105 research outputs found

    A Trust Management Framework for Vehicular Ad Hoc Networks

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    The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers

    Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid

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    The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models

    DEFINING A CYBER OPERATIONS PERFORMANCE FRAMEWORK VIA COMPUTATIONAL MODELING

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    Cyber operations are influenced by a wide range of environmental characteristics, strategic policies, organizational procedures, complex networks, and the individuals who attack and defend these cyber battlegrounds. While no two cyber operations are identical, leveraging the power of computational modeling will enable decision-makers to understand and evaluate the effect of these influences prior to their impact on mission success. Given the complexity of these influences, this research proposes an agent-based modeling framework that will result in an operational performance dashboard for user analysis. To account for cyber team behavioral characteristics, this research includes the development and validation of the Cyber Operations Self-Efficacy Scales (COSES). The underlying statistics, algorithms, research instruments, and equations to support the overall framework are provided. This research represents the most comprehensive cyber operations agent-based performance analysis tools published to date

    The universe without us: a history of the science and ethics of human extinction

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    This dissertation consists of two parts. Part I is an intellectual history of thinking about human extinction (mostly) within the Western tradition. When did our forebears first imagine humanity ceasing to exist? Have people always believed that human extinction is a real possibility, or were some convinced that this could never happen? How has our thinking about extinction evolved over time? Why do so many notable figures today believe that the probability of extinction this century is higher than ever before in our 300,000-year history on Earth? Exploring these questions takes readers from the ancient Greeks, Persians, and Egyptians, through the 18th-century Enlightenment, past scientific breakthroughs of the 19th century like thermodynamics and evolutionary theory, up to the Atomic Age, the rise of modern environmentalism in the 1970s, and contemporary fears about climate change, global pandemics, and artificial general intelligence (AGI). Part II is a history of Western thinking about the ethical and evaluative implications of human extinction. Would causing or allowing our extinction be morally right or wrong? Would our extinction be good or bad, better or worse compared to continuing to exist? For what reasons? Under which conditions? Do we have a moral obligation to create future people? Would past “progress” be rendered meaningless if humanity were to die out? Does the fact that we might be unique in the universe—the only “rational” and “moral” creatures—give us extra reason to ensure our survival? I place these questions under the umbrella of Existential Ethics, tracing the development of this field from the early 1700s through Mary Shelley’s 1826 novel The Last Man, the gloomy German pessimists of the latter 19th century, and post-World War II reflections on nuclear “omnicide,” up to current-day thinkers associated with “longtermism” and “antinatalism.” In the dissertation, I call the first history “History #1” and the second “History #2.” A main thesis of Part I is that Western thinking about human extinction can be segmented into five distinction periods, each of which corresponds to a unique “existential mood.” An existential mood arises from a particular set of answers to fundamental questions about the possibility, probability, etiology, and so on, of human extinction. I claim that the idea of human extinction first appeared among the ancient Greeks, but was eclipsed for roughly 1,500 years with the rise of Christianity. A central contention of Part II is that philosophers have thus far conflated six distinct types of “human extinction,” each of which has its own unique ethical and evaluative implications. I further contend that it is crucial to distinguish between the process or event of Going Extinct and the state or condition of Being Extinct, which one should see as orthogonal to the six types of extinction that I delineate. My aim with the second part of the book is to not only trace the history of Western thinking about the ethics of annihilation, but lay the theoretical groundwork for future research on the topic. I then outline my own views within “Existential Ethics,” which combine ideas and positions to yield a novel account of the conditions under which our extinction would be bad, and why there is a sense in which Being Extinct might be better than Being Extant, or continuing to exist

