The Road towards Predictable Automotive High-Performance Platforms

Due to the trends of centralizing the E/E architecture and new computing-intensive applications, high-performance hardware platforms are currently finding their way into automotive systems. However, the SoCs currently available on the market have significant weaknesses when it comes to providing predictable performance for time-critical applications. The main reason for this is that these platforms are optimized for averagecase performance. This shortcoming represents one major risk in the development of current and future automotive systems. In this paper we describe how high-performance and predictability could (and should) be reconciled in future HW/SW platforms. We believe that this goal can only be reached in a close collaboration between system suppliers, IP providers, semiconductor companies, and OS/hypervisor vendors. Furthermore, academic input will be needed to solve remaining challenges and to further improve initial solutions

Computational Identification of Transcriptional Regulators in Human Endotoxemia

One of the great challenges in the post-genomic era is to decipher the underlying principles governing the dynamics of biological responses. As modulating gene expression levels is among the key regulatory responses of an organism to changes in its environment, identifying biologically relevant transcriptional regulators and their putative regulatory interactions with target genes is an essential step towards studying the complex dynamics of transcriptional regulation. We present an analysis that integrates various computational and biological aspects to explore the transcriptional regulation of systemic inflammatory responses through a human endotoxemia model. Given a high-dimensional transcriptional profiling dataset from human blood leukocytes, an elementary set of temporal dynamic responses which capture the essence of a pro-inflammatory phase, a counter-regulatory response and a dysregulation in leukocyte bioenergetics has been extracted. Upon identification of these expression patterns, fourteen inflammation-specific gene batteries that represent groups of hypothetically ‘coregulated’ genes are proposed. Subsequently, statistically significant cis-regulatory modules (CRMs) are identified and decomposed into a list of critical transcription factors (34) that are validated largely on primary literature. Finally, our analysis further allows for the construction of a dynamic representation of the temporal transcriptional regulatory program across the host, deciphering possible combinatorial interactions among factors under which they might be active. Although much remains to be explored, this study has computationally identified key transcription factors and proposed a putative time-dependent transcriptional regulatory program associated with critical transcriptional inflammatory responses. These results provide a solid foundation for future investigations to elucidate the underlying transcriptional regulatory mechanisms under the host inflammatory response. Also, the assumption that coexpressed genes that are functionally relevant are more likely to share some common transcriptional regulatory mechanism seems to be promising, making the proposed framework become essential in unravelling context-specific transcriptional regulatory interactions underlying diverse mammalian biological processes

A Situational Awareness Dashboard for a Security Operations Center

As a result of this dissertation, a solution was developed which would provide visibility into an institution’s security posture and its exposure to risk. Achieving this required the development of a Situational Awareness Dashboard in a cybersecurity context. This Dashboard provides a unified point of view where workers ranging from analysts to members of the executive board can consult and interact with a visual interface that aggregates a set of strategically picked metrics. These metrics provide insight regarding two main topics, the performance and risk of the organization’s Security Operations Center (SOC). The development of the dashboard was performed while working with the multinational enterprise entitled EY. During this time frame, two dashboards were developed one for each of two of EY’s clients inserted in the financial sector. Even though the first solution did not enter production, hence not leaving testing, the dashboard that was developed for the second client successfully was delivered fulfilling the set of objectives that were proposed initially. One of those objectives was enabling the solution to be as autonomous and selfsustained as possible, through its system architecture. Despite having different architectural components, both solutions were based on the same three-layered model. Whereas the first component runs all data ingestion, parsing and transformation operations, the second is in charge of the storage of said information into a database. Finally, the last component, possibly the most important one, is the visualization software tasked with displaying the previous information into actionable intelligence through the power of data visualization. All in all, the key points listed above converged into the development of a Situational Awareness Dashboard which ultimately allows organizations to have visibility into the SOC’s activities, as well as a perception of the performance and associated risks it faces.Como resultado desta dissertação, foi desenvolvida uma solução que proporcionaria visibilidade sobre a postura de segurança de uma instituição e sua exposição ao risco. Para tal foi necessário o desenvolvimento de um Situational Awareness Dashboard num contexto de cibersegurança. Este Dashboard pretende fornecer um ponto de vista unificado onde os trabalhadores, desde analistas a membros do conselho executivo, podem consultar e interagir com uma interface visual que agrega um conjunto de métricas escolhidas estrategicamente. Essas métricas fornecem informações sobre dois tópicos principais, o desempenho e o risco do Security Operations Center (SOC) da organização. O desenvolvimento do Dashboard foi realizado em parceria com a empresa multinacional EY. Nesse período, foram desenvolvidos dois dashboards, um para cada um dos dois clientes da EY inseridos no setor financeiro. Apesar de a primeira solução não ter entrado em produção, não saindo de teste, o painel que foi desenvolvido para o segundo cliente foi entregue com sucesso cumprindo o conjunto de objetivos inicialmente proposto. Umdesses objetivos era permitir que a solução fosse o mais autónoma e auto-sustentável possível, através da sua arquitetura de sistema. Apesar de terem diferentes componentes arquiteturais, ambas as soluções foram baseadas no mesmo modelo de três camadas. Enquanto a primeiro componente executa todas as operações de ingestão, análise e transformação de dados, a segundo é responsável pelo armazenamento dessas informações numa base de dados. Finalmente, o último componente, possivelmente o mais importante, é o software de visualização encarregue em exibir as informações anteriores em inteligência acionável através do poder da visualização de dados. Em suma, os pontos-chave listados acima convergiram no desenvolvimento de um Situational Awareness Dashboard que, em última análise, permite que as organizações tenham visibilidade das atividades do SOC, bem como uma percepção do desempenho e dos riscos que esta enfrenta

Self-Test Mechanisms for Automotive Multi-Processor System-on-Chips

L'abstract è presente nell'allegato / the abstract is in the attachmen

Securing Arm Platform: From Software-Based To Hardware-Based Approaches

With the rapid proliferation of the ARM architecture on smart mobile phones and Internet of Things (IoT) devices, the security of ARM platform becomes an emerging problem. In recent years, the number of malware identified on ARM platforms, especially on Android, shows explosive growth. Evasion techniques are also used in these malware to escape from being detected by existing analysis systems. In our research, we first present a software-based mechanism to increase the accuracy of existing static analysis tools by reassembleable bytecode extraction. Our solution collects bytecode and data at runtime, and then reassemble them offline to help static analysis tools to reveal the hidden behavior in an application. Further, we implement a hardware-based transparent malware analysis framework for general ARM platforms to defend against the traditional evasion techniques. Our framework leverages hardware debugging features and Trusted Execution Environment (TEE) to achieve transparent tracing and debugging with reasonable overhead. To learn the security of the involved hardware debugging features, we perform a comprehensive study on the ARM debugging features and summarize the security implications. Based on the implications, we design a novel attack scenario that achieves privilege escalation via misusing the debugging features in inter-processor debugging model. The attack has raised our concern on the security of TEEs and Cyber-physical System (CPS). For a better understanding of the security of TEEs, we investigate the security of various TEEs on different architectures and platforms, and state the security challenges. A study of the deploying the TEEs on edge platform is also presented. For the security of the CPS, we conduct an analysis on the real-world traffic signal infrastructure and summarize the security problems