Established Internet of Things (IoT) platforms suffer from their inability to determine whether an IoT app is secure or not. A security analysis system (SAS) is a protective shield against any attack that breaks down data privacy and security. Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. Most of them build on utilizing static or dynamic analysis separately. However, the hybrid analysis is the best for obtaining accurate results. The SAS provides an effective outcome according to many criteria related to the analysis process, such as analysis type, characteristics, sensitivity, and analysis techniques. This paper proposes a new hybrid (static and dynamic) SAS based on the model-checking technique and deep learning, called an HSAS-MD analyzer, which focuses on the holistic analysis perspective of IoT apps. It aims to analyze the data of IoT apps by (1) converting the source code of the target applications to the format of a model checker that can deal with it; (2) detecting any abnormal behavior in the IoT application; (3) extracting the main static features from it to be tested and classified using a deep-learning CNN algorithm; (4) verifying app behavior by using the model-checking technique. HSAS-MD gives the best results in detecting malware from malicious smart Things applications compared to other SASs. The experimental results of HSAS-MD show that it provides 95%, 94%, 91%, and 93% for accuracy, precision, recall, and F-measure, respectively. It also gives the best results compared with other analyzers from various criteria.

The automotive industry currently is seeking to increase remote connectivity to a vehicle, which creates a high demand to implement a secure way of connecting vehicles, as well as verifying and storing their data in a trusted way. Furthermore, much information must be leaked in order to correctly diagnose the vehicle and determine when or how to remotely update it. In this context, we propose a Blockchain-based, fully automated remote vehicle diagnosis system. The proposed system provides a secure and trusted way of storing and verifying vehicle data and analyzing their performance in different environments. Furthermore, we discuss many aspects of the benefits to different parties, such as the vehicle’s owner and manufacturers. Furthermore, a performance evaluation via simulation was performed on the proposed system using MATLAB Simulink to simulate both the vehicles and Blockchain and give a prototype for the system’s structure. In addition, OMNET++ was used to measure the expected system’s storage and throughput given some fixed parameters, such as sending the periodicity and speed. The simulation results showed that the throughput, end-to-end delay, and power consumption increased as the number of vehicles increased. In general, Original Equipment Manufacturers (OEMs) can implement this system by taking into consideration either increasing the storage to add more vehicles or decreasing the sending frequency to allow more vehicles to join. By and large, the proposed system is fully dynamic, and its configuration can be adjusted to satisfy the OEM’s needs since there are no specific constraints while implementing it.