124 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Testing SOAR Tools in Use
Modern security operation centers (SOCs) rely on operators and a tapestry of
logging and alerting tools with large scale collection and query abilities. SOC
investigations are tedious as they rely on manual efforts to query diverse data
sources, overlay related logs, and correlate the data into information and then
document results in a ticketing system. Security orchestration, automation, and
response (SOAR) tools are a new technology that promise to collect, filter, and
display needed data; automate common tasks that require SOC analysts' time;
facilitate SOC collaboration; and, improve both efficiency and consistency of
SOCs. SOAR tools have never been tested in practice to evaluate their effect
and understand them in use. In this paper, we design and administer the first
hands-on user study of SOAR tools, involving 24 participants and 6 commercial
SOAR tools. Our contributions include the experimental design, itemizing six
characteristics of SOAR tools and a methodology for testing them. We describe
configuration of the test environment in a cyber range, including network,
user, and threat emulation; a full SOC tool suite; and creation of artifacts
allowing multiple representative investigation scenarios to permit testing. We
present the first research results on SOAR tools. We found that SOAR
configuration is critical, as it involves creative design for data display and
automation. We found that SOAR tools increased efficiency and reduced context
switching during investigations, although ticket accuracy and completeness
(indicating investigation quality) decreased with SOAR use. Our findings
indicated that user preferences are slightly negatively correlated with their
performance with the tool; overautomation was a concern of senior analysts, and
SOAR tools that balanced automation with assisting a user to make decisions
were preferred
Athermal Phonon Sensors in Searches for Light Dark Matter
In recent years, theoretical and experimental interest in dark matter (DM)
candidates have shifted focus from primarily Weakly-Interacting Massive
Particles (WIMPs) to an entire suite of candidates with masses from the
zeV-scale to the PeV-scale to 30 solar masses. One particular recent
development has been searches for light dark matter (LDM), which is typically
defined as candidates with masses in the range of keV to GeV. In searches for
LDM, eV-scale and below detector thresholds are needed to detect the small
amount of kinetic energy that is imparted to nuclei in a recoil. One such
detector technology that can be applied to LDM searches is that of
Transition-Edge Sensors (TESs). Operated at cryogenic temperatures, these
sensors can achieve the required thresholds, depending on the optimization of
the design.
In this thesis, I will motivate the evidence for DM and the various DM
candidates beyond the WIMP. I will then detail the basics of TES
characterization, expand and apply the concepts to an athermal phonon
sensor--based Cryogenic PhotoDetector (CPD), and use this detector to carry out
a search for LDM at the surface. The resulting exclusion analysis provides the
most stringent limits in DM-nucleon scattering cross section (comparing to
contemporary searches) for a cryogenic detector for masses from 93 to 140 MeV,
showing the promise of athermal phonon sensors in future LDM searches.
Furthermore, unknown excess background signals are observed in this LDM search,
for which I rule out various possible sources and motivate stress-related
microfractures as an intriguing explanation. Finally, I will shortly discuss
the outlook of future searches for LDM for various detection channels beyond
nuclear recoils.Comment: 243 pages, Ph.D. Thesis in Physics at UC Berkele
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors
This paper studies the problem of secure state estimation of a linear
time-invariant (LTI) system with bounded noise in the presence of sparse
attacks on an unknown, time-varying set of sensors. In other words, at each
time, the attacker has the freedom to choose an arbitrary set of no more that
sensors and manipulate their measurements without restraint. To this end,
we propose a secure state estimation scheme and guarantee a bounded estimation
error subject to -sparse observability and a mild, technical assumption
that the system matrix has no degenerate eigenvalues. The proposed scheme
comprises a design of decentralized observer for each sensor based on the local
observable subspace decomposition. At each time step, the local estimates of
sensors are fused by solving an optimization problem to obtain a secure
estimation, which is then followed by a local detection-and-resetting process
of the decentralized observers. The estimation error is shown to be
upper-bounded by a constant which is determined only by the system parameters
and noise magnitudes. Moreover, we optimize the detector threshold to ensure
that the benign sensors do not trigger the detector. The efficacy of the
proposed algorithm is demonstrated by its application on a benchmark example of
IEEE 14-bus system
Translational Functional Imaging in Surgery Enabled by Deep Learning
Many clinical applications currently rely on several imaging modalities such as Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), Computed Tomography (CT), etc. All such modalities provide valuable patient data to the clinical staff to aid clinical decision-making and patient care. Despite the undeniable success of such modalities, most of them are limited to preoperative scans and focus on morphology analysis, e.g. tumor segmentation, radiation treatment planning, anomaly detection, etc. Even though the assessment of different functional properties such as perfusion is crucial in many surgical procedures, it remains highly challenging via simple visual inspection. Functional imaging techniques such as Spectral Imaging (SI) link the unique optical properties of different tissue types with metabolism changes, blood flow, chemical composition, etc. As such, SI is capable of providing much richer information that can improve patient treatment and care. In particular, perfusion assessment with functional imaging has become more relevant due to its involvement in the treatment and development of several diseases such as cardiovascular diseases. Current clinical practice relies on Indocyanine Green (ICG) injection to assess perfusion. Unfortunately, this method can only be used once per surgery and has been shown to trigger deadly complications in some patients (e.g. anaphylactic shock).
