859 research outputs found
A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection
The broadening dependency and reliance that modern societies have on essential services
provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical
Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just
at the economic level but also in terms of physical damage and even loss of human life. Complementing
traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring
Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are
in place and compliant with standards and internal policies. Forensics assist the investigation of past security
incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can
be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the
latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing
in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of
tackling the requirements imposed by massively distributed and complex Industrial Automation and Control
Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and
redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced
a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the
collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic
template for a converged platform. These results are intended to guide future research on forensics and
compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio
Deep generative models for network data synthesis and monitoring
Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network.
Although networks inherently
have abundant amounts of monitoring data, its access and effective measurement is
another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset
without leaking commercial sensitive information. Second, it could be very expensive
to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of
flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources
in the network element that can be applied to support the measurement function are
too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex
structure. Various emerging optimization-based solutions (e.g., compressive sensing)
or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet
meet the current network requirements.
The contributions made in this thesis significantly advance the state of the art in
the domain of network measurement and monitoring techniques. Overall, we leverage
cutting-edge machine learning technology, deep generative modeling, throughout the
entire thesis. First, we design and realize APPSHOT , an efficient city-scale network
traffic sharing with a conditional generative model, which only requires open-source
contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we
design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time
network telemetry system with latent GANs and spectral-temporal networks. Finally,
we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through
this research are summarized, and interesting topics are discussed for future work in
this domain. All proposed solutions have been evaluated with real-world datasets and
applied to support different applications in real systems
Joint multi-objective MEH selection and traffic path computation in 5G-MEC systems
Multi-access Edge Computing (MEC) is an emerging technology that allows to reduce the service latency and traffic congestion and to enable cloud offloading and context awareness. MEC consists in deploying computing devices, called MEC Hosts (MEHs), close to the user. Given the mobility of the user, several problems rise. The first problem is to select a MEH to run the service requested by the user. Another problem is to select the path to steer the traffic from the user to the selected MEH. The paper jointly addresses these two problems. First, the paper proposes a procedure to create a graph that is able to capture both network-layer and application-layer performance. Then, the proposed graph is used to apply the Multi-objective Dijkstra Algorithm (MDA), a technique used for multi-objective optimization problems, in order to find solutions to the addressed problems by simultaneously considering different performance metrics and constraints. To evaluate the performance of MDA, the paper implements a testbed based on AdvantEDGE and Kubernetes to migrate a VideoLAN application between two MEHs. A controller has been realized to integrate MDA with the 5G-MEC system in the testbed. The results show that MDA is able to perform the migration with a limited impact on the network performance and user experience. The lack of migration would instead lead to a severe reduction of the user experience.publishedVersio
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Modern computing: Vision and challenges
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
COVID-19 Vaccine Refusal and Delay among Adults in Italy: Evidence from the OBVIOUS Project, a National Survey in Italy
BACKGROUND: Vaccine hesitancy was defined by the World Health Organization (WHO) in 2019 as a major threat to global health. In Italy, reluctance to receive vaccines is a widespread phenomenon that was amplified during the COVID-19 pandemic by fear and mistrust in government. This study aims to depict different profiles and characteristics of people reluctant to vaccinate, focusing on the drivers of those who are in favor of and those who are opposed to receiving the COVID-19 vaccine. METHODS: A sample of 10,000 Italian residents was collected. A survey on COVID-19 vaccination behavior and possible determinants of vaccine uptake, delay, and refusal was administered to participants through a computer-assisted web interviewing method. RESULTS: In our sample, 83.2% stated that they were vaccinated as soon as possible ("vaccinators"), 8.0% delayed vaccination ("delayers"), and 6.7% refused to be vaccinated ("no-vaccinators"). In general, the results show that being female, aged between 25 and 64, with an education level less than a high school diploma or above a master's degree, and coming from a rural area were characteristics significantly associated with delaying or refusing COVID-19 vaccination. In addition, it was found that having minimal trust in science and/or government (i.e., 1 or 2 points on a scale from 1 to 10), using alternative medicine as the main source of treatment, and intention to vote for certain parties were characteristics associated with profiles of "delayers" or "no-vaccinators". Finally, the main reported motivation for delaying or not accepting vaccination was fear of vaccine side effects (55.0% among delayers, 55.6% among no-vaccinators). CONCLUSION: In this study, three main profiles of those who chose to be vaccinated are described. Since those who are in favor of vaccines and those who are not usually cluster in similar sociodemographic categories, we argue that findings from this study might be useful to policy makers when shaping vaccine strategies and choosing policy instruments
Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
Quantum processing units (QPUs) are currently exclusively available from
cloud vendors. However, with recent advancements, hosting QPUs is soon possible
everywhere. Existing work has yet to draw from research in edge computing to
explore systems exploiting mobile QPUs, or how hybrid applications can benefit
from distributed heterogeneous resources. Hence, this work presents an
architecture for Quantum Computing in the edge-cloud continuum. We discuss the
necessity, challenges, and solution approaches for extending existing work on
classical edge computing to integrate QPUs. We describe how warm-starting
allows defining workflows that exploit the hierarchical resources spread across
the continuum. Then, we introduce a distributed inference engine with hybrid
classical-quantum neural networks (QNNs) to aid system designers in
accommodating applications with complex requirements that incur the highest
degree of heterogeneity. We propose solutions focusing on classical layer
partitioning and quantum circuit cutting to demonstrate the potential of
utilizing classical and quantum computation across the continuum. To evaluate
the importance and feasibility of our vision, we provide a proof of concept
that exemplifies how extending a classical partition method to integrate
quantum circuits can improve the solution quality. Specifically, we implement a
split neural network with optional hybrid QNN predictors. Our results show that
extending classical methods with QNNs is viable and promising for future work.Comment: 16 pages, 5 figures, Vision Pape
Facing Unknown: Open-World Encrypted Traffic Classification Based on Contrastive Pre-Training
Traditional Encrypted Traffic Classification (ETC) methods face a significant
challenge in classifying large volumes of encrypted traffic in the open-world
assumption, i.e., simultaneously classifying the known applications and
detecting unknown applications. We propose a novel Open-World Contrastive
Pre-training (OWCP) framework for this. OWCP performs contrastive pre-training
to obtain a robust feature representation. Based on this, we determine the
spherical mapping space to find the marginal flows for each known class, which
are used to train GANs to synthesize new flows similar to the known parts but
do not belong to any class. These synthetic flows are assigned to Softmax's
unknown node to modify the classifier, effectively enhancing sensitivity
towards known flows and significantly suppressing unknown ones. Extensive
experiments on three datasets show that OWCP significantly outperforms existing
ETC and generic open-world classification methods. Furthermore, we conduct
comprehensive ablation studies and sensitivity analyses to validate each
integral component of OWCP.Comment: Accepted by 2023 IEEE ISCC, 6 pages, 5 figure
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