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Cybersecurity 2023年第4期6卷 144-162页

作者： Yisroel Mirsky Ben-Gurion University Department of Software and Information Systems EngineeringBeershebaIsrael

Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single *** the past,it has been shown how an attacker can fool these models by placing an adversarial patch within a ***,these patches must be placed in the target location and do not explicitly alter the semantics elsewhere in the *** this paper,we introduce a new type of adversarial patch which alters a model’s perception of an image’s *** patches can be placed anywhere within an image to change the classification or semantics of locations far from the *** call this new class of adversarial examples‘remote adversarial patches’(RAP).We implement our own RAP called IPatch and perform an in-depth analysis on without pixel clipping on image segmentation RAP attacks using five state-of-the-art architectures with eight different encoders on the CamVid street view ***,we demonstrate that the attack can be extended to object recognition models with preliminary results on the popular YOLOv3 *** found that the patch can change the classification of a remote target region with a success rate of up to 93%on average.

关键词： patch image hundreds

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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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Echo State Network With Probabilistic Regularization for Time Series Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1743-1753页

作者： Xiufang Chen Mei Liu Shuai Li the Engineering Research Center of Open Source Software and Real-Time Systems Ministry of EducationSchool of Information Science and EngineeringLanzhou UniversityLanzhou 730000China IEEE

Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational ***,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world *** work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness ***,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the ***,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical ***,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed *** source code is publicly available at https://***/LongJinlab/probabilistic-regularized-echo-state-network.

关键词： Echo state network(ESN) noise probabilistic regularization robustness

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IoT device identification based on network communication analysis using deep learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9113-9129页

作者： Kotak, Jaidip Elovici, Yuval Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization’s network has also increased due to the bring your own device (BYOD) policy which permits employees to bring IoT devices onto the premises and attach them to the organization’s network. To tackle this threat and protect their networks, organizations generally implement security policies in which only white-listed IoT devices are allowed on the organization’s network. To monitor compliance with such policies, it has become essential to distinguish IoT devices permitted within an organization’s network from non-white-listed (unknown) IoT devices. In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network. In contrast to existing methods, the proposed approach does not require complex feature engineering of the network communication, because the ’communication behavior’ of IoT devices is represented as small images which are generated from the device’s network communication payload. The proposed approach is applicable for any IoT device, regardless of the protocol used for communication. As our approach relies on the network communication payload, it is also applicable for the IoT devices behind a network address translation (NAT) enabled router. In this study, we trained various classifiers on a publicly accessible dataset to identify IoT devices in different scenarios, including the identification of known and unknown IoT devices, achieving over 99% overall average detection accuracy. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms 38

DeSparsify: Adversarial Attack Against Token Sparsification ...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Yehezkel, Oryan Zolfi, Alon Baras, Amit Elovici, Yuval Shabtai, Asaf Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

Vision transformers have contributed greatly to advancements in the computer vision domain, demonstrating state-of-the-art performance in diverse tasks (e.g., image classification, object detection). However, their high computational requirements grow quadratically with the number of tokens used. Token sparsification mechanisms have been proposed to address this issue. These mechanisms employ an input-dependent strategy, in which uninformative tokens are discarded from the computation pipeline, improving the model's efficiency. However, their dynamism and average-case assumption makes them vulnerable to a new threat vector - carefully crafted adversarial examples capable of fooling the sparsification mechanism, resulting in worst-case performance. In this paper, we present DeSparsify, an attack targeting the availability of vision transformers that use token sparsification mechanisms. The attack aims to exhaust the operating system's resources, while maintaining its stealthiness. Our evaluation demonstrates the attack's effectiveness on three token sparsification mechanisms and examines the attack's transferability between them and its effect on the GPU resources. To mitigate the impact of the attack, we propose various countermeasures. The source code is available online. © 2024 Neural information processing systems foundation. All rights reserved.

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A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

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Journal of Modern Power systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang Li School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems Engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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Towards A software Factory for Developing the Chatbots in Smart Tourism Mobile Applications 14

Towards A Software Factory for Developing the Chatbots in Sm...

