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threatFinderAI: Automated threat modeling Applied to LLM system Integration

ThreatFinderAI: Automated Threat Modeling Applied to LLM Sys...

引用

International conference on Network and Service Management

作者： Jan Von der Assen Alberto Huertas Jamo Sharif Chao Feng Gérôme Bovet Burkhard Stiller Department of Informatics Communication Systems Group University of Zurich UZH Zürich Switzerland Cyber-Defence Campus Armasuisse Science & Technology Thun Switzerland

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

Artificial Intelligence (AI) is a rapidly integrated technology, significantly contributing to advancements like 6G. However, its swift adoption raises considerable security concerns. Large Language Models (LLMs) pose risks such as spear phishing, code injections, and remote code execution. Conventional threat modeling, used in secure software development, faces challenges when applied to AI systems, as existing methodologies are designed for traditional software. Furthermore, AI-specific threat modeling research is sparse and lacks approaches providing practical support or automation. thus, this demo paper presents threatFinderAI, an asset-centric threat modeling and risk assessment framework. threatFinderAI fulfills seven steps aligned with AI system design and transforms AI threat and control knowledge bases into a queryable knowledge graph for automated asset identification and threat elicitation. It also proposes business impact analysis and expert estimates for AI threat impact quantification. In the demonstration, threatFinderAI is illustrated by securing a customer care application relying on LLMs. through this, it is demonstrated how the proposed framework can be used to identify relevant threats and practical countermeasures and communicate strategic risk.

关键词： threat modeling Codes Transforms system integration Software Security Risk management Artificial intelligence system analysis and design Software development management

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Integrating end-to-end prediction-with-optimization for distributed hydrogen energy system scheduling*

Integrating end-to-end prediction-with-optimization for dist...

引用

IEEE International conference on Automation Science and Engineering (CASE)

作者： Wangyi Guo Zhanbo Xu Zhequn Zhou Jinhui Liu Jiang Wu Haoming Zhao Xiaohong Guan School of Future Technology Xi'an Jiaotong University Xi’an China Moe Klinns Lab and Systems Engineering Institute of Xi’an Jiaotong University Xi’an China Department of Automation TNLIST Center for Intelligent and Networked Systems (CFINS) Tsinghua University Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

the rapid growth of distributed renewable energy resources challenges electricity grids due to its intermittent and stochastic character. In addition to integrating energy storage devices, accurately predicting renewable energy is also crucial for achieving optimal scheduling in distributed energy systems. While neural network prediction models can effectively capture uncertainties in renewable energy, they often fail to consider downstream optimization problems, leading to significant decision errors even with minor prediction errors. In this work, we propose an integrated end-to-end prediction-with-optimization (IPO) approach that combines prediction and optimization processes by replacing prediction errors with decision errors during training. Numerical results, based on a distributed hydrogen energy system, demonstrate that the IPO approach outperforms the traditional two-stage prediction-then-optimization approach, achieving a 14.1% to 18.4% reduction in decision error and a 3.36% to 11.85% increase in the optimal ratio.

关键词： Training Renewable energy sources Uncertainty Hydrogen Neural networks Stochastic processes Optimal scheduling Predictive models Power grids Energy storage

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Energy Management system for the Optimal Operation of a Grid-Connected Building with Renewables and an Electric Delivery Vehicle

Energy Management System for the Optimal Operation of a Grid...

引用

Eurocon

作者： Stefano Bracco Matteo Fresia Electrical Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN) University of Genoa Genoa Italy

the paper proposes a Mixed-Integer Linear Programming (MILP) optimization model used to define an Energy Management system (EMS) to daily operate two Renewable Energy Source (RES) power plants and an Electric Vehicle (EV) charging station installed in a building. the analysed case study refers to a building located in a small town on an island in the South of Italy. the building hosts the post office and the EV is a van used to transport mail. the building is connected to the external distribution grid, allowing active and reactive power exchanges. Different scenarios have been evaluated, considering the installation of photovoltaic (PV) modules, small wind turbines (WTs) and enabling vehicle-to-building (V2B) application. the analysis aims to highlight the role of RES power plants to limit reactive power absorption from the distribution grid and the contribution of the electric vehicle to mitigate RES curtailment.

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Self-Renewal Machine Learning Approach for Fast Wireless Network optimization

Self-Renewal Machine Learning Approach for Fast Wireless Net...

