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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Broda-Milian, Kiernan Dagdougui, Hanane Mallah, Ranwa Al Royal Military College of Canada Electrical and Computer Engineering Kingston Canada Polytechnique Montreal Computer and Software Engineering Quebec Canada

ISBN: (纸本)9798331528041

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. © 2024 IEEE.

关键词： Adversarial machine learning

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DLICA: Deep Learning based novel Strategy for Intelligent Channel Adaption in Wireless SDN-IoT Environment 3

DLICA: Deep Learning based novel Strategy for Intelligent Ch...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Zabeehullah Arif, Fahim Abbas, Yawar Ahmad, Shahbaz Waseem, Muhammad Computer Software Engineering Military College of Signals Rawalpindi Pakistan Computer Science University of Engineering and Technology Lahore Pakistan

ISBN: (纸本)9798350332391

software-Defined Network (SDN) is an emerging networking paradigm and provides the cost-effective scalability and flexibility required for an IoT environment. Here, wireless SDN is supposed to efficiently use limited network resources as well as provide smart traffic routing in order to deliver a bunch of IoT-generated data to the remote destination (Cloud). As, with the increasing number of IoT nodes and a huge amount of data generation, there is an immense load on SDN-IoT channels. This paper will mainly focus on appropriate channels assignment to each SDN-IoT enabled switch in order to prevent network congestion and improve network performance. Traditional fixed channel assignment algorithms in the SDN-IoT environments have failed because of large-scale and unpredictable dynamic traffic flows. In this paper, we have proposed a novel deep learning-based model called DLICA for intelligent channel assignment in wireless SDN-IoT. Moreover, a deep learning-based future traffic load prediction model has also been proposed to adaptively assign channels according to the traffic load prediction. Simulations show that proposed deep learning models achieve excellent accuracy while predicting traffic load and appropriate channel assignment. Apart from high accuracy, the proposed models also improve network performance significantly as compared to traditional channel assignment algorithms. © 2023 IEEE.

关键词： Cost effectiveness

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A Study on Identifying, Categorizing and Reporting Usability Bugs and Challenges 4

A Study on Identifying, Categorizing and Reporting Usability...

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4th International Conference on Communication Technologies, ComTech 2023

作者： Ain, Qurat Ul Rana, Tauseef Aamana Department of Computer Science Islamabad Pakistan NUST Military College of Signals Department of Computer Software Engineering Islamabad Pakistan Department of Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350337211

To assess the quality, acceptability and user experience of interactive applications, usability is one of the most integral quality attributes. However, significant number of usability bugs are being experienced by the end users of interactive applications thus creating a gap between users and technology. To bridge this gap and to overcome those usability bugs and challenges, it is significantly important to clearly identify the those usability bugs along with the gaps and challenges causing them. This research study aims to identify the usability bugs being experienced by users of interactive applications and proposes a categorization taxonomy for the identified bugs. Moreover, to reach the cause of emergence of usability bugs, the study attempts to identify and report the usability gaps and challenges in existing usability practices. Furthermore, this study performs a mapping between the identified gaps and challenges in existing practices to that of the recommendations to address the identified gaps. Index Terms - Usability, interactive applications, usability bugs, user experience, user satisfaction, usability testing, user interface. © 2023 IEEE.

关键词： User interfaces

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A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

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computer Modeling in engineering & Sciences 2023年第5期135卷 1209-1228页

作者： Chuan Wang Ruoyu Zhu Yi Jiang Weili Liu Sang-Woon Jeon Lin Sun Hua Wang College of Software Henan Normal UniversityXinxiang453007China College of Computer and Information Engineering Henan Normal UniversityXinxiang453007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou510665China Department of Military Information Engineering Hanyang UniversityAnsan15588South Korea College of Engineering and Science Institute for Sustainable Industries and Liveable CitiesVictoria UniversityMelbourneVIC 8001Australia

The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to *** paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP *** work is novel and contributes to three aspects:problemmodel,optimization framework,and ***,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over ***,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic *** framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous *** way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme *** the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational ***,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search *** evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic *** experimental results show that ACO with 2-opt can solve the DTSPs effectively.

