The subject area of this project is IT security related to cars, specifically the security of devices connected through a cars OBD-II connector. The aim of the project is to see the security level of the AutoPi OBD-II unit and to analyse where potential vulnerabilities are likely to occur when in use. The device was investigated using threat modeling consisting of analysing the architecture, using the STRIDE model to see the potential attacks that could be implemented and risk assessments of the attacks using the DREAD model. After modelling the system, attempts of implementing attacks, with the basis in the threat modelling, were carried out. No major vulnerabilities were found in the AutoPi device but a MITM attack on the user was shown to be possible for an attacker to succeed with. Even though no major vulnerability was found IoT devices connected to cars might bring security concerns that needs to be looked into by companies and researchers. / Ämnesområdet för detta projekt är ITsäkerhet relaterad till bilar, mer specifikt säkerheten gällande enheter som kopplas in i en bils OBD-II-kontakt. Syftet med uppsatsen är att bedöma säkerhetsnivån på en OBD-II-enhet av modell AutoPi och att analysera var potentiella sårbarheter kan finnas i systemet. Enheten kommer att undersökas med hjälp av hotmodellering som består av att analysera arkitekturen, använda STRIDE-modellen för att upptäcka potentiella attackmetoder samt bedöma riskerna för attackerna med hjälp av DREAD-modellen. Efter det steget görs attackförsök utifrån resultaten från hotmodelleringen. Inga större sårbarheter hittades i AutoPi-enheten men en MITM-attack på användaren visades vara möjlig för en angripare att lyckas med. Ä ven fast inga större sårbarheter hittades kan IoT-enheter kopplade till bilar medföra säkerhetsbrister som företag och forskare måste se över.

Das Cloud Computing hat die Art und Weise unserer Kommunikation in den letzten Jahren rapide verändert. Es ermöglicht die Bereitstellung unterschiedlicher Dienste über das Internet. Inzwischen wurden sowohl für Unternehmen, als auch für den privaten Sektor verschiedene Anwendungen des Cloud Computing entwickelt. Dabei bringt jede Anwendung zahlreiche Vorteile mit sich, allerdings werden auch neue Herausforderungen an die IT-Sicherheit gestellt. In dieser Dissertation werden besonders wichtige Anwendungen des Cloud Computing auf die aktuellen Herausforderungen für die IT-Sicherheit untersucht. 1. Die Container Virtualisierung ermöglicht die Trennung der eigentlichen Anwendung von der IT-Infrastruktur. Dadurch kann ein vorkonfiguriertes Betriebssystem-Image zusammen mit einer Anwendung in einem Container kombiniert und in einer Testumgebung evaluiert werden. Dieses Prinzip hat vor allem die Software-Entwicklung in Unternehmen grundlegend verändert. Container können verwendet werden, um software in einer isolierten Umgebung zu testen, ohne den operativen Betrieb zu stören. Weiterhin ist es möglich, verschiedene Container-Instanzen über mehrere Hosts hinweg zu verwalten. In dem Fall spricht man von einer Orchestrierung. Da Container sensible unternehmensinterne Daten beinhalten, müssen Unternehmen ihr IT-Sicherheitskonzept für den Einsatz von Container Virtualisierungen überarbeiten. Dies stellt eine große Herausforderung dar, da es derzeit wenig Erfahrung mit der Absicherung von (orchestrierten) Container Virtualisierungen gibt. 2. Da Container Dienste über das Internet bereitstellen, sind Mitarbeiterinnen und Mitarbeiter, die diese Dienste für ihre Arbeit benötigen, an keinen festen Arbeitsplatz gebunden. Dadurch werden wiederum Konzepte wie das home o