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IEEE Internatonal conference on Mobile Adhoc and Sensor systems (MASS)

作者： Oluwaseun T. Ajayi Xianghui Cao Hangguan Shan Yu Cheng Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago USA School of Automation Southeast University Nanjing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

the throughput maximization in multi-hop wireless networks is largely limited by interference due to the reuse of the channel resources. Although machine learning (ML) can accelerate the optimization of wireless network capacity, the existing system can become limited because of insufficient knowledge from available data. We propose a self-renewal ML (SRML) method that incrementally improves the throughput maximization of future optimization instances through the design of a data selection algorithm for scheduling structure classification and application identification model retraining. With one round of implementation, the SRML method outperforms the fixed ML (FML) method, random (RAND) method and Greedy Heuristic method in the multi-commodity flow deployment setting with an average achievable throughput of 100% for small flows and at least 79% for large flows, relative to the delayed column generation (DCG) benchmark algorithm, while reducing the computational complexity and achieving a high solution efficiency. By leveraging the transfer learning of parameters during self-renewal, the computational cost of model training is reduced by at least 71.09%.

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A Program Slicer for Java (Tool Paper) 20th

A Program Slicer for Java (Tool Paper)

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20th International conference on Software Engineering and Formal Methods (SEFM)

作者： Galindo, Carlos Perez, Sergio Silva, Josep Univ Politecn Valencia Dept Sistemas Informat & Comp Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031171086;9783031171079

Program slicing is a static analysis technique used in debugging, compiler optimization, program parallelization, and program specialization. However, current implementations for Java are proprietary software, pay-per-use, and closed source. Most public and open-source implementations for Java are not maintained anymore or they are obsolete because they do not cover novel Java features or they do not implement advanced techniques for the treatment of objects, exceptions, and unconditional jumps. this paper presents JavaSlicer, a public and open-source tool written in Java for slicing Java programs, which supports the aforementioned features. We present its usage, architecture, and performance.

关键词： Program slicing system Dependence Graph Tool paper

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Automation of the Power Plant with Variable Parameters of the optimization Criterion 20

Automation of the Power Plant with Variable Parameters of th...

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20th IEEE International conference on Modern Electrical and Energy systems, MEES 2021

作者： Kukharenko, Dmitriy Novokhatko, Olha Maznytska, Oksana DIgtiar, Sergii Sakun, Oksana Nykyforova, Olena Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Department of Electronic Devices Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Department of Biotechnology and Bioengineering Kremenchuk Ukraine

ISBN: (纸本)9781665423663

Scientific novelty of the work consists in use of the proposed method of optimal control of biogas installation, as well as partial automation of the energy complex. In addition, the increase in biogas can be from 1 to 2.6 per cent during 30-days non-stop operation of the system. In additional, a signal corresponding to the effective operation of the system was determined, ie the criterion of optimality, characterized in that it has a flexible structure and can take values ranging from the maximum value added criterion to the efficiency criterion values. the technical result consists in reducing energy consumption during the production of biogas by means of full or partial non-stop loading of the system. © 2021 IEEE.

关键词： Biogas

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Improving Computational Efficiency in Power systems: Development of Model Reduction Framework with Error-Bound for Wind Turbines-Based on DFIG

Improving Computational Efficiency in Power Systems: Develop...

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International Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Muhammad Latif Kanwal Waqar Hira Ambreen Muhammad Imran Sajid Bashir Department of EE MCS NUST Rawalpindi Pakistan Department of ME UET Taxila Pakistan Department of EE SS-CASE-IT Islamabad Pakistan Department of EE CEME NUST Rawalpindi Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

Modern wind turbines are steady, controlled, and observable because of the analytical and modeling precision used in their design. In the context of a varying speed-based wind turbine powered by a double-feeded induction generator, this article offers a state-of-the-art technique for reducing model order. the proposed method leverages state-space representations to establish a comprehensive and systematic framework to analyze wind turbine performance and ensure conformity to grid requirements. Dimension reduction in the large state-space model is accomplished using time-constrained Gramians and a unique, balanced realization procedure. the reduced-order system is stable and offers less approximation error. As an additional perk, the proposed method gives researchers an a priori formula for the error-bound, which is easier to compute. Using extensive simulation data, we prove that our approach best optimizes models incorporating wind turbines with varying rotational speeds in the context of stability and error calculation compared to other approaches. To improve the efficacy and accuracy of their modeling procedures, wind turbine analysis academics and researchers should consider this new methodology seriously.

关键词： Analytical models Computational modeling Doubly fed induction generators Power system stability Approximation error Reduced order systems Generators Wind turbines optimization Context modeling

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Neuro-Fuzzy Methods for Detecting Sensor Failures in Helicopters Turboshaft Engines 8

Neuro-Fuzzy Methods for Detecting Sensor Failures in Helicop...

