Internet of Things (IoT) has weaknesses due to the vulnerabilities in the wireless medium and massively interconnected nodes that form an extensive attack surface for adversaries. It is essential to ensure security including IoT networks and applications. The thesis focus on three streams in IoT scenario, including fake task attack detection in Mobile Crowdsensing (MCS), blockchain technique-integrated system security and privacy protection in MCS, and network intrusion detection in IoT. In this thesis, to begin, in order to detect fake tasks in MCS with promising performance, a detailed analysis is provided by modeling a deep belief network (DBN) when the available sensory data is scarce for analysis. With oversampling to cope with the class imbalance challenge, a Principal Component Analysis (PCA) module is implemented prior to the DBN and weights of various features of sensing tasks are analyzed under varying inputs. Additionally, an ensemble learning-based solution is proposed for MCS platforms to mitigate illegitimate tasks. Meanwhile, a k-means-based classification is integrated with the proposed ensemble method to extract region-specific features as input to the machine learning-based fake task detection. A novel approach that is based on horizontal Federated Learning (FL) is proposed to identify fake tasks that contain a number of independent detection devices and an aggregation entity. Moreover, the submitted tasks are collected and managed conventionally by a centralized MCS platform. A centralized MCS platform is not safe enough to protect and prevent tampering sensing tasks since it confronts the single point of failure which reduces the effectiveness and robustness of MCS system. In order to address the centralized issue and identify fake tasks, a blockchain-based decentralized MCS is designed. Integration of blockchain into MCS enables a decentralized framework. The distributed nature of a blockchain chain prevents sensing tasks from being tampered. The blockchain network uses a Practical Byzantine Fault Tolerance (PBFT) consensus that can tolerate 1/3 faulty nodes, making the implemented MCS system robust and sturdy. Lastly, Machine Learning (ML)-based frameworks are widely investigated to identity attacks in IoT networks, namely Network Intrusion Detection System (NIDS). ML models perform divergent detection performance in each class, so it is challenging to select one ML model applicable to all classes prediction. With this in mind, an innovative ensemble learning framework is proposed, two ensemble learning approaches, including All Predict Wisest Decides (APWD) and Predictor Of the Lowest Cost (POLC), are proposed based on the training of numerous ML models. According to the individual model outcomes, a wise model performing the best detection performance (e.g., F1 score) or contributing the lowest cost is determined. Moreover, an innovated ML-based framework is introduced, combining NIDS and host-based intrusion detection system (HIDS). The presented framework eliminates NIDS restrictions via observing the entire traffic information in host resources (e.g., logs, files, folders).

I extended and adapted the current technology acceptance models and privacy research to the peculiar context of the COVID-19 pandemic to ascertain the effective "power" of IT in fighting such a pandemic. The research models developed for the purpose of this study contain peculiar modifications to the technological-personal-environmental (TPE) framework and privacy calculus model because of the unique technologies implemented and the peculiar pandemic scenario. I developed three studies that investigate the interaction between social, legal, and technical factors that affect the adoption of IoT devices and blockchain systems implemented to fight the spread of COVID-19. Essay 1 systematically reviews existing literature on the analysis of the social, legal, and technical components in addressing phenomena related to IoT architecture and blockchain technology. The employment of a comparable coding method allows finding which of the above components is prominent in relation to the study of IoT and blockchain. Essay 2 develops a technological acceptance model by integrating the TPE framework with new constructs, i.e., regulatory environment, epidemic ecosystem, pre-epidemic ecosystem, perceived social usefulness, and technical characteristics. Essay 3 further explores the interplay between social, legal, and technical factors toward the adoption of smart contracts in the context of the COVID-19 pandemic. Essay 3 integrates the privacy calculus model by introducing new constructs, i.e., technical characteristics, regulatory environment, and perceived social benefits. For both Essays 2 and 3, research surveys were developed and distributed to undergraduate and graduate students in a major university located in the US. The research hypotheses were tested using partial least square modeling.

Internet of Things (IoT) has drawn much attention in recent years. With IoT, physical world entities get connected through internet. IoT is used currently in various applications, such as environmental monitoring, control systems, farming, home automation, security and surveillance systems etc. The aim of this research is to design a low-cost, energy-efficient, reliable and scalable embedded application for the smart greenhouse monitoring system. The IoT based system designed in this thesis uses various sensors to measure the air and soil quality parameters in the greenhouse, and monitor real-time data online using web-server and mobile phone based applications. A ZigBee based wireless sensor network is implemented to transport various sensory data to the gateway. Among other contributions, the designed system develops a new routing algorithm by introducing a confirmed delivery of packets and re-routing features. We also introduced an efficient cost metric for making routing decisions within WSN using hops count, and simple bi-directional link quality estimator using PRR and current battery voltage of neighbor nodes. We also verified the stability of the system by conducting various performance tests. The system is equipped with data analytic functions for the online examination of the data. The designed system adopts event-based triggering and data aggregation methods to reduce the number of transmissions, and develops a new algorithm for such purpose. The web-server and mobile applications have user interface to display the output of the data analytic services, warning, control operations and give access to query data of the user's interest.

