5,084 research outputs found
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
An Efficient Authentication Protocol for Smart Grid Communication Based on On-Chip-Error-Correcting Physical Unclonable Function
Security has become a main concern for the smart grid to move from research
and development to industry. The concept of security has usually referred to
resistance to threats by an active or passive attacker. However, since smart
meters (SMs) are often placed in unprotected areas, physical security has
become one of the important security goals in the smart grid. Physical
unclonable functions (PUFs) have been largely utilized for ensuring physical
security in recent years, though their reliability has remained a major problem
to be practically used in cryptographic applications. Although fuzzy extractors
have been considered as a solution to solve the reliability problem of PUFs,
they put a considerable computational cost to the resource-constrained SMs. To
that end, we first propose an on-chip-error-correcting (OCEC) PUF that
efficiently generates stable digits for the authentication process. Afterward,
we introduce a lightweight authentication protocol between the SMs and
neighborhood gateway (NG) based on the proposed PUF. The provable security
analysis shows that not only the proposed protocol can stand secure in the
Canetti-Krawczyk (CK) adversary model but also provides additional security
features. Also, the performance evaluation demonstrates the significant
improvement of the proposed scheme in comparison with the state-of-the-art
Reinforcement learning in large state action spaces
Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios.
This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory).
In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications
Assessing Atmospheric Pollution and Its Impacts on the Human Health
This reprint contains articles published in the Special Issue entitled "Assessing Atmospheric Pollution and Its Impacts on the Human Health" in the journal Atmosphere. The research focuses on the evaluation of atmospheric pollution by statistical methods on the one hand, and on the other hand, on the evaluation of the relationship between the level of pollution and the extent of its effect on the population's health, especially on pulmonary diseases
Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity
IoT networks are increasingly becoming target of sophisticated new
cyber-attacks. Anomaly-based detection methods are promising in finding new
attacks, but there are certain practical challenges like false-positive alarms,
hard to explain, and difficult to scale cost-effectively. The IETF recent
standard called Manufacturer Usage Description (MUD) seems promising to limit
the attack surface on IoT devices by formally specifying their intended network
behavior. In this paper, we use SDN to enforce and monitor the expected
behaviors of each IoT device, and train one-class classifier models to detect
volumetric attacks.
Our specific contributions are fourfold. (1) We develop a multi-level
inferencing model to dynamically detect anomalous patterns in network activity
of MUD-compliant traffic flows via SDN telemetry, followed by packet inspection
of anomalous flows. This provides enhanced fine-grained visibility into
distributed and direct attacks, allowing us to precisely isolate volumetric
attacks with microflow (5-tuple) resolution. (2) We collect traffic traces
(benign and a variety of volumetric attacks) from network behavior of IoT
devices in our lab, generate labeled datasets, and make them available to the
public. (3) We prototype a full working system (modules are released as
open-source), demonstrates its efficacy in detecting volumetric attacks on
several consumer IoT devices with high accuracy while maintaining low false
positives, and provides insights into cost and performance of our system. (4)
We demonstrate how our models scale in environments with a large number of
connected IoTs (with datasets collected from a network of IP cameras in our
university campus) by considering various training strategies (per device unit
versus per device type), and balancing the accuracy of prediction against the
cost of models in terms of size and training time.Comment: 18 pages, 13 figure
KYT2022 Finnish Research Programme on Nuclear Waste Management 2019–2022 : Final Report
KYT2022 (Finnish Research Programme on Nuclear Waste Management 2019–2022), organised by the Ministry of Economic Affairs and Employment, was a national research programme with the objective to ensure that the authorities have sufficient levels of nuclear expertise and preparedness that are needed for safety of nuclear waste management.
The starting point for public research programs on nuclear safety is that they create the conditions for maintaining the knowledge required for the continued safe and economic use of nuclear energy, developing new know-how and participating in international collaboration.
The content of the KYT2022 research programme was composed of nationally important research topics, which are the safety, feasibility and acceptability of nuclear waste management.
KYT2022 research programme also functioned as a discussion and information-sharing forum for the authorities, those responsible for nuclear waste management and the research organizations, which helped to make use of the limited research resources. The programme aimed to develop national research infrastructure, ensure the continuing availability of expertise, produce high-level scientific research and increase general knowledge of nuclear waste management
SecureCyclon: Dependable Peer Sampling
Overlay management is the cornerstone of building robust and dependable
Peer-to-Peer systems. A key component for building such overlays is the
peer-sampling service, a mechanism that continuously supplies each node with a
set of up-to-date peers randomly selected across all alive nodes. Arguably, the
most pernicious malicious action against such mechanisms is the provision of
arbitrarily created links that point at malicious nodes. This paper proposes
SecureCyclon, a peer-sampling protocol that deterministically eliminates the
ability of malicious nodes to overrepresent themselves in Peer-to-Peer
overlays. To the best of our knowledge, this is the first protocol to offer
this property, as previous works were able to only bound the proportion of
excessive links to malicious nodes, without completely eliminating them.
SecureCyclon redefines the concept of node descriptors from just being
containers of information that enable communication with specific nodes, to
being communication certificates that traverse the network and enable nodes to
provably discover malicious nodes. We evaluate our solution with the conduction
of extended simulations, and we demonstrate that it provides resilience even at
the extreme condition of 40% malicious node participation.Comment: 12 pages, 7 figures, ICDCS 202
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
Cyberbullying in educational context
Kustenmacher and Seiwert (2004) explain a man’s inclination to resort to technology in his interaction with the environment and society. Thus, the solution to the negative consequences of Cyberbullying in a technologically dominated society is represented by technology as part of the technological paradox (Tugui, 2009), in which man has a dual role, both slave and master, in the interaction with it. In this respect, it is noted that, notably after 2010, there have been many attempts to involve artificial intelligence (AI) to recognize, identify, limit or avoid the manifestation of aggressive behaviours of the CBB type. For an overview of the use of artificial intelligence in solving various problems related to CBB, we extracted works from the Scopus database that respond to the criterion of the existence of the words “cyberbullying” and “artificial intelligence” in the Title, Keywords and Abstract. These articles were the subject of the content analysis of the title and, subsequently, only those that are identified as a solution in the process of recognizing, identifying, limiting or avoiding the manifestation of CBB were kept in the following Table where we have these data synthesized and organized by years
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