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LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Towards trustworthy computing on untrustworthy hardware
Historically, hardware was thought to be inherently secure and trusted due to its
obscurity and the isolated nature of its design and manufacturing. In the last two
decades, however, hardware trust and security have emerged as pressing issues.
Modern day hardware is surrounded by threats manifested mainly in undesired
modifications by untrusted parties in its supply chain, unauthorized and pirated
selling, injected faults, and system and microarchitectural level attacks. These threats,
if realized, are expected to push hardware to abnormal and unexpected behaviour
causing real-life damage and significantly undermining our trust in the electronic and
computing systems we use in our daily lives and in safety critical applications. A
large number of detective and preventive countermeasures have been proposed in
literature. It is a fact, however, that our knowledge of potential consequences to
real-life threats to hardware trust is lacking given the limited number of real-life
reports and the plethora of ways in which hardware trust could be undermined. With
this in mind, run-time monitoring of hardware combined with active mitigation of
attacks, referred to as trustworthy computing on untrustworthy hardware, is proposed
as the last line of defence. This last line of defence allows us to face the issue of live
hardware mistrust rather than turning a blind eye to it or being helpless once it occurs.
This thesis proposes three different frameworks towards trustworthy computing
on untrustworthy hardware. The presented frameworks are adaptable to different
applications, independent of the design of the monitored elements, based on
autonomous security elements, and are computationally lightweight. The first
framework is concerned with explicit violations and breaches of trust at run-time,
with an untrustworthy on-chip communication interconnect presented as a potential
offender. The framework is based on the guiding principles of component guarding,
data tagging, and event verification. The second framework targets hardware elements
with inherently variable and unpredictable operational latency and proposes a
machine-learning based characterization of these latencies to infer undesired latency
extensions or denial of service attacks. The framework is implemented on a DDR3
DRAM after showing its vulnerability to obscured latency extension attacks. The
third framework studies the possibility of the deployment of untrustworthy hardware
elements in the analog front end, and the consequent integrity issues that might arise
at the analog-digital boundary of system on chips. The framework uses machine
learning methods and the unique temporal and arithmetic features of signals at this
boundary to monitor their integrity and assess their trust level
Mismatch responses: Probing probabilistic inference in the brain
Sensory signals are governed by statistical regularities and carry valuable information about the unfolding of environmental events. The brain is thought to capitalize on the probabilistic nature of sequential inputs to infer on the underlying (hidden) dynamics driving sensory stimulation. Mis-match responses (MMRs) such as the mismatch negativity (MMN) and the P3 constitute prominent neuronal signatures which are increasingly interpreted as reflecting a mismatch between the current sensory input and the brain’s generative model of incoming stimuli. As such, MMRs might be viewed as signatures of probabilistic inference in the brain and their response dynamics can provide insights into the underlying computational principles. However, given the dominance of the auditory modality in MMR research, the specifics of brain responses to probabilistic sequences across sensory modalities and especially in the somatosensory domain are not well characterized.
The work presented here investigates MMRs across the auditory, visual and somatosensory modality by means of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We designed probabilistic stimulus sequences to elicit and characterize MMRs and employed computational modeling of response dynamics to inspect different aspects of the brain’s generative model of the sensory environment. In the first study, we used a volatile roving stimulus paradigm to elicit somatosensory MMRs and performed single-trial modeling of EEG signals in sensor and source space. Model comparison suggested that responses reflect Bayesian inference based on the estimation of transition probability and limited information integration of the recent past in order to adapt to a changing environment. The results indicated that somatosensory MMRs reflect an initial mismatch between sensory input and model beliefs represented by confidence-corrected surprise (CS) followed by model adjustment dynamics represented by Bayesian surprise (BS). For the second and third study we designed a tri-modal roving stimulus paradigm to delineate modality specific and modality general features of mismatch processing. Computational modeling of EEG signals in study 2 suggested that single-trial dynamics reflect Bayesian inference based on estimation of uni-modal transition probabilities as well as cross-modal conditional dependencies. While early mismatch processing around the MMN tended to reflect CS, later MMRs around the P3 rather reflect BS, in correspondence to the somatosensory study. Finally, the fMRI results of study 3 showed that MMRs are generated by an interaction of modality specific regions in higher order sensory cortices and a modality general fronto-parietal network. Inferior parietal regions in particular were sensitive to expectation violations with respect to the cross-modal contingencies in the stimulus sequences. Overall, our results indicate that MMRs across the senses reflect processes of probabilistic inference in a complex and inherently multi-modal environment.Sensorische Signale sind durch statistische Regularitäten bestimmt und beinhalten wertvolle Informationen über die Entwicklung von Umweltereignissen. Es wird angenommen, dass das Gehirn die Wahrscheinlichkeitseigenschaften sequenzieller Reize nutzt um auf die zugrundeliegenden (verborgenen) Dynamiken zu schließen, welche sensorische Stimulation verursachen. Diskrepanz-Reaktionen ("Mismatch responses"; MMRs) wie die "mismatch negativity" (MMN) und die P3 sind bekannte neuronale Signaturen die vermehrt als Signale einer Diskrepanz zwischen der momentanen sensorischen Einspeisung und dem generativen Modell, welches das Gehirn von den eingehenden Reizen erstellt angesehen werden. Als solche können MMRs als Signaturen von wahrscheinlichkeitsbasierter Inferenz im Gehirn betrachtet werden und ihre Reaktionsdynamiken können Einblicke in die zugrundeliegenden komputationalen Prinzipien geben. Angesichts der Dominanz der auditorischen Modalität in der MMR-Forschung, sind allerdings die spezifischen Eigenschaften von Hirn-Reaktionen auf Wahrscheinlichkeitssequenzen über sensorische Modalitäten hinweg und vor allem in der somatosensorischen Modalität nicht gut charakterisiert.
