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Tariff Versus Sanction Under Bounded Rationality 11th

Tariff Versus Sanction Under Bounded Rationality

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11th International conference on network Games, control and Optimization

作者： Pokou, Fredy Fochesato, Marta Le Cadre, Helene Univ Lille Inria CNRS Cent LilleUMR 9189 CRIStAL F-59000 Lille France Swiss Fed Inst Technol Automat Control Lab Dept Elect Engn & Informat Technol Phys Str CH-38092 Zurich Switzerland

ISBN: (纸本)9783031785993;9783031786006

We formulate the design of a taxation mechanism as a Stackelberg game assuming: a) perfect competition, with exogenous prices;b) imperfect competition, captured through a variational inequality approach, with endogenous prices. Three settings of the mechanism are considered: (i) benchmark involving no taxation, (ii) optimum tariff, (iii) optimum sanction. The expected utility maximization formulation of the game is extended further by relying on cumulative prospect theory to account for the bounded rationality of the stakeholders. We derive closed-form mappings linking the outcomes of the three settings. Additionally, we assess the impact of bounded rationality through a new performance metric, the Price of Irrationality. Numerical results are derived on a randomized instance of a gas trading game between Europe, Asia, and Russia.

关键词： Game Theory Sanction Design Cumulative Prospect Theory

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A study on Anomaly-based Intrusion Detection systems Employing Supervised Deep Learning Techniques 8

A study on Anomaly-based Intrusion Detection Systems Employi...

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8th International conference on Inventive systems and control, ICISC 202

作者： Shaffi, Abubucker.S. Chacko, John Velloreuzhathil Eliyan, Greeshma Balaji, S. Gulf College Department of Computing Sciences Oman Al Zahra College for Women Department of Information Technology Ghala Muscat Oman

ISBN: (纸本)9798350386578

The rise of smart cities, driverless automobiles, smart watches, and mobile banking has led to increased reliance on the Internet. Although technology has enormous advantages for people and society, it also introduces threats. Cyber attacks are more common in this digital world and the intruders are working hard to enter into the business websites or an organization data server. Hence, integrating an intrusion detection system (IDS) is essential in the security environment because it enables IT infrastructure to resist threats. Conventional IDS are limited to detect only sophisticated attacks and fails to detect the hidden and other anomalies that occur in the network systems. An accurate and strong approach for IDS must be created to solve this difficulty for the successful functioning of businesses. The present study explores the use of supervised Deep Learning (DL) techniques and recommends an effective model for anomaly detection. The performance evaluation of the model is performed using NSL-KDD dataset and KDDcup99 and the explored DL models in this study are compared in terms of accuracy and precision. © 2024 IEEE.

关键词： network intrusion

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Dynamic Bidirectional-controlled Inverter-Based Grid Optimized by Neural networks

Dynamic Bidirectional-Controlled Inverter-Based Grid Optimiz...

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2024 Vehicle Power and Propulsion conference

作者： Alzayed, Mohamad Lemaire, Michel Chaoui, Hicham Massicotte, Daniel Carleton Univ DoE Ottawa ON Canada UQTR DoEC Trois Rivieres PQ Canada Univ Quebec Trois Rivieres Dept Elect & Comp Engn Trois Rivieres PQ Canada Carleton Univ Dept Elect Ottawa ON Canada

ISBN: (纸本)9798331541613;9798331541606

Grid-based voltage source inverters frequently utilize the droop control technique combined with inner/outer voltage and current regulation mechanisms to ensure a dependable electricity supply. This study seeks to introduce a Cascade-Forward Neural network (CFNN) control approach designed to lead inverter-based grids when operating in grid-connected or islanded modes, focusing on improving the transient state performance of the CFNN technique. The suggested approach involves utilizing the inverter in a bidirectional manner, suitable for a diverse array of battery energy storage systems and distributed generation setups. The proposed strategy leverages CFNN to grasp the inverter's non-linear model, enabling precise monitoring of demand and reference power across various operational scenarios within smart grid applications. Furthermore, the approach redefines the grid control concept, guiding the inverter according to optimal parameters that encompass power demand, reference power, equipment dimensions, and external disturbances. Notably, this method circumvents the need for any manual tuning procedures. Furthermore, incorporating dynamic elements into the approach improves the protection system's responsiveness. This ensures continuous power supply during faults, enabling a more effective and rapid response, enhancing system resilience, and reducing downtime. This dual advantage of better power supply and heightened protection system sensitivity highlights the proposed method's significance in fortifying the reliability of power systems.. To assess the efficacy of the suggested CFNN controller, its power tracking, operational capabilities, and dynamic response are assessed via multiple experimental trials employing the hardware-in-the-loop (HIL) approach across various scenarios. The outcomes of these tests are meticulously compared to a well-established conventional strategy, affirming the effectiveness of the proposed method.