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14th International Conference on Emerging Ubiquitous systems and Pervasive Networks / 13th International Conference on Current and Future Trends of information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Benaddi, Lamya Ouaddi, Charaf Jakimi, Abdeslam Ouchao, Brahim Software Engineering and Information Systems Engineering Team FST Errachidia University of Moulay Ismail Meknes50050 Morocco

Today, the tourism industry is significantly impacted by mobile applications leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) to enhance tourists' experiences before, during, and after their visits. This technological convergence has given rise to Smart Tourism Destinations (STDs). However, efficiently integrating these functionalities into mobile apps poses a major challenge, leading to the emergence of chatbots. Companies like IBM, Google, Microsoft, and Amazon offer tools such as Watson, Bot Framework, Dialogflow, and Amazon Lex for their development. Nevertheless, creating chatbots remains intricate, demanding expertise in software development and AI, along with associated costs related to NLP service providers. Additionally, employing an appropriate modeling language is crucial for designing a chatbot. At this juncture, the concepts of software factories and Domain-Specific Languages (DSLs) become indispensable. These innovative approaches provide solutions for addressing these complex challenges. software factories automate the development process, expediting chatbot creation while ensuring consistency. Simultaneously, DSLs furnish tools for accurately modeling and articulating the specific requirements of smart tourism, simplifying the development of tailor-made chatbots suited to this domain. This article introduces a model-driven approach for a DSL aimed at offering an abstract, high-level representation of the various aspects of chatbots within the context of an intelligent tourism mobile application. © 2024 Elsevier B.V.. All rights reserved.

关键词： Problem oriented languages

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Architecture, Tools, and DSLs for Developing Conversational Agents: An Overview 14

Architecture, Tools, and DSLs for Developing Conversational ...

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作者： Ouaddi, Charaf Benaddi, Lamya Jakimi, Abdeslam Software Engineering and Information Systems Engineering Team FST Errachidia University of Moulay Ismail Meknes50050 Morocco

Conversational agents (CA) are software programs that can converse with users using natural language. They are now widely used in various domains, such as tourism, healthcare, and others, to perform tasks and provide permanent assistance to users by interacting with them in natural language. The development of such applications is a task that requires expertise in several fields, such as software engineering, machine learning, deep learning, and natural language processing (NLP). However, several platforms and frameworks on the market facilitate the building of CA, such as Dialogflow, Rasa, and others. Recently, several research studies have proposed solutions to reduce the workload of developers and designers by offering their model-driven development approaches using domain-specific languages (DSLs), which facilitate the automation of the development of CA. This work aims to provide an Overview of CA to identify and describe their architecture and the details of its key components. and discuss the tools and technologies for their development. At the same time, discover the research topics that focus on using DSLs for model-driven development to automate and speed up the creation of these agents and discover approaches and technologies employed to implement each of these DSLs. © 2024 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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A Graphical DSL for Accelerating Chatbot Development 7

A Graphical DSL for Accelerating Chatbot Development

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Ouaddi, Charaf Benaddi, Lamya Naimi, Lahbib Bouziane, El Mahi Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Sciences and Technologies Errachidia Morocco

ISBN: (纸本)9798350367027

The development of chatbots utilizing artificial intelligence (AI) techniques represents a significant advancement in Natural Language Processing (NLP). Numerous studies employ deep learning and NLP methodologies to construct sophisticated chatbot systems. Additionally, developers and companies often utilize APIs provided by intent recognition services like Dialogflow and Amazon Lex to easily create chatbots using graphical forms, which enhance chatbot functionality. However, these APIs have limitations, such as potential dependency on specific NLP service providers and associated high costs. To address these limitations, the proposed work addresses critical gaps in chatbot development tools by constructing a Domain-Specific Language (DSL) for chatbot development. This DSL aims to simplify the chatbot development process and reduce costs. Specifically, the paper introduces a graphical DSL designed to accelerate chatbot development. We use code generation templates to easily generate the source code for chatbots of the Rasa framework, simplifying the development process and reducing the time spent on manual coding. © 2024 IEEE.

关键词： Bot (Internet)

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Use Cases and Scenarios engineering in Smart software Application Development Services 7

Use Cases and Scenarios Engineering in Smart Software Applic...

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7th International Conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Bouziane, El Mahi Naimi, Lahbib Ouaddi, Charaf Benaddi, Lamya Jakimi, Abdeslam Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Sciences and Technologies Errachidia Morocco

ISBN: (纸本)9798350367027

Unified Modeling Language (UML) is a versatile tool for specifying, visualizing, and documenting software systems through diagrams. In the early stages of development, addressing design issues is critical to improving software quality and reducing costs. Use case modeling and UML scenario engineering are key techniques in requirements elicitation and specification development. This paper presents best practices for applying these techniques, highlighting their role in constructing accurate sequence diagrams and integrating formal specifications with user interface prototyping. Additionally, it explores extending scenarios to manage complex requirements like time and security constraints and offers a framework for visualizing business interactions in B2B environments to improve communication among stakeholders. © 2024 IEEE.

关键词： Requirements engineering

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