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. / Computer and Information Science

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One of the fundamental challenges in Internet-of-Things systems is that network environment is always changing. Conventional networking approaches do not consider the dynamic evaluation of the networks or consider the network dynamic as a mirror thing, which may not be able to work or has a low efficiency in the Internet-of-Things systems. This dissertation is uniquely built by considering the dynamic network environment and even taking advantage of the network dynamic to improve the network performances, with a focus on the routing and offloading issues. The first part is related to the routing design in the opportunistic mobile networks. The opportunistic mobile network is expected to be an intrinsic part of the Internet of Things. Devices communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multi-hop manner. The main challenge in opportunistic mobile networks is due to intermittent connection and thus data is delivered through store-carry-forwarding paradigm. In this dissertation, We found an observation regarding the contact duration and proposed efficient data partitioning routing algorithms in the opportunistic mobile networks. The second part is related to the offloading issues in the Internet-of-things systems. With the surging demand on high-quality mobile services at any time, from anywhere, how to accommodate the explosive growth of traffics with/without existing network infrastructures is a fundamental issue. Specifically, We consider three different offloading problems, i.e., cellular data offloading, cloud task offloading, and mobile worker task offloading problems in vehicular networks, cloud, and crowdsourcing platforms. The common issue behind them is how to efficiently utilize the network resources in different scenarios by design efficient scheduling mechanisms. For the cellular data offloading, We explored the trade-off of cellular offloading in the vehicular network. For the cloud task offloading, We conducted the research to adjust the offloading strategies wisely so that the total offloading cost is minimized. For the worker task offloading in the smart cities, We optimized the cost-efficiency of the crowdsourcing platforms. / Computer and Information Science

The growing ubiquity of modern wireless and mobile electronic devices has brought our daily lives with more convenience and fun. Today's smartphones are equipped with a variety of sensors and wireless communication technologies, which can support not only the basic functions like phone call and web browsing, but also advanced functions like mobile pay, biometric security, fitness monitoring, etc. Internet-of-Things (IoT) is another category of popular wireless devices that are networked to collect and exchange data. For example, the smart appliances are increasingly deployed to serve in home and office environments, such as smart thermostat, smart bulb, and smart meter. Additionally, implantable medical devices (IMD) is the typical type of modern wireless devices that are implanted within human body for diagnostic, monitoring, and therapeutic purposes. However, these modern wireless and mobile devices are not well protected compared with traditional personal computers (PCs), due to the intrinsic limitations in computation power, battery capacity, etc. In this dissertation, we first present the security and privacy vulnerabilities we discovered. Then, we present our designs to address these issues and enhance the security of smartphones, IoT devices, and IMDs. For smartphone security, we investigate the mobile phishing attacks, mobile clickjacking attacks and mobile camera-based attacks. Phishing attacks aim to steal private information such as credentials. We propose a novel anti-phishing scheme MobiFish, which can detect both phishing webpages and phishing applications (apps). The key idea is to check the consistency between the claimed identity and the actual identity of a webpage/app. The claimed identity can be extracted from the screenshot of login user interface (UI) using the optical character recognition (OCR) technique, while the actual identity is indicated by the secondary-level domain name of the Uniform Resource Locator (URL) to which the credentials are submitted. Clickjacking attacks intend to hijack user inputs and re-route them to other UIs that are not supposed to receive them. To defend such attacks, a lightweight and independent detection service is integrated into the Android operating system. Our solution requires no user or app developer effort, and is compatible with existing commercial apps. Camera-based attacks on smartphone can secretly capture photos or videos without the phone user's knowledge. One advanced attack we discovered records the user's eye movements when entering passwords. We found that it is possible to recover simple passwords from the video containing user eye movements. Next, we propose an out-of-band two-factor authentication scheme for indoor IoT devices (e.g., smart appliances) based on the Blockchain infrastructure. Since smart home environment consists of multiple IoT devices that may share their sensed data to better serve the user, when one IoT device is being accessed, our design utilizes another device to conduct a secondary authentication over an out-of-band channel (light, acoustic, etc.), to detect if the access requestor is a malicious external device. Unlike smartphones and IoT devices, IMDs have the most limited computation and battery resources. We devise a novel smartphone-assisted access control scheme in which the patient's smartphone is used to delegate the heavy computations for authentication and authorization. The communications between the smartphone and the IMD programmer are conducted through an audio cable, which can resist the wireless eavesdropping and other active attacks. / Computer and Information Science

Yes / IET Editorial: In the future communications will be pervasive in nature, allowing users access at the “touch of button” to attain any service, at any time, on any device. The future device design process requires both a reconfigurable RF front end and back end with high tuning speed, energy efficiency, excellent linearity and intelligence to maximise the “greenness” of the network. But energy efficiency and excellent linearity are the main topics that are driving the designs of future transceivers, including their efforts to minimise network contributions to climate changes such as the effect of CO2 emissions: the minimisation of these is a requirement for information and communication technology (ICT) as much as for other technologies. Recently, information and communication technologies were shown to account for 3% of global power consumption and 2% of global CO2 emissions, and hence far from insignificant. The approach towards energy conservation and CO2 reduction in future communications will require a gret deal of effort which should be targeted both at the design of energy efficient, low-complexity physical, MAC and network layers, while maintaining the required Quality of Service (QoS). There is also a need, in infrastructures, networks and user terminals, to take a more holistic approach to improving or achieving green communications, from radio operation, through functionality, up to implementation. The increasing demand for data and voice services is not the only cause for concern since energy management and conservation are now at the forefront of the political agenda. The vision of Europe 2020 is to become a smart, sustainable and inclusive economy, and as part of these priorities the EU have set forth the 20:20:20 targets, whereby greenhouse gas emissions and energy consumption should be reduced by 20% while energy from renewables should be increased by 20%.

Yes / The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.

Yes