The Internet of Things (IoT) is comprised of numerous devices that are connected through wired or wireless networks, including sensors and actuators. The number of IoT applications has recently increased dramatically, including Smart Homes, Internet of Vehicles (IoV), Internet of Medical Things (IoMT), Smart Cities, and Wearables. IoT Analytics has reported that the number of connected devices is expected to grow 18% to 14.4 billion in 2022 and will be 27 billion by 2025. Security is a critical issue in today's IoT, due to the nature of the architecture, the types of devices, the different methods of communication (mainly wireless), and the volume of data being transmitted over the network. Furthermore, security will become even more important as the number of devices connected to the IoT increases. However, devices can protect themselves and detect threats with the Intrusion Detection System (IDS). IDS typically use one of two approaches: anomaly-based or signature-based. In this thesis, we define the problems and the particular requirements of securing the IoT environments, and we have proposed a Deep Learning (DL) anomaly-based model with optimal features selection to detect the different potential attacks in IoT environments. We then compare the performance results with other works that have been used for similar tasks. We also employ the idea of reinforcement learning to combine the two different IDS approaches (i.e., anomaly-based and signature-based) to enable the model to detect known and unknown IoT attacks and classify the recognized attacked into five classes: Denial of Service (DDoS), Probe, User-to-Root (U2R), Remote-to-Local (R2L), and Normal traffic. We have also shown the effectiveness of two trending machine-learning techniques, Federated and Transfer learning (FL/TL), over using the traditional centralized Machine and Deep Learning (ML/DL) algorithms. Our proposed models improve the model's performance, increase the learning speed, reduce the amount of data that needs to be trained, and reserve user data privacy when compared with the traditional learning approaches. The proposed models are implemented using the three benchmark datasets generated by the Canadian Institute for Cybersecurity (CIC), NSL-KDD, CICIDS2017, and the CSE-CIC-IDS2018. The performance results were evaluated in different metrics, including Accuracy, Detection Rate (DR), False Alarm Rate (FAR), Sensitivity, Specificity, F-measure, and training and fine-tuning times.

The emerging Internet of Things (IoT) revolution has introduced many useful applications that are utilized in our daily lives. Users can program these devices in order to develop their own IoT applications; however, the platforms and languages that are used during development are abounding, complicated, and time-consuming. The software solution provided in this thesis, PROVIZ+, is a secure sensor application development software suite that helps users create sophisticated and secure IoT applications with little software and hardware experience. Moreover, a simple and efficient domain-specific programming language, namely Panther language, was designed for IoT application development to unify existing programming languages. In addition to these contributions, PROVIZ+ supports a novel secure over-the-air programming framework, namely SOTA, using Bluetooth and WiFi as well as serial programming. In this thesis, we explain the features of PROVIZ+’s components, how these tools can help develop IoT applications, and SOTA. We also present the performance evaluations of PROVIZ+ and SOTA.

This thesis is presented as an artifact of a project conducted at MalmöUniversity IoTaP LABS. The Internet of Things (IoT) is a growing field and its usehas been adopted in many aspects of our daily lives, which has led todigitalization and the creation of smart IoT ecosystems. However, with the rapidadoption of IoT, little or no focus has been put on the security implications,device proliferations and its advancements. This thesis takes a step forward toexplore the usefulness of implementing a security mechanism that canproactively be used to aid understanding attacker behaviour in an IoTenvironment. To achieve this, this thesis has outlined a number of objectivesthat ranges from how to create a deliberate vulnerability by using honeypots inorder to lure attacker’s in order to study their modus operandi. Furthermore,an Intrusion Attack Detection (Model) has been constructed that has aided withthis implementation. The IAD model, has been successfully implemented withthe help of interaction and dependence of key modules that have allowedhoneypots to be executed in a controlled IoT environment. Detailed descriptionsregarding the technologies that have been used in this thesis have also beenexplored to a greater extent. On the same note, the implemented system withthe help of an attack scenario allowed an attacker to access the system andcircumnavigate throughout the camouflaged network, thereafter, the attacker’sfootprints are mapped based on the mode of attack. Consequently, given thatthis implementation has been conducted in MAU environment, the results thathave been generated as a result of this implementations have been reportedcorrectly. Eventually, based on the results that have been generated by thesystem, it is worth to note that the research questions and the objective posedby the thesis have been met.

The IoT offers enormous potentials thanks to its Widespread adoption by many industries, individuals, and governments, leading explosive growth and remarkable breakthroughs that have made it a technology with seemingly boundless applications. However, the far-reaching IoT applications cum its characteristic heterogeneity and ubiquity come with a huge price for more security vulnerabilities, making the deployed IoT systems increasingly susceptible to, and prime targets of many different physical and cyber-attacks including insider attacks, thereby growing the overall security risks to the systems. This research, which focuses on addressing insider attacks on IoT, studies the likelihood of malicious insiders' activities compromising some of the security triad of Confidentiality, Integrity and Availability (CIA) of a supposedly secure IoT system with implemented security mechanisms. To further establish the vulnerability of the IoT systems to the insider attack being investigated in our research, we first produced a research output that emphasized the need for multi-layer security of the overall system and proposed the implementation of security mechanisms on components at all layers of the IoT system to safeguard the system and ensure its CIA. Those conventional measures however do not safeguard against insider attacks, as found by our experimental investigation of a working IoT system prototype. The outcome of the investigation therefore necessitates our proposed solution to the problem, which leverages the integration of distributed edge computing with decentralized Ethereum blockchain technology to provide countermeasures that preserve the Integrity of the IoT system data and improve effectiveness of the system. We employed the power of Ethereum smart contracts to perform integrity checks on the system data logically and take risk management decisions. We considered the industry use case of Downstream Petroleum sector for application of our solution. The solution was evaluated using datasets from different experimental settings and showed up to 86% accuracy rate. / Government of the Federal Republic of Nigeria through the Petroleum Technology Development Fund (PTDF) Overseas Scholarship Scheme (OSS)