关键词： dynamic control inverter-based grid neural network

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Advanced Persistent Threats Detection based on Deep Learning Approach 6

Advanced Persistent Threats Detection based on Deep Learning...

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IEEE 6th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Eke, Hope Nkiruka Petrovski, Andrei Robert Gordon Univ Sch Comp Aberdeen Scotland Natl Subsea Ctr Sch Comp Aberdeen Scotland

ISBN: (纸本)9798350311259

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses "low-and-slow" approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed "APT(DASAC)" to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) Data input and probing layer - this involves data gathering and pre-processing, (ii) Data analysis layer;applies the core process of "APT(DASAC)" to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer;the ensemble probability approach is utilized to integrate the output and make attack prediction. The framework was validated with three different datasets and three case studies. The proposed approach achieved a significant attacks detection capability of 86.36% with loss as 0.32%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. This suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question.

关键词： Advanced Persistent Threats Cyber-Physical systems Critical Infrastructures Deep Learning Industrial control systems Supervisory control and Data Acquisition

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Learning state observers for recurrent neural network models 63

Learning state observers for recurrent neural network models

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63rd IEEE conference on Decision and control, CDC 2024

作者： Bonassi, Fabio Andersson, Carl Mattsson, Per Schon, Thomas B. Uppsala University Department of Information Technology Lägerhyddsvägen 1 Uppsala751 05 Sweden

ISBN: (纸本)9798350316339

In this paper, we discuss the problem of learning state observers for Recurrent Neural network (RNN) black-box models of dynamical systems. State observers are indeed key to designing state-feedback control laws, such as nonlinear Model Predictive control, with satisfactory closed-loop performance. Besides, they can also improve the training procedure of RNN models themselves. Then, we summarize recent developments aimed at jointly learning RNN models and neural network-based state observers, and we propose a new structure based on the recent S 5 architecture. We finally test various observer structures on a p H neutralization process benchmark system, showing the advantages and shortcomings of each architecture. © 2024 IEEE.

关键词： Neural network models

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network communication optimization of RCCL communication library in Multi-NIC systems 3

Network communication optimization of RCCL communication lib...

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3rd International conference on Algorithms, Microchips, and network Applications, AMNA 2024

作者： He, Shuaiming Wan, Wei Li, Junhong School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China Co. Ltd. Beijing100193 China Tsinghua University Software School Beijing100084 China

ISBN: (纸本)9781510680098

With the widespread application of deep learning frameworks, large-scale computing and GPU programming are receiving increased attention. For upper-layer applications that utilize GPUs for computational communication, such as TensorFlow and PyTorch, improving the communication efficiency of the underlying communication library is of paramount importance to enhance the overall performance of the frameworks. Among them, the RCCL (Rocm Collective Communication Library) GPU communication library, provided by the Rocm (Radeon Open Compute platform) computing platform, supports various collective communication operations and point-to-point operations. Through analysis, we have identified a problem in the initialization and usage of the ring channel network in the RCCL library, specifically in multi-network card systems. This issue results in certain network cards being unable to communicate, leading to wasted system resources. To address this problem, optimizations can be made at the code level by introducing data structures and algorithms to control the invocation of network cards. The goal is to adjust the usage strategy of multiple network cards in the ring channel network without modifying the original design concept of RCCL. After optimization, extensive evaluations were conducted using a large-scale GPU cluster. The optimized RCCL library achieved significant improvements in communication performance. Under a communication scale of 16 compute nodes and 64 GPUs, the peak bandwidth increased from 5.28GB/s to 7.78GB/s. In inter-node collective communication tests, the performance improvement reached up to 60%. The improved RCCL library provides better low-level communication performance for upper-layer applications on the Rocm computing platform, offering enhanced communication support. © 2024 SPIE.

关键词： Graphics processing unit

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Advanced Intrusion Detection systems Leveraging Knowledge Graph-Based Techniques 12

Advanced Intrusion Detection Systems Leveraging Knowledge Gr...

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2024 12th International conference on systems and control, ICSC 2024

作者： Daoud, Mohamed Amine Mostefaoui, Sid Ahmed Mokhtar Meghazi, Hadj Madani Labbadi, Ourida Zenina, Chaima Bouguessa, Abdelkader Ibn-Khaldoun University of Tiaret LRIAS Lab Dept. of Computer Sciences Algeria Ibn-Khaldoun University of Tiaret Dept. of Computer Sciences Algeria

ISBN: (纸本)9798331531812

The integration of intrusion detection systems (IDS) is crucial for strengthening network security. Improving IDS performance requires advanced techniques for handling intrusion detection data, with machine learning playing a key role. However, machine learning methods often face difficulties in detecting complex attack patterns and are prone to generating a high number of false positives, especially when dealing with unknown attacks. To overcome these issues, enhancing IDS capabilities with knowledge graphs is essential. Knowledge graphs have proven to be valuable tools for modeling and analyzing complex interactions within security data. This article proposes an approach that combines knowledge graphs with machine learning and deep learning. By harnessing the semantic modeling, querying, and reasoning capabilities of knowledge graphs, this integration aims to address the challenges of intelligent detection and decision-making in cybersecurity. © 2024 IEEE.

