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Classification Method for Malware Detection on Android Devices

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Classification Method for Malware Detection on Android Devic...

Future Technologies Conference, FTC 2020

作者： Fournier, Arthur El Khoury, Franjieh Pierre, Samuel Department of Computer and Software Engineering Polytechnique Montreal Station Centre-Ville P.O. Box 6079 MontrealQCH3C 3A7 Canada

ISBN: (纸本)9783030630911

Due to its popularity and open-source nature, Android is the mobile platform that has been targeted the most by malware. Android allows downloading and installation of apps from other unofficial market places. This aims to steal personal information or to control the users’ devices. More specifically, malware attacks private and financial information on mobile payment applications and networks, and thus is very sensitive. In this paper, we propose an efficient malware detection model for Android devices centered on mobile payment applications. This model is based on client/server architecture to reduce the heavy computations of data on the mobile device and doing the processing remotely on the server. Our approach aims to develop an optimized algorithm based on machine learning models to extract the permissions and for better classification of the new installed applications on Android devices. The Random Forest regression algorithm with the numerical ranging from −100 (benign) to 100 (malware) gives good results and an accuracy close to 100%. Therefore, the proposed model is suitable to secure the Android devices in the mobile commerce context. © 2021, Springer Nature Switzerland AG.

关键词： Decision trees

From Assistive Devices to Manufacturing Cobot Swarms

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From Assistive Devices to Manufacturing Cobot Swarms

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Monica Li Bruno Belzile Ali Imran Lionel Birglen Giovanni Beltrame David St-Onge Department of Software and Computer Engineering Polytechnique Montreal 2900 boul. Edouard Montpetit Montreal Quebec Canada Kinova Robotics 4333 Bd de la Grande-Allée Boisbriand Quebec Canada Department of Mechanical Engineering Ecole de technologie superieure 1100 Notre-Dame W. Montreal Quebec Canada Department of Mechanical Engineering Polytechnique Montreal 2900 boul. Edouard Montpetit Montreal Quebec Canada

This paper provides an overview of the latest trends in robotics research and development, with a particular focus on applications in manufacturing and industrial settings. We highlight recent advances in robot design, including cutting-edge collaborative robot mechanics and advanced safety features, as well as exciting developments in perception and human-swarm interaction. By examining recent contributions from Kinova, a leading robotics company, we illustrate the differences between industry and academia in their approaches to developing innovative robotic systems and technologies that enhance productivity and safety in the workplace. Ultimately, this paper demonstrates the tremendous potential of robotics to revolutionize manufacturing and industrial operations, and underscores the crucial role of companies like Kinova in driving this transformation forward.

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Probabilistic Graph Modeling based Safety Classifier Algorithm for Smart Transportation

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Procedia computer Science 2024年 236卷 502-507页

作者： Najib El Karkouri Smail Tigani Rachid Saadane Abdelah Chehri Samuel Pierre Noureddine Neya Mathematics and Informatics Lab Faculty of Sciences University Ibno Toufial Kenitra Morocco Research and Development Unit Accsellium LLC Fez Morocco SIRC-LaGeS Hassania School of Public Labors 20250 Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Computer and Software Engineering Polytechnique Montreal Canada

This research introduces a novel Probabilistic Graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments.

关键词： Probabilistic Graph Numerical Computation Smart Transportation Classification Road Safety

Digital Twin and DRL-Driven Semantic Dissemination for 6G Autonomous Driving Service

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Digital Twin and DRL-Driven Semantic Dissemination for 6G Au...

IEEE Conference on Global Communications (GLOBECOM)

作者： Yihang Tao Jun Wu Xi Lin Shahid Mumtaz Soumaya Cherkaoui School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Faculty of Science and Engineering Waseda University Japan Department of Engineering Nottingham Trent University UK Department of Computer and Software Engineering Polytechnique Montreal Canada

Data dissemination is critical for 6G autonomous driving (AD) service because of the extensive demand for real-time traffic information. However, the heavier data transmission burden and more stringent requirements of AD service bring challenges for current data dissemination methods. In this paper, we first propose a novel digital twin (DT)-based semantic dissemination architecture to better support 6G AD service. Under this architecture, an energy-efficient semantic communication mechanism is developed to reduce the data dissemination burden while keeping low semantic model update costs. Meanwhile, the DT network is leveraged to disseminate semantic data in parallel with the physical vehicular networks, which alleviates the physical transmission contention and improves the dissemination efficiency. Second, we design a deep-reinforcement-learning (DRL)-driven semantic data dissemination scheme for the proposed architecture, named Proximal-policy-optimization for Digital-twin-aided Data Dissemination (PD3), which seeks the optimal DT transfer and semantic transmission scheduling strategy. Finally, experimental results show that our approach surpasses the state-of-the-art methods by 18.36% lower dissemination delay and 4.51% higher dissemination ratio on average.