引用

Machine Learning Workshop of the 8th International conference on Computational Linguistics and Intelligent systems, MLW-CoLInS 2024

作者： Vysotska, Victoria Vladov, Serhii Yakovliev, Ruslan Yurko, Alexey Voronin, Andrii Lviv Polytechnic National University Stepan Bandera Street 12 Lviv79013 Ukraine Kremenchuk Flight College Kharkiv National University of Internal Affairs Peremohy Street 17/6 Kremenchuk39605 Ukraine Kremenchuk Mykhailo Ostrohradskyi National University University Street 20 Kremenchuk39600 Ukraine Ivan Kozhedub Kharkiv National Air Force University Sumska Street 77/79 Kharkiv61023 Ukraine

this work is devoted to the development of a neural network method for detecting failures of sensors of helicopters turboshaft engines under on-board operation conditions. the proposed method is based on the use of the ANFIS neuro-fuzzy network with a modified hybrid method for its training. A modification of the hybrid method of training the neuro-fuzzy network ANFIS is proposed, which, through the use of the Adam method as a gradient-based optimization algorithm, as well as the adaptive k-means clustering method for optimizing the shape of fuzzy membership functions, allowed reducing the number of training epochs from 400 to 50 to obtain the minimum the standard deviation of the training error is 2.646 · 10–4 using the Gaussian membership function. An evolutionary system of fuzzy rules has been developed to determine the gas temperature in front of the compressor turbine sensor failure, the compressor defect, and the failure of the free turbine rotor speed sensor failure. the proposed system can be extended by adding new fuzzy rules in order to detect and identify other failures of sensors and components of helicopters turboshaft engines. An experiment was carried out, which consists of computer modeling of the gradual failure of a gas temperature sensor in front of a compressor turbine. the results of a comparative analysis of traditional and neural network methods for detecting failures in helicopters turboshaft engines sensors showed that the maximum errors of the first and second types when using neural network methods did not exceed 0.78 and 0.52 %, while for traditional methods they reached 2.48 and 1.91 %. © 2024 Copyright for this paper by its authors.

关键词： Gas temperature

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Energy Efficiency Evaluation of Five-axis Machine Tools with Different Configurations based on Workspace Stiffness and Machining Energy Consumption

Energy Efficiency Evaluation of Five-axis Machine Tools with...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Xiaojian Liu Hanqi Yang Langning Xing Yang Wang Lemiao Qiu Shuyou Zhang Zhejiang University Hangzhou China Zhejiang High-Grade CNC Machine Tool Technology Innovation Center Taizhou China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

When machining curved parts with five-axis machine tools, different configurations can lead to differences in machining quality. In this study, the machining stiffness and energy consumption are modeled to evaluate the energy efficiency of five-axis machine tool. To quantitatively calculate static stiffness deformation of machining toolpath, an analytical static stiffness model of the whole machining workspace for the machine is established. According to the requirements of lightweight and balanced machining moving components, the machining energy model is established and the load level of machine tool is quantified. through the blade machining simulation with four typical machine tool configurations, the energy efficiency evaluation is made using the TOPSIS method. the proposed method can realize the energy efficiency evaluation of machining capability for machine tools with multi-dimensional indicators related to machining toolpath and provide a quantitative basis for configuration optimization of machine tools.

关键词： Energy consumption Analytical models Deformation Machining Energy efficiency Machine tools Indexes Feeds optimization Load modeling

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Enhancing Predictability in Deep Reinforcement Learning for Building Temperature Control

Enhancing Predictability in Deep Reinforcement Learning for ...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Luca Ferrarini Alberto Valentini

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In recent years, machine learning-based methods have become increasingly popular for addressing control problems. Within the context of temperature control in buildings, reinforcement learning algorithms stand out as an appealing model-free and fast real-time execution alternative to classical model-based control methods. However, these techniques lack interpretability and require a significant amount of data. In this paper, we investigate the application of an actor-critic reinforcement learning algorithm for the temperature control of a medium-size building through a specific model-based training approach. More specifically, the goal is here to enhance the predictive capabilities of actor-critic schemes by proposing extensions to state-of-the-art algorithms, which integrate a simplified model as a state predictor and incorporate future disturbances. this leads to the so-called Recursive Actor-Critic, Look-Ahead Trajectory Actor-Critic, and Augmented Actor-Critic schemes. Experimental results show that such improved reinforcement learning approaches can achieve performance comparable to model predictive control, without the need for real-time optimization nor a huge amount of data.

关键词： Training Energy consumption Buildings Predictive models Prediction algorithms Real-time systems Temperature control Trajectory optimization Context modeling

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