The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, houses, and cities, as well as electrical grids, gas plants, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that are pervasive, cost-effective, and can be accessed from anywhere and at any time. However, due to the exponential number of interconnected devices, cyber-security in the IoT is a major challenge. It heavily relies on the digital identity concept to build security mechanisms such as authentication and authorization. Current centralized identity management systems are built around third party identity providers, which raise privacy concerns and present a single point of failure. In addition, IoT unconventional characteristics such as scalability, heterogeneity and mobility require new identity management systems to operate in distributed and trustless environments, and uniquely identify a particular device based on its intrinsic digital properties and its relation to its human owner. In order to deal with these challenges, we present a Blockchain-based Identity Framework for IoT (BIFIT). We show how to apply our BIFIT to IoT smart homes to achieve identity self-management by end users. In the context of smart home, the framework autonomously extracts appliances signatures and creates blockchain-based identifies for their appliance owners. It also correlates appliances signatures (low level identities) and owners identifies in order to use them in authentication credentials and to make sure that any IoT entity is behaving normally.

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities. The IoT take advantage of the latest communication technologies in order to provide optimal and reliable services to Smart Cities (SC). The backbone of SC are the Critical Infrastructures such as the city's water system. In SC, the water system benefits from the development of automation and communication technologies to create smart environments which are more efficient in the use of the available resources; we call it Smart Water System (SWS). In this paper we present a multilayer architecture to integrate the SWS to the IoT, making it available from everywhere at any time. However, with the introduction of IoT we will be experiencing grand challenges to secure and protect its advanced information services due to the significant increase of the attack surface, complexity, heterogeneity, and number of interconnected resources. We also introduce an IoT Framework for SWSs to build trustworthy and secure applications and services. The framework enables developers to consider security issues at all IoT layers and integrate security algorithms with the functions and services offered in each layer instead of considering security in an ad-hoc and after thought manner. We show how this framework can be used to develop highly secure and trustworthy SWS services and how to apply our Anomaly Behavior Analysis methodology to secure and protect these services against any type of attacks.

Autonomous vehicles (AVs) are poised to revolutionize modern transportation, offering enhanced safety, efficiency, and convenience. However, AV architectures' increasing connectivity and complexity have introduced significant cybersecurity risks. This survey provides a comprehensive review of AV security challenges, focusing on widely adopted threat modeling frameworks such as STRIDE, DREAD, and MITRE ATT&CK. By examining common attack vectors and real-world case studies, including the Jeep Cherokee and Tesla Model S exploits, we highlight the urgent need for robust cybersecurity in in-vehicle systems and external interfaces. To complement existing modeling practices, we introduce Hybrid-SCDM, a novel framework that combines STRIDE-based threat classification with CVSS-derived DREAD scoring. This model transforms qualitative threat identification into quantitative risk prioritization by mapping CVSS metrics to DREAD dimensions through normalization. Applied to a generic multi-layered AV architecture, our findings show that intra-vehicle networks, especially CAN bus spoofing and fuzzing attacks, and suspension attacks, represent the most critical vulnerabilities due to their high exploitability and systemic impact. Beyond technical modeling, the survey explores emerging defense mechanisms such as blockchain-enabled Vehicle-to-Everything (V2X) communication, AI-driven anomaly detection, and secure Over-The-Air (OTA) updates. We also examine legal and ethical considerations surrounding data privacy, user safety, and regulatory compliance. By integrating analytical modeling with broad system insights, this work provides actionable recommendations for advancing the cybersecurity posture of autonomous vehicles.