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Message Flow Analysis with Complex Causal Links for Distributed ROS 2 Systems

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arXiv 2022年

作者： Bédard, Christophe Lajoie, Pierre-Yves Beltrame, Giovanni Dagenais, Michel Department of Computer Engineering and Software Engineering Polytechnique Montréal MontrealQCH3T 1J4 Canada

Distributed robotic systems rely heavily on the publish-subscribe communication paradigm and middleware frameworks that support it, such as the Robot Operating System (ROS), to efficiently implement modular computation graphs. The ROS 2 executor, a high-level task scheduler which handles ROS 2 messages, is a performance bottleneck. We extend ros2 tracing, a framework with instrumentation and tools for real-time tracing of ROS 2, with the analysis and visualization of the flow of messages across distributed ROS 2 systems. Our method detects one-to-many and many-to-many causal links between input and output messages, including indirect causal links through simple user-level annotations. We validate our method on both synthetic and real robotic systems, and demonstrate its low runtime overhead. Moreover, the underlying intermediate execution representation database can be further leveraged to extract additional metrics and high-level results. This can provide valuable timing and scheduling information to further study and improve the ROS 2 executor as well as optimize any ROS 2 system. The source code is available at: ***/christophebedard/ros2-message-flow-analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Robot programming

Multi-robot energy autonomy with wind and constrained resources

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arXiv 2021年

作者： Fouad, Hassan Beltrame, Giovanni Computer and Software Engineering Department Ecole Polytechnique de Montreal Canada

One aspect of the ever-growing need for long term autonomy of multi-robot systems, is ensuring energy sufficiency. In particular, in scenarios where charging facilities are limited, battery-powered robots need to coordinate to share access. In this work we extend previous results by considering robots that carry out a generic mission while sharing a single charging station, while being affected by air drag and wind fields. Our mission-agnostic framework based on control barrier functions (CBFs) ensures energy sufficiency (i.e., maintaining all robots above a certain voltage threshold) and proper coordination (i.e., ensuring mutually exclusive use of the available charging station). Moreover, we investigate the feasibility requirements of the system in relation to individual robots’ properties, as well as air drag and wind effects. We show simulation results that demonstrate the effectiveness of the proposed framework. © 2021, CC BY.

关键词： Multipurpose robots

Robustness Assessment of Hyperspectral Image Cnns Using Metamorphic Testing

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SSRN

SSRN 2022年

作者： Bouchoucha, Rached Ben Braiek, Houssem Khomh, Foutse Bouzidi, Sonia Zaatour, Rania Department of Computer Engineering and Software Engineering Polytechnique MontrealQCH3T 1J4 Canada Department of Computer Engineering and Software Engineering National Institute of Applied Science and Technology Tunis1080 Tunisia

Remote sensing has proven its utility in many critical domains, such as medicine, military, and ecology. Recently, we have been witnessing a surge in the adoption of deep learning (DL) techniques by the remote sensing community. DL-based classifiers, such as convolutional neural networks (CNNs), have been reported to achieve impressive predictive performances reaching 99\% of accuracy when applied to hyperspectral images (HSIs), a high-dimensional type of remote sensing data. However, these deep learners are known to be highly sensitive to even slight perturbations of their high-dimensional raw inputs. In real-world contexts, concerns can be raised about how robust they really are against corner-case scenarios. When HSI classifiers are applied in safety-critical applications, ensuring an adequate level of robustness is crucial to prevent unexpected system behaviors. Yet, there are few studies dealing with their robustness, nor are RGB-testing methods able to cover the HSI-specific challenges. This led us to propose a systematic testing method to assess the robustness of the CNNs trained to classify HSIs. First, we elaborate domain-specific metamorphic transformations that simulate naturally-occurring distortions of remote sensing HSIs. Then, we leverage metaheuristic search algorithms to optimize the fitness of synthetically-distorted inputs to stress the weaknesses of the on-testing CNN, while remaining in compliance with domain expert requirements, in order to preserve the semantic of the generated inputs. Relying on our metamorphic testing method, we assess the robustness of established and novel CNNs for HSI classification, and demonstrate their failure, on average, in 25\% of the produced test cases. Furthermore, we fine-tuned the tested CNNs on training data augmented with these failure-revealing metamorphic transformations. Results show that the fined-tuning successfully fixed at least 90\% of the CNN weaknesses, with less than 1\% of degradation in the origi

