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arXiv 2025年

作者： Chen, Cheng Tian, Geng Qin, Shaoxiang Geng, Dingyang Zhan, Dongxue Yang, Jinqiu Vidal, David Wang, Liangzhu Concordia University Department of Building Civil and Environmental Engineering Department of Computer Science and Software Engineering Montreal Canada Polytechnique Montreal Department of Mechanical Engineering Montreal Canada

Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow fields under different wind directions and urban layouts. In this study, we investigate the effectiveness of the Fourier Neural Operator (FNO) model in predicting urban wind conditions under different wind directions and urban layouts. By training the model on velocity data from large eddy simulation data, we evaluate the performance of the model under different urban configurations and wind conditions. The results show that the FNO model can provide accurate predictions while significantly reducing the computational time by 99%. Our innovative approach of dividing the wind field into smaller spatial blocks for training improves the ability of the FNO model to capture wind frequency features effectively. The SDF data also provides important spatial building information, enhancing the model’s ability to recognize physical boundaries and generate more realistic predictions. The proposed FNO approach enhances the AI model’s generalizability for different wind directions and urban layouts. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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Adversarial Threats and Defense Mechanisms in Machine Learning-Based SQL Injection Detection: A Security Analysis

Adversarial Threats and Defense Mechanisms in Machine Learni...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Ranwa Al Mallah Alejandro Quintero Electrical and Computer Engineering Royal Military College of Canada Kingston Canada Polytechnique Montreal Computer and Software Engineering Québec Canada

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

QL injection (SQLi) is a type of cyber attack where malicious code is inserted into a SQL query through an input field in a web application. This exploit targets vulnerabilities in the application’s software that allows unsanitized or improperly validated user inputs to be executed as part of a database query. As a result, an attacker can gain unauthorized access to the database, retrieve, modify, or delete data, bypass authentication mechanisms, or perform other malicious actions. In Artificial Intelligence (AI), Machine Learning (ML) techniques are used to automatically detect SQL injections before they reach the web application. However, machine learning techniques are themselves vulnerable to attacks. We conduct an in-depth security analysis of a highly realistic threat model, experimentally demonstrating the effectiveness of poisoning and evasion attacks on a machine learning-based SQL injection detector. We then propose a defense mechanism tailored to the ML algorithm, and designed to protect the system against such attacks. This research highlights the consequences of inadequate management of AI technology in this field.

关键词： Training Threat modeling Databases Perturbation methods Prevention and mitigation Neural networks Machine learning SQL injection Malware Security

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Hierarchies define the scalability of robot swarms

arXiv

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arXiv 2024年

作者： Varadharajan, Vivek Shankar Soma, Karthik Dyanatkar, Sepand Lajoie, Pierre-Yves Beltrame, Giovanni Department of Computer and Software Engineering Polytechnique Montreal Canada Department of Computer Science University of Cambridge United Kingdom

The emerging behaviors of swarms have fascinated scientists and gathered significant interest in the field of robotics. Traditionally, swarms are viewed as egalitarian, with robots sharing identical roles and capabilities. However, recent findings highlight the importance of hierarchy for deploying robot swarms more effectively in diverse scenarios. Despite nature’s preference for hierarchies, the robotics field has clung to the egalitarian model, partly due to a lack of empirical evidence for the conditions favoring hierarchies. Our research demonstrates that while egalitarian swarms excel in environments proportionate to their collective sensing abilities, they struggle in larger or more complex settings. Hierarchical swarms, conversely, extend their sensing reach efficiently, proving successful in larger, more unstructured environments with fewer resources. We validated these concepts through simulations and physical robot experiments, using a complex radiation cleanup task. This study paves the way for developing adaptable, hierarchical swarm systems applicable in areas like planetary exploration and autonomous vehicles. Moreover, these insights could deepen our understanding of hierarchical structures in biological organisms. © 2024, CC BY-SA.

