61 research outputs found

    X-Attack 2.0: The Risk of Power Wasters and Satisfiability Don’t-Care Hardware Trojans to Shared Cloud FPGAs

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    Cloud computing environments increasingly provision field-programmable gate arrays (FPGAs) for their programmability and hardware-level parallelism. While FPGAs are typically used by one tenant at a time, multitenant schemes supporting spatial sharing of cloud FPGA resources have been proposed in the literature. However, the spatial multitenancy of FPGAs opens up new attack surfaces. Investigating potential security threats to multitenant FPGAs is thus essential for better understanding and eventually mitigating the security risks. This work makes a notable step forward by systematically analyzing the combined threat of FPGA power wasters and satisfiability don’t-care hardware Trojans in shared cloud FPGAs. We demonstrate a successful remote undervolting attack that activates a hardware Trojan concealed within a victim FPGA design and exploits the payload. The attack is carried out entirely remotely, assuming two spatially colocated FPGA users isolated from one another. The victim user’s circuit is infected with a Trojan, triggered by a pair of don’t-care signals that never reach the combined trigger condition during regular operation. The adversary, targeting the exploitation of the Trojan, deploys power waster circuits to lower the supply voltage of the FPGA. The assumption is that, under the effect of the lowered voltage, don’t-care signals may reach the particular state that triggers the Trojan. We name this exploit X-Attack and demonstrate its feasibility on an embedded FPGA and real-world cloud FPGA instances. Additionally, we study the effects of various attack tuning parameters on the exploit’s success. Finally, we discuss potential countermeasures against this security threat and present a lightweight self-calibrating countermeasure. To the best of our knowledge, this is the first work on undervolting-based fault-injection attacks in multitenant FPGAs to demonstrate the attack on commercially available cloud FPGA instances

    SECURITY CHALLENGES IN CLOUD COMPUTING

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    Models, methods, and tools for developing MMOG backends on commodity clouds

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    Online multiplayer games have grown to unprecedented scales, attracting millions of players worldwide. The revenue from this industry has already eclipsed well-established entertainment industries like music and films and is expected to continue its rapid growth in the future. Massively Multiplayer Online Games (MMOGs) have also been extensively used in research studies and education, further motivating the need to improve their development process. The development of resource-intensive, distributed, real-time applications like MMOG backends involves a variety of challenges. Past research has primarily focused on the development and deployment of MMOG backends on dedicated infrastructures such as on-premise data centers and private clouds, which provide more flexibility but are expensive and hard to set up and maintain. A limited set of works has also focused on utilizing the Infrastructure-as-a-Service (IaaS) layer of public clouds to deploy MMOG backends. These clouds can offer various advantages like a lower barrier to entry, a larger set of resources, etc. but lack resource elasticity, standardization, and focus on development effort, from which MMOG backends can greatly benefit. Meanwhile, other research has also focused on solving various problems related to consistency, performance, and scalability. Despite major advancements in these areas, there is no standardized development methodology to facilitate these features and assimilate the development of MMOG backends on commodity clouds. This thesis is motivated by the results of a systematic mapping study that identifies a gap in research, evident from the fact that only a handful of studies have explored the possibility of utilizing serverless environments within commodity clouds to host these types of backends. These studies are mostly vision papers and do not provide any novel contributions in terms of methods of development or detailed analyses of how such systems could be developed. Using the knowledge gathered from this mapping study, several hypotheses are proposed and a set of technical challenges is identified, guiding the development of a new methodology. The peculiarities of MMOG backends have so far constrained their development and deployment on commodity clouds despite rapid advancements in technology. To explore whether such environments are viable options, a feasibility study is conducted with a minimalistic MMOG prototype to evaluate a limited set of public clouds in terms of hosting MMOG backends. Foli lowing encouraging results from this study, this thesis first motivates toward and then presents a set of models, methods, and tools with which scalable MMOG backends can be developed for and deployed on commodity clouds. These are encapsulated into a software development framework called Athlos which allows software engineers to leverage the proposed development methodology to rapidly create MMOG backend prototypes that utilize the resources of these clouds to attain scalable states and runtimes. The proposed approach is based on a dynamic model which aims to abstract the data requirements and relationships of many types of MMOGs. Based on this model, several methods are outlined that aim to solve various problems and challenges related to the development of MMOG backends, mainly in terms of performance and scalability. Using a modular software architecture, and standardization in common development areas, the proposed framework aims to improve and expedite the development process leading to higher-quality MMOG backends and a lower time to market. The models and methods proposed in this approach can be utilized through various tools during the development lifecycle. The proposed development framework is evaluated qualitatively and quantitatively. The thesis presents three case study MMOG backend prototypes that validate the suitability of the proposed approach. These case studies also provide a proof of concept and are subsequently used to further evaluate the framework. The propositions in this thesis are assessed with respect to the performance, scalability, development effort, and code maintainability of MMOG backends developed using the Athlos framework, using a variety of methods such as small and large-scale simulations and more targeted experimental setups. The results of these experiments uncover useful information about the behavior of MMOG backends. In addition, they provide evidence that MMOG backends developed using the proposed methodology and hosted on serverless environments can: (a) support a very high number of simultaneous players under a given latency threshold, (b) elastically scale both in terms of processing power and memory capacity and (c) significantly reduce the amount of development effort. The results also show that this methodology can accelerate the development of high-performance, distributed, real-time applications like MMOG backends, while also exposing the limitations of Athlos in terms of code maintainability. Finally, the thesis provides a reflection on the research objectives, considerations on the hypotheses and technical challenges, and outlines plans for future work in this domain