    DDoS Capability and Readiness - Evidence from Australian Organisations

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    A common perception of cyber defence is that it should protect systems and data from malicious attacks, ideally keeping attackers outside of secure perimeters and preventing entry. Much of the effort in traditional cyber security defence is focused on removing gaps in security design and preventing those with legitimate permissions from becoming a gateway or resource for those seeking illegitimate access. By contrast, Distributed Denial of Service (DDoS) attacks do not use application backdoors or software vulnerabilities to create their impact. They instead utilise legitimate entry points and knowledge of system processes for illegitimate purposes. DDoS seeks to overwhelm system and infrastructure resources so that legitimate requests are prevented from reaching their intended destination. For this thesis, a literature review was performed using sources from two perspectives. Reviews of both industry literature and academic literature were combined to build a balanced view of knowledge of this area. Industry and academic literature revealed that DDoS is outpacing internet growth, with vandalism, criminal and ideological motivations rising to prominence. From a defence perspective, the human factor remains a weak link in cyber security due to proneness for mistakes, oversights and the variance in approach and methods expressed by differing cultures. How cyber security is perceived, approached, and applied can have a critical effect on the overall outcome achieved, even when similar technologies are implemented. In addition, variance in the technical capabilities of those responsible for the implementation may create further gaps and vulnerabilities. While discussing technical challenges and theoretical concepts, existing literature failed to cover the experiences held by the victim organisations, or the thoughts and feelings of their personnel. This thesis addresses these identified gaps through exploratory research, which used a mix of descriptive and qualitative analysis to develop results and conclusions. The websites of 60 Australian organisations were analysed to uncover the level and quality of cyber security information they were willing to share and the methods and processes they used to engage with their audience. In addition, semi-structured interviews were conducted with 30 employees from around half of those websites analysed. These were analysed using NVivo12 qualitative analysis software. The difficulty experienced with attracting willing participants reflected the comfort that organisations showed with sharing cyber security information and experiences. However, themes found within the results show that, while DDoS is considered a valid threat, without encouragement to collaborate and standardise minimum security levels, firms may be missing out on valuable strategies to improve their cyber security postures. Further, this reluctance to share leads organisations to rely on their own internal skill and expertise, thus failing to realise the benefits of established frameworks and increased diversity in the workforce. Along with the size of the participant pool, other limitations included the diversity of participants and the impact of COVID-19 which may have influenced participants' thoughts and reflections. These limitations however, present opportunity for future studies using greater participant numbers or a narrower target focus. Either option would be beneficial to the recommendations of this study which were made from a practical, social, theoretical and policy perspective. On a practical and social level, organisational capabilities suffer due to the lack of information sharing and this extends to the community when similar restrictions prevent collaboration. Sharing of knowledge and experiences while protecting sensitive information is a worthy goal and this is something that can lead to improved defence. However, while improved understanding is one way to reduce the impact of cyber-attacks, the introduction of minimum cyber security standards for products, could reduce the ease at which devices can be used to facilitate attacks, but only if policy and effective governance ensures product compliance with legislation. One positive side to COVID-19's push to remote working, was an increase in digital literacy. As more roles were temporarily removed from their traditional physical workplace, many employees needed to rapidly accelerate their digital competency to continue their employment. To assist this transition, organisations acted to implement technology solutions that eased the ability for these roles to be undertaken remotely and as a consequence, they opened up these roles to a greater pool of available candidates. Many of these roles are no longer limited to the geographical location of potential employees or traditional hours of availability. Many of these roles could be accessed from almost anywhere, at any time, which had a positive effect on organisational capability and digital sustainability

    Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks

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    The Internet of Things (IoT) is a complex and diverse network consisting of resource-constrained sensors/devices/things that are vulnerable to various security threats, particularly Distributed Denial of Services (DDoS) attacks. Recently, the integration of Software Defined Networking (SDN) with IoT has emerged as a promising approach for improving security and access control mechanisms. However, DDoS attacks continue to pose a significant threat to IoT networks, as they can be executed through botnet or zombie attacks. Machine learning-based security frameworks offer a viable solution to scrutinize the behavior of IoT devices and compile a profile that enables the decision-making process to maintain the integrity of the IoT environment. In this paper, we present a machine learning-based approach to detect DDoS attacks in an SDN-WISE IoT controller. We have integrated a machine learning-based detection module into the controller and set up a testbed environment to simulate DDoS attack traffic generation. The traffic is captured by a logging mechanism added to the SDN-WISE controller, which writes network logs into a log file that is pre-processed and converted into a dataset. The machine learning DDoS detection module, integrated into the SDN-WISE controller, uses Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) algorithms to classify SDN-IoT network packets. We evaluate the performance of the proposed framework using different traffic simulation scenarios and compare the results generated by the machine learning DDoS detection module. The proposed framework achieved an accuracy rate of 97.4%, 96.1%, and 98.1% for NB, SVM, and DT, respectively. The attack detection module takes up to 30% usage of memory and CPU, and it saves about 70% memory while keeping the CPU free up to 70% to process the SD-IoT network traffic with an average throughput of 48 packets per second, achieving an accuracy of 97.2%. Our experimental results demonstrate the superiority of the proposed framework in detecting DDoS attacks in an SDN-WISE IoT environment. The proposed approach can be used to enhance the security of IoT networks and mitigate the risk of DDoS attacks