关键词： Hierarchical systems

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Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning

Performance Optimization of Energy-Harvesting Underlay Cogni...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Deemah H. Tashman Soumaya Cherkaoui Walaa Hamouda Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada Department of Electrical and Computer Engineering Concordia University Montreal Canada

In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) access the licensed band within underlay mode. In addition, the SU transmitter is assumed to be an energy-constrained device that requires harvesting energy in order to transmit signals to their intended destination. Therefore, we propose that there are two main sources of energy; the interference of PUs’ transmissions and ambient radio frequency (RF) sources. The SU will select whether to gather energy from PUs or only from ambient sources based on a predetermined threshold. The process of energy harvesting from the PUs’ messages is accomplished via the time switching approach. In addition, based on a deep Q-network (DQN) approach, the SU transmitter determines whether to collect energy or transmit messages during each time slot as well as selects the suitable transmission power in order to maximize its average data rate. Our approach outperforms a baseline strategy and converges, as shown by our findings.

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The Bifurcation Method: White-Box Observation Perturbation Attacks on Reinforcement Learning Agents on a Cyber Physical System

The Bifurcation Method: White-Box Observation Perturbation A...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： KIERNAN BRODA-MILIAN HANANE DAGDOUGUI RANWA AL MALLAH Royal Military College of Canada Electrical and Computer Engineering Kingston Canada Polytechnique Montreal Computer and Software Engineering Quebec Canada

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Components of cyber physical systems, which affect real-world processes, are often exposed to the internet. Replacing conventional control methods with Deep Reinforcement Learning (DRL) in energy systems is an active area of research, as these systems become increasingly complex with the advent of renewable energy sources and the desire to improve their efficiency. Artificial Neural Networks (ANN) are vulnerable to specific perturbations of their inputs or features, called adversarial examples. These perturbations are difficult to detect when properly regularized, but have significant effects on the ANN's output. Because DRL uses ANN to map optimal actions to observations, they are similarly vulnerable to adversarial examples. This work proposes a novel attack technique for continuous control using Group Difference Logits loss with a bifurcation layer. By combining aspects of targeted and untargeted attacks, the attack significantly increases the impact compared to an untargeted attack, with drastically smaller distortions than an optimally targeted attack. We demonstrate the impacts of powerful gradient-based attacks in a realistic smart energy environment, show how the impacts change with different DRL agents and training procedures, and use statistical and time-series analysis to evaluate attacks' stealth. The results show that adversarial attacks can have significant impacts on DRL controllers, and constraining an attack's perturbations makes it difficult to detect. However, certain DRL architectures are far more robust, and robust training methods can further reduce the impact.

关键词： Training Renewable energy sources Perturbation methods Time series analysis Artificial neural networks Bifurcation Deep reinforcement learning Distortion Robustness Glass box

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Swarm-SLAM: Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems

arXiv

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arXiv 2023年

作者： Lajoie, Pierre-Yves Beltrame, Giovanni Department of Computer and Software Engineering Polytechnique Montréal Montreal Canada

Collaborative Simultaneous Localization And Mapping (C-SLAM) is a vital component for successful multirobot operations in environments without an external positioning system, such as indoors, underground or underwater. In this paper, we introduce Swarm-SLAM, an open-source C-SLAM system that is designed to be scalable, flexible, decentralized, and sparse, which are all key properties in swarm robotics. Our system supports lidar, stereo, and RGB-D sensing, and it includes a novel inter-robot loop closure prioritization technique that reduces communication and accelerates convergence. We evaluated our ROS 2 implementation on five different datasets, and in a real-world experiment with three robots communicating through an ad-hoc network. Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

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A Deep Intrusion Detection System in Lambda Architecture Based on Edge Cloud Computing for IoT 4

A Deep Intrusion Detection System in Lambda Architecture Bas...