    Cloud adoption and cyber security in public organizations: an empirical investigation on Norwegian municipalities

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    The public sector in Norway, particularly municipalities, is currently transforming through the adoption of cloud solutions. This multiple case study investigates cloud adoption and is security challenges that come along with it. The objective is to identify the security challenges that cloud solutions present and techniques or strategies that can be used to mitigate these security challenges. The Systematic Literature Review (SLR) provided valuable insights into the prevalent challenges and associated mitigation techniques in cloud adoption. The thesis also uses a qualitative approach using Semi-Structured Interviews (SSI) to gather insight into informants’ experiences regarding cloud adoption and its security challenges. The study’s empirical data is based on interviews with six different Norwegian municipalities, providing a unique and broad perspective. The analysis of the empirical findings, combined with the literature, reveals several security challenges and mitigation techniques in adopting cloud solutions. The security challenges encompass organizational, environmental, legal, and technical aspects of cloud adoption in the municipality. Based on the findings, it is recommended that Norwegian municipalities act on these issues to ensure a more secure transition to cloud solutions

    ‘Responsibility to detect?’: autonomous threat detection and its implications for due diligence in cyberspace

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    Private and public organizations have long relied on intrusion detection systems to alert them of malicious activity in their digital networks. These systems were designed to detect threat signatures in static networks or infer anomalous activity based on their security ‘logs’. They are, however, of limited use to detect threats across heterogeneous, modern-day networks, where computing resources are distributed across cloud or routing services. Recent advancements in machine learning (ML) have led to the development of autonomous threat detection (ATD) applications that monitor, evaluate, and respond to malicious activity with minimal human intervention. The use of ‘intelligent’ and programmable algorithms for ATD will reduce incident response times and enhance the capacity of states to detect threats originating from any layer of their territorial information and communications technologies (ICT) infrastructure. This paper argues that ATD technologies will influence the evolution of a due diligence rule for cyberspace by raising the standard of care owed by states to prevent their networks from being used for malicious, transboundary ICT activities. This paper comprises five sections. Section 1 introduces the paper and its central argument. Section 2 outlines broad trends and operational factors pushing public and private entities towards the adoption of ATD. Section 3 offers an overview of a typical ATD application. Section 4 analyses the impact of ATD on the due diligence obligations of states. Section 5 presents the paper’s conclusions.FGGA – Publicaties zonder aanstelling Universiteit LeidenCybersecurity en cybergovernanc

    Internet of Things and the Law: Legal Strategies for Consumer-Centric Smart Technologies

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    Internet of Things and the Law: Legal Strategies for Consumer-Centric Smart Technologies is the most comprehensive and up-to-date analysis of the legal issues in the Internet of Things (IoT). For decades, the decreasing importance of tangible wealth and power – and the increasing significance of their disembodied counterparts – has been the subject of much legal research. For some time now, legal scholars have grappled with how laws drafted for tangible property and predigital ‘offline’ technologies can cope with dematerialisation, digitalisation, and the internet. As dematerialisation continues, this book aims to illuminate the opposite movement: rematerialisation, namely, the return of data, knowledge, and power within a physical ‘smart’ world. This development frames the book’s central question: can the law steer rematerialisation in a human-centric and socially just direction? To answer it, the book focuses on the IoT, the sociotechnological phenomenon that is primarily responsible for this shift. After a thorough analysis of how existing laws can be interpreted to empower IoT end users, Noto La Diega leaves us with the fundamental question of what happens when the law fails us and concludes with a call for collective resistance against ‘smart’ capitalism

    Online learning on the programmable dataplane

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    This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms

    Risk Assessment Method of Cloud Environment

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    Cloud technology usage in nowadays companies constantly grows every year. Moreover, the COVID-19 situation caused even a higher acceleration of cloud adoption. A higher portion of deployed cloud services, however, means also a higher number of exploitable attack vectors. For that reason, risk assessment of the cloud environment plays a significant role for the companies. The target of this paper is to present a risk assessment method specialized in the cloud environment that supports companies with the identification and assessments of the cloud risks. The method itself is based on ISO/IEC 27005 standard and addresses a list of predefined cloud risks. Besides, the paper also presents the risk score calculation definition. The risk assessment method is then applied to an accounting company in a form of a case study. As a result, 24 risks are identified and assessed within the case study where each risk included also exemplary countermeasures. Further, this paper includes a description of the selected cloud risks
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