Säkerhetskameror spelar en avgörande roll i att skydda våra smarta hem, men om de inte håller tillräckligt höga säkerhetsstandarder, kan de i stället minska bostadens säkerhet och riskera att exponera känslig privat information. Denna rapport ämnar att noggrant utvärdera säkerhetsaspekterna hos ett urval av säkerhetskameror för att bestämma deras förmåga att effektivt skydda användarnas privatliv och säkerhet. Analysen avslöjade att de granskade kamerorna inte uppvisade några allvarliga säkerhetsbrister. Det upptäcktes dock ett antal mindre förbättringsområden som, om åtgärdade, skulle kunna stärka deras skyddsförmåga ytterligare. / Security cameras play a crucial role in protecting our smart homes, but if they do not meet sufficiently high security standards, they can decrease the safety of the home and risk exposing sensitive private information. This report aims to thoroughly evaluate the security aspects of a selection of security cameras to determine their ability to effectively protect users' privacy and safety. The analysis revealed that the reviewed cameras did not exhibit any major security flaws. However, a few minor improvement areas were identified which, if addressed, could further enhance their protective capabilities.

The Internet of Things (IoT) is seen as one of the next Internet revolutions. In a near future the majority of all connected devices to the Internet will be IoT devices. These devices will connect previously offline constrained systems, thus it is essential to ensure end-to-end security for such devices. Object Security is a concept where the actual packet or sensitive parts of the packet are encrypted instead of the radio channel. A compromised node in the network will with this mechanism still have the data encrypted ensuring full end-to-end security. This paper proposes an architecture for using the object security format COSE in a typical constrained short-range radio based IoT platform. The IoT platform utilizes Bluetooth Low Energy and the Constrained Application Protocol for data transmission via a capillary gateway. A proof-of-concept implementation based on the architecture validates that the security solution is implementable. An overhead comparison between current channel security guidelines and the proposed object security solution results in a similar size for each data packet. The thesis concludes that object security should be seen as an alternative for ensuring end-to-end security for the Internet of Things.

abstract: The manufacturing process for electronic systems involves many players, from chip/board design and fabrication to firmware design and installation. In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk. Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities. It is becoming increasingly necessary to trust but verify the received devices both at production and in the field. Unauthorized hardware or firmware modifications, known as Trojans, can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices. Since Trojans may be triggered in the field at an unknown instance, it is essential to detect their presence at run-time. However, it isn't easy to run sophisticated detection algorithms on these devices due to limited computational power and energy, and in some cases, lack of accessibility. Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain. In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory. When the device enters the test mode, a predefined test application is run on the device repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes. The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

<div>Comprehensive networking security is a goal to achieve for enterprise networks. In forensics, the traffic analysis, causality dependence in intricate program network flows is needed in flow-based attribution techniques. The provenance, the connection between stealthy advanced persistent threats (APTs) and the execution of loadable modules is stripped because loading a module does not guarantee an execution. The reports of common vulnerabilities and exposures (CVE) demonstrate that lots of vulnerabilities have been introduced in protocol engineering process, especially for the emerging Internet-of-Things (IoT) applications. A code generation framework targeting secure protocol implementations can substantially enhance security.</div><div>A novel automaton-based technique, NetCrop, to infer fine-grained program behavior by analyzing network traffic is proposed in this thesis. Based on network flow causality, it constructs automata that describe both the network behavior and the end-host behavior of a whole program to attribute individual packets to their belonging programs and fingerprint the high-level program behavior. A novel provenance-oriented library tracing system, Lprov, which enforces library tracing on top of existing syscall logging based provenance tracking approaches is investigated. With the dynamic library call stack, the provenance of implicit library function execution is revealed and correlated to system events, facilitating the locating and defense of malicious libraries. The thesis presents ProFactory, in which a protocol is modeled, checked and securely generated, averting common vulnerabilities residing in protocol implementations.</div>