This thesis addressed common roadblocks in the path to translating optical functional imaging modalities to clinical practice. The main challenges that were tackled are related to a) the slow recording and processing speed that SI devices suffer from, b) the errors introduced in functional parameter estimations under changing illumination conditions, c) the lack of medical data, and d) the high tissue inter-patient heterogeneity that is commonly overlooked. This framework follows a natural path to translation that starts with hardware optimization. To overcome the limitation that the lack of labeled clinical data and current slow SI devices impose, a domain- and task-specific band selection component was introduced. The implementation of such component resulted in a reduction of the amount of data needed to monitor perfusion. Moreover, this method leverages large amounts of synthetic data, which paired with unlabeled in vivo data is capable of generating highly accurate simulations of a wide range of domains. This approach was validated in vivo in a head and neck rat model, and showed higher oxygenation contrast between normal and cancerous tissue, in comparison to a baseline using all available bands. The need for translation to open surgical procedures was met by the implementation of an automatic light source estimation component. This method extracts specular reflections from low exposure spectral images, and processes them to obtain an estimate of the light source spectrum that generated such reflections. The benefits of light source estimation were demonstrated in silico, in ex vivo pig liver, and in vivo human lips, where the oxygenation estimation error was reduced when utilizing the correct light source estimated with this method. These experiments also showed that the performance of the approach proposed in this thesis surpass the performance of other baseline approaches.
Video-rate functional property estimation was achieved by two main components: a regression and an Out-of-Distribution (OoD) component. At the core of both components is a compact SI camera that is paired with state-of-the-art deep learning models to achieve real time functional estimations. The first of such components features a deep learning model based on a Convolutional Neural Network (CNN) architecture that was trained on highly accurate physics-based simulations of light-tissue interactions. By doing this, the challenge of lack of in vivo labeled data was overcome. This approach was validated in the task of perfusion monitoring in pig brain and in a clinical study involving human skin. It was shown that this approach is capable of monitoring subtle perfusion changes in human skin in an arm clamping experiment. Even more, this approach was capable of monitoring Spreading Depolarizations (SDs) (deoxygenation waves) in the surface of a
pig brain. Even though this method is well suited for perfusion monitoring in domains that are well represented with the physics-based simulations on which it was trained, its performance cannot be guaranteed for outlier domains. To handle outlier domains, the task of ischemia monitoring was rephrased as an OoD detection task. This new functional estimation component comprises an ensemble of Invertible Neural Networks (INNs) that only requires perfused tissue data from individual patients to detect ischemic tissue as outliers. The first ever clinical study involving a video-rate capable SI camera in laparoscopic partial nephrectomy was designed to validate this approach. Such study revealed particularly high inter-patient tissue heterogeneity under the presence of pathologies (cancer). Moreover, it demonstrated that this personalized approach is now capable of monitoring ischemia at video-rate with SI during laparoscopic surgery.
In conclusion, this thesis addressed challenges related to slow image recording and processing during surgery. It also proposed a method for light source estimation to facilitate translation to open surgical procedures. Moreover, the methodology proposed in this thesis was validated in a wide range of domains: in silico, rat head and neck, pig liver and brain, and human skin and kidney. In particular, the first clinical trial with spectral imaging in minimally invasive surgery demonstrated that video-rate ischemia monitoring is now possible with deep learning
- …