mobile devices. Due to exchanging personal data generated by sensors and the possibility of combining both real and virtual worlds, security is becoming crucial for IoT systems. Furthermore, IoT requires lightweight encryption techniques. Therefore, the goal of this paper is to identify the security challenges and key issues that are likely to arise in the IoT environment in order to guide authentication techniques to achieve a secure IoT service.

Due to the recent advancements in the Internet of things (IoT) and cloud computing technologies and growing number of devices connected to the Internet, the security and privacy issues are important to be resolved and protect the data and computer network. To provide security, a real-time monitoring of the network data and resources is needed. Intrusion detection systems have been used to monitor, detect, and alert an intrusion event in real time. Recently, the intrusion detection systems (IDS) incorporate several machine learning (ML) techniques. One of the techniques is decision tree, which can take reliable network measures and make good decisions by increasing the detection rate and accuracy. In this paper, we propose a reliable network intrusion detection approach using decision tree with enhanced data quality. Specifically, network data preprocessing and entropy decision feature selection is carried out for enhancing the data quality and relevant training; then, a decision tree classifier is built for reliable intrusion detection. Experimental study on two datasets shows that the proposed model can reach robust results. Actually, our model achieves 99.42% and 98.80% accuracy with NSL-KDD and CICIDS2017 datasets, respectively. The novel approach gives many advantages compared to the other models in term of accuracy (ACC), detection rate (DR), and false alarm rate (FAR).