Exploring Vulnerabilities and Security Schemes of Service-Oriented Internet 0f Things (IoT) Protocols

Abstract

The Internet of Things (IoT) is spearheading a significant revolution in the realm of computing systems for the next generation. IoT has swiftly permeated various domains, including healthcare, manufacturing, military, and transportation, becoming an essential component of numerous smart devices and applications. However, as the number of IoT devices proliferates, security concerns have surged, resulting in severe attacks in recent years. Consequently, it is imperative to conduct a comprehensive investigation into IoT networks to identify and address vulnerabilities in order to preempt potential adversarial activities. The aim of this research is to examine different IoT-based systems and comprehend their security weaknesses. Additionally, the objective is to develop effective strategies to mitigate vulnerabilities and explore the security loopholes inherent in IoT-based systems, along with a plan to rectify them. IoT-based systems present unique challenges due to the expanding adoption of IoT technology across diverse applications, accompanied by a wide array of IoT devices. Each IoT network has its own limitations, further compounding the challenge. For instance, IoT devices used in sensor networks often face constraints in terms of resources, possessing limited power and computational capabilities. Moreover, integration of IoT with existing systems introduces security issues. A prime example of this integration is found in connected cars, where traditional in-vehicle networks, designed to connect internal car components, must be highly robust to meet stringent requirements. However, modern cars are now connected to a wide range of IoT nodes through various interfaces, thus creating new security challenges for professionals to address. This work offers a comprehensive investigation plan for different types of IoT-based systems with varying constraints to identify security vulnerabilities. We also propose security measures to mitigate the vulnerabilities identified in our investigation, thereby preventing adversarial activities. To facilitate the exploration and investigation of vulnerabilities, our work is divided into two parts: resource-constrained IoT-based systems (sensor networks, smart homes) and robustness-constrained IoT-based systems (connected cars). In our investigation of resource-constrained IoT networks, we focus on two widely used service-oriented IoT protocols, namely Universal Plug and Play (UPnP) and Message Queue Telemetry Transport (MQTT). Through a structured phase-by-phase analysis of these protocols, we establish a comprehensive threat model that explains the existing security gaps in communications. The threat models present security vulnerabilities of service-oriented resource-constrained IoT networks and the corresponding security attacks that exploit these vulnerabilities. We propose security solutions to mitigate the identified vulnerabilities and defend against potential security breaches. Our security analysis demonstrates that the proposed measures successfully thwart adversarial activities, and our experimental data supports the feasibility of the proposed models. For robustness-constrained IoT-based systems, we investigate the in-vehicle networks of modern cars, specifically focusing on the Controller Area Network (CAN) bus system, which is widely adopted for connecting Electronic Control Units (ECUs) in vehicles. To uncover vulnerabilities in these in-vehicle networks, we leverage fuzz testing, a method that involves testing with random data. Fuzz testing over the CAN bus is a well-established technique for detecting security vulnerabilities in in-vehicle networks. Furthermore, the automatic execution of test cases and assessment of robustness make CAN bus fuzzing a popular choice in the automotive testing community. However, a major drawback of fuzz testing is the generation of a large volume of execution reports, often containing false positives. Consequently, all execution reports must be manually reviewed, which is time-consuming and prone to human errors. To address this issue, we propose an automatic investigation mechanism to identify security vulnerabilities from fuzzing logs, considering the class, relative severity, and robustness of failures. Our proposed schema utilizes artificial intelligence (AI) to identify genuine security-critical vulnerabilities from fuzz testing execution logs. Additionally, we provide mechanisms to gauge the relative severity and robustness of a failure, thereby determining the criticality of a vulnerability. Moreover, we propose an AI-assisted vulnerability scoring system that indicates the criticality of a vulnerability, offering invaluable assistance in prioritizing the mitigation of critical issues in in-vehicle networks.