Die hier vorgestellte Arbeit untersucht MMRs über die auditorische, visuelle und somatosensorische Modalität hinweg anhand von Elektroenzephalographie (EEG) und funktioneller Magnetresonanztomographie (fMRT). Wir gestalteten wahrscheinlichkeitsbasierte Reizsequenzen, um MMRs auszulösen und zu charakterisieren und verwendeten komputationale Modellierung der Reaktionsdynamiken, um verschiedene Aspekte des generativen Modells des Gehirns von der sensorischen Umwelt zu untersuchen. In der ersten Studie verwendeten wir ein volatiles "Roving-Stimulus"-Paradigma, um somatosensorische MMRs auszulösen und modellierten die Einzel-Proben der EEG-Signale im sensorischen und Quell-Raum. Modellvergleiche legten nahe, dass die Reaktionen Bayes’sche Inferenz abbilden, basierend auf der Schätzung von Transitionswahrscheinlichkeiten und limitierter Integration von Information der jüngsten Vergangenheit, welche eine Anpassung an Umweltänderungen ermöglicht. Die Ergebnisse legen nahe, dass somatosen-sorische MMRs eine initiale Diskrepanz zwischen sensorischer Einspeisung und Modellüberzeugung reflektieren welche durch "confidence-corrected surprise" (CS) repräsentiert ist, gefolgt von Modelanpassungsdynamiken repräsentiert von "Bayesian surprise" (BS). Für die zweite und dritte Studie haben wir ein Tri-Modales "Roving-Stimulus"-Paradigma gestaltet, um modalitätsspezifische und modalitätsübergreifende Eigenschaften von Diskrepanzprozessierung zu umreißen. Komputationale Modellierung von EEG-Signalen in Studie 2 legte nahe, dass Einzel-Proben Dynamiken Bayes’sche Inferenz abbilden, basierend auf der Schätzung von unimodalen Transitionswahrscheinlichkeiten sowie modalitätsübergreifenden bedingten Abhängigkeiten. Während frühe Diskrepanzprozessierung um die MMN dazu tendierten CS zu reflektieren, so reflektierten spätere MMRs um die P3 eher BS, in Übereinstimmung mit der somatosensorischen Studie. Abschließend zeigten die fMRT-Ergebnisse der Studie 3 dass MMRs durch eine Interaktion von modalitätsspezifischen Regionen in sensorischen Kortizes höherer Ordnung mit einem modalitätsübergreifenden fronto-parietalen Netzwerk generiert werden. Inferior parietale Regionen im Speziellen waren sensitiv gegenüber Erwartungsverstoß in Bezug auf die modalitätsübergreifenden Wahrscheinlichkeiten in den Reizsequenzen. Insgesamt weisen unsere Ergebnisse darauf hin, dass MMRs über die Sinne hinweg Prozesse von wahrscheinlichkeitsbasierter Inferenz in einer komplexen und inhärent multi-modalen Umwelt darstellen
Brain Computations and Connectivity [2nd edition]
This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations.
Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed.
The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes.
Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions.
This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press.
Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics
Superconducting Proximity Effect in Nanowire Josephson Junctions
Semiconducting nanowires contacted with superconductors are an interesting class of hybrid mesoscopic devices, in which charge transport is quantum mechanical due to the confinement potential of the nanowire, as well as the quantum mechanical nature of superconductivity. Especially interesting is the case where transport is phase coherent, resulting in the semiconductor inheriting certain properties of the superconductor (e.g. sustaining a dissipation-less current), a phenomenon called proximity superconductivity. Proximity effects allow for rich and interesting physics to occur at the intersection of superconductivity and mesoscopic transport, which are the subject of study in this thesis. Furthermore, proximitized nanowire devices with a strong spin-orbit coupling are promising candidates for the realization of Majorana bound states — quasiparticle states that are topological in nature and have enjoyed much recent attention due to their applications to topological quantum computing. As well as fundamental curiosity about proximity phenomena, it is imperative to fully understand them in order to utilize hybrid nanowire devices as the building blocks of a topological quantum computer.
In this thesis we present experimental studies of three generations of Nb/InAs nanowire/Nb Josephson junctions in which proximity superconductivity is observed. Cryogenic transport measurements allow us to identify Andreev reflection as the mechanism behind the proximity effects — a mechanism wherein an electron incident on the superconductor/semiconductor interface is retro-reflected as a (conduction band) hole, carrying a charge equal to twice the electronic charge from the semiconductor into the superconductor, where it is carried as a Cooper pair. This mechanism is critically dependent on the transparency of the superconductor/semiconductor interface, whose qualities are successively improved over the three generations of devices. Further interesting and rich phenomena are also observed in the nanowire junctions, including Multiple Andreev reflections, Andreev bound states, and the likelihood of a novel form of Josephson interference called Orbital Josephson interference. We present theoretical and numerical studies that model these observed phenomena. Finally, we explore the relevance of this work to the topological quantum computing community by describing future challenges and experiments which can reveal the physics of Majorana bound states in similar systems. We give an in-depth proposal involving a proximitized nanowire and a quantum dot which can be used to verify the topological nature of the system, as well as read out the parity state of the Majorana bound states within the system
Machine Learning and Its Application to Reacting Flows
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
Program analysis for android security and reliability
The recent, widespread growth and adoption of mobile devices have revolutionized the way users interact with technology. As mobile apps have become increasingly prevalent, concerns regarding their security and reliability have gained significant attention. The ever-expanding mobile app ecosystem presents unique challenges in ensuring the protection of user data and maintaining app robustness. This dissertation expands the field of program analysis with techniques and abstractions tailored explicitly to enhancing Android security and reliability. This research introduces approaches for addressing critical issues related to sensitive information leakage, device and user fingerprinting, mobile medical score calculators, as well as termination-induced data loss. Through a series of comprehensive studies and employing novel approaches that combine static and dynamic analysis, this work provides valuable insights and practical solutions to the aforementioned challenges. In summary, this dissertation makes the following contributions: (1) precise identifier leak tracking via a novel algebraic representation of leak signatures, (2) identifier processing graphs (IPGs), an abstraction for extracting and subverting user-based and device-based fingerprinting schemes, (3) interval-based verification of medical score calculator correctness, and (4) identifying potential data losses caused by app termination
Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023
Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
東北大学電気通信研究所研究活動報告 第29号(2022年度)
紀要類(bulletin)departmental bulletin pape
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
Three recent breakthroughs due to AI in arts and science serve as motivation:
An award winning digital image, protein folding, fast matrix multiplication.
Many recent developments in artificial neural networks, particularly deep
learning (DL), applied and relevant to computational mechanics (solid, fluids,
finite-element technology) are reviewed in detail. Both hybrid and pure machine
learning (ML) methods are discussed. Hybrid methods combine traditional PDE
discretizations with ML methods either (1) to help model complex nonlinear
constitutive relations, (2) to nonlinearly reduce the model order for efficient
simulation (turbulence), or (3) to accelerate the simulation by predicting
certain components in the traditional integration methods. Here, methods (1)
and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3)
relying on convolutional neural networks. Pure ML methods to solve (nonlinear)
PDEs are represented by Physics-Informed Neural network (PINN) methods, which
could be combined with attention mechanism to address discontinuous solutions.
Both LSTM and attention architectures, together with modern and generalized
classic optimizers to include stochasticity for DL networks, are extensively
reviewed. Kernel machines, including Gaussian processes, are provided to
sufficient depth for more advanced works such as shallow networks with infinite
width. Not only addressing experts, readers are assumed familiar with
computational mechanics, but not with DL, whose concepts and applications are
built up from the basics, aiming at bringing first-time learners quickly to the
forefront of research. History and limitations of AI are recounted and
discussed, with particular attention at pointing out misstatements or
misconceptions of the classics, even in well-known references. Positioning and
pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at
CMES-Computer Modeling in Engineering & Science
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