    IoT Applications Computing

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    The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

    Characterizing the IoT ecosystem at scale

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    Internet of Things (IoT) devices are extremely popular with home, business, and industrial users. To provide their services, they typically rely on a backend server in- frastructure on the Internet, which collectively form the IoT Ecosystem. This ecosys- tem is rapidly growing and offers users an increasing number of services. It also has been a source and target of significant security and privacy risks. One notable exam- ple is the recent large-scale coordinated global attacks, like Mirai, which disrupted large service providers. Thus, characterizing this ecosystem yields insights that help end-users, network operators, policymakers, and researchers better understand it, obtain a detailed view, and keep track of its evolution. In addition, they can use these insights to inform their decision-making process for mitigating this ecosystem’s security and privacy risks. In this dissertation, we characterize the IoT ecosystem at scale by (i) detecting the IoT devices in the wild, (ii) conducting a case study to measure how deployed IoT devices can affect users’ privacy, and (iii) detecting and measuring the IoT backend infrastructure. To conduct our studies, we collaborated with a large European Internet Service Provider (ISP) and a major European Internet eXchange Point (IXP). They rou- tinely collect large volumes of passive, sampled data, e.g., NetFlow and IPFIX, for their operational purposes. These data sources help providers obtain insights about their networks, and we used them to characterize the IoT ecosystem at scale. We start with IoT devices and study how to track and trace their activity in the wild. We developed and evaluated a scalable methodology to accurately detect and monitor IoT devices with limited, sparsely sampled data in the ISP and IXP. Next, we conduct a case study to measure how a myriad of deployed devices can affect the privacy of ISP subscribers. Unfortunately, we found that the privacy of a substantial fraction of IPv6 end-users is at risk. We noticed that a single device at home that encodes its MAC address into the IPv6 address could be utilized as a tracking identifier for the entire end-user prefix—even if other devices use IPv6 privacy extensions. Our results showed that IoT devices contribute the most to this privacy leakage. Finally, we focus on the backend server infrastructure and propose a methodology to identify and locate IoT backend servers operated by cloud services and IoT vendors. We analyzed their IoT traffic patterns as observed in the ISP. Our analysis sheds light on their diverse operational and deployment strategies. The need for issuing a priori unknown network-wide queries against large volumes of network flow capture data, which we used in our studies, motivated us to develop Flowyager. It is a system built on top of existing traffic capture utilities, and it relies on flow summarization techniques to reduce (i) the storage and transfer cost of flow captures and (ii) query response time. We deployed a prototype of Flowyager at both the IXP and ISP.Internet-of-Things-Geräte (IoT) sind aus vielen Haushalten, Büroräumen und In- dustrieanlagen nicht mehr wegzudenken. Um ihre Dienste zu erbringen, nutzen IoT- Geräte typischerweise auf eine Backend-Server-Infrastruktur im Internet, welche als Gesamtheit das IoT-Ökosystem bildet. Dieses Ökosystem wächst rapide an und bie- tet den Nutzern immer mehr Dienste an. Das IoT-Ökosystem ist jedoch sowohl eine Quelle als auch ein Ziel von signifikanten Risiken