关键词： network intrusion

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Using Machine Learning to control Congestion in SDN: A Review

Using Machine Learning to Control Congestion in SDN: A Revie...

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International conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023

作者： Yassin, Tabarak Ali, Omar Department of Information and Communication Engineering Al-Khwarizmi College of Engineering University of Baghdad Baghdad Iraq

ISBN: (纸本)9783031567278

Congestion is a major issue in networks, as it decreases efficiency and wastes bandwidth. While the basic operations of TCP remain the same, there are different flavors of TCP developed for specific network environments that help control congestion by updating window size and data transmission. Software-defined networking (SDN) can provide centralized control for network traffic, and the amount of data received by SDN controllers is huge. This huge data can be used as an input dataset for machine learning algorithms to extract a lot of information that helps improve network performance. To process this data, machine learning (ML) has been suggested to improve network performance and reinforcement learning (RL) to predict congestion. This article reviews recent ML algorithms for congestion control in SDNs, starting with a brief overview of SDN, ML, and congestion control and then reviewing the recent works that apply ML to control congestion. Based on this comprehensive review, it has been concluded that the RL actor-critic algorithm is the most efficient approach to prevent congestion in SDN networks. In addition, the ML random forest algorithm has successfully classified flow types and detected the flow that may cause congestion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Reinforcement learning

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Can graph neural network-based detection mitigate the impact of hardware imperfections?

Can graph neural network-based detection mitigate the impact...

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International Balkan conference on Communications and networking (BalkanCom)

作者： Mitsiou, Lamprini Trevlakis, Stylianos Tsiolas, Argiris Vergados, Dimitrios J. Michalas, Angelos Boulogeorgos, Alexandros-Apostolos A. InnoCube PC Res & Dev Dept Thessaloniki 55535 Greece Univ Western Macedonia Dept Elect & Comp Engn Kozani 50100 Greece

ISBN: (纸本)9798350339109

Until recently, researchers used machine learning methods to compensate for hardware imperfections at the symbol level, indicating that optimum radio-frequency transceiver performance is possible. Nevertheless, such approaches neglect the error correcting codes used in wireless networks, which inspires machine learning (ML)-approaches that learn and minimise hardware imperfections at the bit level. In the present work, we evaluate a graph neural network (GNN)-based intelligent detector's in-phase and quadrature imbalance (IQI) mitigation capabilities. We focus on a high-frequency, high-directional wireless system where IQI affects both the transmitter (TX) and the receiver (RX). The TX uses a GNN-based decoder, whilst the RX uses a linear error correcting algorithm. The bit error rate (BER) is computed using appropriate Monte Carlo simulations to quantify performance. Finally, the outcomes are compared to both traditional systems using conventional detectors and wireless systems using belief propagation based detectors. Due to the utilization of graph neural networks, the proposed algorithm is highly scalable with few training parameters and is able to adapt to various code parameters.

关键词： Belief propagation bit error rate graph neural networks hardware imperfection mitigation in-phase and quadrature imbalance machine learning

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Construction of Security control and Protection of Internal and External networks of Communication Terminals Under the Heterogeneous network Environment

Construction of Security Control and Protection of Internal ...

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International conference on Artificial Intelligence and Communication Technologies, ICAICT 2023

作者： Zhang, Jian Zeng, Ying Li, Bo Li, Xingnan Shi, Zhan Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd. Guangzhou510000 China

ISBN: (纸本)9789819969555

The construction of network security control and protection system of internal and external communication terminals under the heterogeneous network environment can promote the research and development of power network security technology, promote the information construction and intelligent upgrading of the power grid, and improve the overall operation level and competitiveness of the power grid. Based on this, this paper proposes the construction of security control and protection of communication terminals under the heterogeneous network environment of the power grid. By analyzing the condition of grid heterogeneous network environment, the communication terminal network security control system to build design, build the network isolation, certification and authorization, traffic monitoring, and security reinforcement, in order to improve the network heterogeneous network environment communication terminal network security control efforts, comprehensive guard against all kinds of network attacks or malicious software intrusion, improve the overall security performance of power grid system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： network security

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