关键词： Dynamic traveling salesman problem(DTSP) offline optimization and online application ant colony optimization(ACO) two-optimization(2-opt)strategy

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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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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

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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An software Defined Networking (SDN) Enhanced Edge Computing Framework for Internet of Healthcare Things (IoHT)

An Software Defined Networking (SDN) Enhanced Edge Computing...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Anh, Dang Van Chehri, Abdellah Quy, Nguyen Minh Hue, Chu Thi Minh Nguyen, Dinh C. Quy, Vu Khanh Hung Yen University of Technology and Education Faculty of Information Technology Hungyen Viet Nam Royal Military College of Canada Department of Mathematics and Computer Science Kingston Canada Fpt University Faculty of Software Engineering Hanoi Viet Nam The University of Alabama Department of Electrical and Computer Engineering HuntsvilleAL United States

ISBN: (纸本)9798350351255

The rapid proliferation of intelligent Internet of Health Things (IoHT) applications within the context of the COVID-19 pandemic has exerted significant strain on the backhaul network infrastructure. This paper aims to introduce a novel framework that leverages software-defined networking (SDN) to enhance edge computing capabilities. This framework will expect to facilitate dynamic and adaptable communication between edge and cloud servers, specifically designed to support real-time Internet of Health Things applications. Through the establishment of a connection between servers and the software-Defined Networking controller, the system is expected to facilitate load balancing, network optimization, and the utilization of resources in an efficient manner. This, in turn, enables the provision of real-time healthcare services. Ultimately, the efficacy of the suggested framework is assessed by analyzing its impact on service response time. The research results indicate that the proposed framework significantly benefits IoHT systems' service response times across various workloads and traffic. © 2024 IEEE.

关键词： Electronic health record

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DLICA: Deep Learning based novel Strategy for Intelligent Channel Adaption in Wireless SDN-IoT Environment

DLICA: Deep Learning based novel Strategy for Intelligent Ch...

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International Conference on Communication, Computing and Digital Systems (C-CODE)

作者： Zabeehullah Fahim Arif Yawar Abbas Shahbaz Ahmad Muhammad Waseem Computer Software Engineering Military College of Signals Rawalpindi Pakistan Computer Science University of Engineering and Technology Lahore Pakistan

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MORPSO_ECD+ELM: A Unified Framework for Gene Selection and Cancer Classification

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4571-4584页

作者： Sumet Mehta Fei Han Qinghua Ling Muhammad Sohail Arfan Nagra School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Computer Science Jiangsu University of Science and Technology Zhenjiang China Department of Computer Software Engineering Military College of Signals NUST Islamabad Pakistan School of Computer Science Lahore Garrison University Lahore Pakistan

Gene selection and cancer classification are inherently multi-objective tasks that require balancing competing objectives, such as maximizing classification accuracy while minimizing irrelevant or redundant genes. Existing methods often optimize a single objective or treat gene selection and classification independently, limiting their overall effectiveness. This study proposes a unified framework, MORPSO_ECD+ELM, which formulates gene selection and classification as a multimodal multi-objective optimization problem (MMOP) to optimize both objectives simultaneously. The framework introduces two key innovations: (1) an enhanced crowding distance (ECD) metric to improve diversity preservation and (2) an advanced multi-objective particle swarm optimization variant (MORPSO_ECD) that incorporates ECD and ring topography to effectively explore the MMOP solution space. Integrated with the Extreme Learning Machine (ELM), this framework achieves robust and efficient cancer classification. Extensive experimental validations demonstrate that the proposed approach achieves high classification accuracy while identifying biologically meaningful gene subsets, providing a powerful solution to bridge the gap between gene selection and cancer classification.

关键词： Cancer Accuracy Optimization Particle swarm optimization Measurement Training Surfaces Extreme learning machines Functional magnetic resonance imaging Electronic mail

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Programmer Experience: - An HCI Prespective 3

Programmer Experience: - An HCI Prespective

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3rd International Conference on Communication Technologies, ComTech 2021

作者： Khan, Umar Farooq Rana, Tauseef NUST Military College of Signals Computer Software Engineering Department Rawalpindi Pakistan

ISBN: (纸本)9781665419017

User experience (abbreviated as UX) is how a user feels when interacting with a particular system. Not only does it include user's direct interactions with the system, but also how it helps the user in the overall satisfactory completion of a particular task. software programmers are also users of a specific systems, and several types of artifacts related to software development lifecycle. We performed a systematic mapping about Programmer eXperience (PX). In this paper we considered the programmers as a type of users of an IDE and different programming and system related artifacts. After studying different research articles about programmer experiences related to different programming languages, tools and environments, we analyzed that most of the currently available literature focuses on PX from a computer software engineering perspective, also it has been felt that there is an increasing interest in the PX topic, but still the main concept is not clearly defined and understood. Thus, we have tried to focus our main research work on programmer's experience from a Human-computer-Interaction (HCI) perspective. Most of current literature and research work focuses on the usability and PX aspects focusing on four topics: (i) programming languages, (ii) programmers' interaction with integrated development environment, (iii) application programming interfaces, and (iv) programmers' behavior. This represents a research opportunity for this systematic review and others that can be performed. © 2021 IEEE.

关键词： Life cycle

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