für die Sicherheit und Privatsphäre. Ein bemerkenswertes Beispiel sind die jüngsten groß angelegten, koordinierten globa- len Angriffe wie Mirai, durch die große Diensteanbieter gestört haben. Deshalb ist es wichtig, dieses Ökosystem zu charakterisieren, eine ganzheitliche Sicht zu bekommen und die Entwicklung zu verfolgen, damit Forscher, Entscheidungsträger, Endnutzer und Netzwerkbetreibern Einblicke und ein besseres Verständnis erlangen. Außerdem können alle Teilnehmer des Ökosystems diese Erkenntnisse nutzen, um ihre Entschei- dungsprozesse zur Verhinderung von Sicherheits- und Privatsphärerisiken zu verbes- sern. In dieser Dissertation charakterisieren wir die Gesamtheit des IoT-Ökosystems indem wir (i) IoT-Geräte im Internet detektieren, (ii) eine Fallstudie zum Einfluss von benutzten IoT-Geräten auf die Privatsphäre von Nutzern durchführen und (iii) die IoT-Backend-Infrastruktur aufdecken und vermessen. Um unsere Studien durchzuführen, arbeiten wir mit einem großen europäischen Internet- Service-Provider (ISP) und einem großen europäischen Internet-Exchange-Point (IXP) zusammen. Diese sammeln routinemäßig für operative Zwecke große Mengen an pas- siven gesampelten Daten (z.B. als NetFlow oder IPFIX). Diese Datenquellen helfen Netzwerkbetreibern Einblicke in ihre Netzwerke zu erlangen und wir verwendeten sie, um das IoT-Ökosystem ganzheitlich zu charakterisieren. Wir beginnen unsere Analysen mit IoT-Geräten und untersuchen, wie diese im Inter- net aufgespürt und verfolgt werden können. Dazu entwickelten und evaluierten wir eine skalierbare Methodik, um IoT-Geräte mit Hilfe von eingeschränkten gesampelten Daten des ISPs und IXPs präzise erkennen und beobachten können. Als Nächstes führen wir eine Fallstudie durch, in der wir messen, wie eine Unzahl von eingesetzten Geräten die Privatsphäre von ISP-Nutzern beeinflussen kann. Lei- der fanden wir heraus, dass die Privatsphäre eines substantiellen Teils von IPv6- Endnutzern bedroht ist. Wir entdeckten, dass bereits ein einzelnes Gerät im Haus, welches seine MAC-Adresse in die IPv6-Adresse kodiert, als Tracking-Identifikator für das gesamte Endnutzer-Präfix missbraucht werden kann — auch wenn andere Geräte IPv6-Privacy-Extensions verwenden. Unsere Ergebnisse zeigten, dass IoT-Geräte den Großteil dieses Privatsphäre-Verlusts verursachen. Abschließend fokussieren wir uns auf die Backend-Server-Infrastruktur und wir schla- gen eine Methodik zur Identifizierung und Lokalisierung von IoT-Backend-Servern vor, welche von Cloud-Diensten und IoT-Herstellern betrieben wird. Wir analysier- ten Muster im IoT-Verkehr, der vom ISP beobachtet wird. Unsere Analyse gibt Auf- schluss über die unterschiedlichen Strategien, wie IoT-Backend-Server betrieben und eingesetzt werden. Die Notwendigkeit a-priori unbekannte netzwerkweite Anfragen an große Mengen von Netzwerk-Flow-Daten zu stellen, welche wir in in unseren Studien verwenden, moti- vierte uns zur Entwicklung von Flowyager. Dies ist ein auf bestehenden Netzwerkverkehrs- Tools aufbauendes System und es stützt sich auf die Zusammenfassung von Verkehrs- flüssen, um (i) die Kosten für Archivierung und Transfer von Flow-Daten und (ii) die Antwortzeit von Anfragen zu reduzieren. Wir setzten einen Prototypen von Flowyager sowohl im IXP als auch im ISP ein

    Cybersecurity of Digital Service Chains

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    This open access book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios. We expect the book to be interesting for the broad group of researchers, engineers, and professionals daily experiencing the inadequacy of outdated cybersecurity models for modern computing environments and cyber-physical systems
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