关键词： Remote sensing

AI Agents for computer Use: A Review of Instruction-based computer Control, GUI Automation, and Operator Assistants

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arXiv 2025年

作者： Sager, Pascal J. Meyer, Benjamin Yan, Peng von Wartburg-Kottler, Rebekka Etaiwi, Layan Enayati, Aref Nobel, Gabriel Abdulkadir, Ahmed Grewe, Benjamin F. Stadelmann, Thilo Centre for Artificial Intelligence Zurich University of Applied Sciences Winterthur Switzerland Institute of Neuroinformatics University of Zurich ETH Zurich Zurich Switzerland Department of Computer Engineering and Software Engineering Polytechnique Montreal MontrealQC Canada Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Winterthur Switzerland European Centre for Living Technology Venice Italy

Instruction-based computer control agents (CCAs) execute complex action sequences on personal computers or mobile devices to fulfill tasks using the same graphical user interfaces as a human user would, provided instructions in natural language. This review offers a comprehensive overview of the emerging field of instruction-based computer control, examining available agents – their taxonomy, development, and respective resources – and emphasizing the shift from manually designed, specialized agents to leveraging foundation models such as large language models (LLMs) and vision-language models (VLMs). We formalize the problem and establish a taxonomy of the field to analyze agents from three perspectives: (a) the environment perspective, analyzing computer environments;(b) the interaction perspective, describing observations spaces (e.g., screenshots, HTML) and action spaces (e.g., mouse and keyboard actions, executable code);and (c) the agent perspective, focusing on the core principle of how an agent acts and learns to act. Our framework encompasses both specialized and foundation agents, facilitating their comparative analysis and revealing how prior solutions in specialized agents, such as an environment learning step, can guide the development of more capable foundation agents. Additionally, we review current CCA datasets and CCA evaluation methods and outline the challenges to deploying such agents in a productive setting. In total, we review and classify 86 CCAs and 33 related datasets. By highlighting trends, limitations, and future research directions, this work presents a comprehensive foundation to obtain a broad understanding of the field and push its future development. © 2025, CC BY.

关键词： Taxonomies

Assessing the Security of GitHub Copilot Generated Code - A Targeted Replication Study

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arXiv 2023年

作者： Majdinasab, Vahid Bishop, Michael Joshua Rasheed, Shawn Moradidakhel, Arghavan Tahir, Amjed Khomh, Foutse Department of Computer and Software Engineering Polytechnique Montreal Canada School of Mathematical and Computational Sciences Massey University New Zealand Information & Communication Technology Group UCOL - Te Pūkenga New Zealand

AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on large corpora of code (primarily sourced from public repositories), which may contain bugs and vulnerabilities. Several concerns have been raised about the security of the code generated by these models. Recent studies have investigated security issues in AI-powered code generation tools such as GitHub Copilot and Amazon CodeWhisperer, revealing several security weaknesses in the code generated by these tools. As these tools evolve, it is expected that they will improve their security protocols to prevent the suggestion of insecure code to developers. This paper replicates the study of Pearce et al., which investigated security weaknesses in Copilot and uncovered several weaknesses in the code suggested by Copilot across diverse scenarios and languages (Python, C and Verilog). Our replication examines Copilot’s security weaknesses using newer versions of Copilot and CodeQL (the security analysis framework). The replication focused on the presence of security vulnerabilities in Python code. Our results indicate that, even with the improvements in newer versions of Copilot, the percentage of vulnerable code suggestions has reduced from 36.54% to 27.25%. Nonetheless, it remains evident that the model still suggests insecure code. © 2023, CC BY.

关键词： Network security