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Alghamdi, Rubayyi Bellaiche, Martine Ecole Polytechnique de Montreal Computer and Software Engineering Department Montreal Canada

ISBN: (纸本)9780738131702

IoT devices enable a massive amount of data to be aggregated and analyzed for anomaly detection. The nature of heterogeneous devices introduces the challenge of collecting and handling these massive datasets to perform data analyses to discover cyber attacks in near real-time. However, the traditional IDS cannot deal with such a problem due to scalability limitations and insufficient storage and processing capabilities. Moreover, issues such as network bandwidth, real-time support, and security limit the power of cloud server-based IDSs. This paper presents an edge-cloud deep IDS model in Lambda architecture for IoT security to address these challenges. Our system decreases the training phase's time compared to traditional machine learning algorithms and increases the accuracy of true positive detected attacks. Furthermore, neural network layers lead deep learning to achieve better performance and flexibility compared to conventional machine learning. Our solution enables the detection of suspicious activities in real-time and allows to classify them by analyzing historical data in a batch process. © 2021 IEEE.

关键词： Network security

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Scalable Distributed Architecture for Trace Analysis

SSRN

引用

SSRN 2023年

作者： Tran, Quoc-Hao Dagenais, Michel R. Department of Computer and Software Engineering Ecole Polytechnique de Montreal MontrealQC Canada

Trace analysis is a powerful tool for troubleshooting with information from the production environment, especially with the rising complexity of software applications. Recent trends in favor of microservices and High-Performance Computing cluster architectures bring challenges in distributed tracing, both in terms of scalability and usability. Various tracing platforms like Jaeger and Prometheus were created to trace distributed systems, each of them focusing on solving a specific problem. Modern projects like Prometheus, come with a set of end-to-end tools for tracing, and offer powerful query capabilities into its trace database. However, the query & aggregation operators offered by these frameworks are tightly coupled to its ecosystem. Meanwhile, supporting external services, and developing custom operators, results in a trade-off that implies losing a large number of supported features. Our work offers a distributed tracing framework, based on existing solutions, with processing time as well as storage and memory scalability in mind. Furthermore, it provides a standard paradigm for developing new query operators with ease, without sacrificing useful features. The evaluation demonstrates that the proposed approach is feasible, flexible enough to support different trace types, and introduces a minimal overhead. This is important towards making distributed tracing more easily adopted for heterogeneous systems. © 2023, The Authors. All rights reserved.

关键词： Scalability

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A Soft Voting Classification Model for Network Traffic Prediction in VANET/V2X

A Soft Voting Classification Model for Network Traffic Predi...

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Parvin Ahmadi Doval Amiri Samuel Pierre Department of Computer and Software Engineering Mobile Computing and Networking Research Laboratory Polytechnique Montreal Montreal Canada Department of Computer and Software Engineering Mobile Computing and Networking Research Laboratory (LARIM) Polytechnique Montreal Montreal Canada

Vehicular network services in the smart cities generate enormous data by vehicular road users, which is a critical challenge. Network traffic leads to a negative impact on safety applications. AI techniques are a promising solution to address network traffic in VANETs with V2X data. In this paper, we propose a soft voting classification model, which consists of hybrid supervised machine learning algorithms to predict traffic in the network. We evaluate the prediction performance of five well-known machine learning models and the proposed model based on various classification evaluation metrics. The simulation results show that the proposed network traffic prediction model performs better than other considered machine learning models in terms of accuracy (0.94%), time consumption (12.25 seconds) and AUROC (0.907) that proves its stability.

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Physical Simulation for Multi-agent Multi-machine Tending

arXiv

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arXiv 2024年

作者： Abdalwhab, Abdalwhab Beltrame, Giovanni St-Onge, David Department of Mechanical Engineering ETS Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Canada

The manufacturing sector was recently affected by workforce shortages, a problem that automation and robotics can heavily minimize. Simultaneously, reinforcement learning (RL) offers a promising solution where robots can learn through interaction with the environment. In this work, we leveraged a simplistic robotic system to work with RL with "real" data without having to deploy large expensive robots in a manufacturing setting. A real-world tabletop arena was designed with robots that mimic the agents' behavior in the simulation. Despite the difference in dynamics and machine size, the robots were able to depict the same behavior as in the simulation. In addition, those experiments provided an initial understanding of the real deployment challenges. © 2024, CC BY.

关键词： Multi agent systems

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