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3rd International Conference on Applied Mathematics and Computer Science, ICAMCS 2023

作者： Petriashvili, Lily Modebadze, Zurab Lominadze, Tamar Kiknadze, Mzia Otkhozoria, Nona Zhvania, Taliko Georgian Technical University Software Engineering Tbilisi Georgia Georgian Technical University Information Systems Tbilisi Georgia Georgian Technical University Information Technology Tbilisi Georgia Georgian Technical University Computer Engineering Tbilisi Georgia Georgian Technical University Microprocessors and Measurement Systems Tbilisi Georgia

ISBN: (纸本)9798350324266

Current global events, such as the COVID-19 pandemic and the Russian-Ukrainian war, have had a notable impact on the rise in the volume of freight transportation on the Georgian railway. Consequently, the efficient utilization of railway systems, along with monitoring the state of operational processes and technical equipment, plays a crucial role in ensuring smooth and uninterrupted transportation. Key factors to consider include speed, reliability, and the quality of shipments. Railway transportation is progressing worldwide, and effective management is undeniably a priority for our *** essential element for identifying and managing freight wagons is the Arabic numerals printed on them, which indicate their main characteristics and ownership. According to internationally recognized agreements, foreign wagons, as well as those owned or temporarily used by railway administrations, must be promptly returned to their country of dispatch. Within the first 24 hours of crossing the border, "inventory"freight wagons are subject to an average daily charge of 20 Swiss francs, while owned wagons incur an average daily rental fee of US$30. This circumstance, along with rolling stock management, presents one of the critical ***, hundreds of railway employees are involved in identifying the rolling stock in our country. These workers walk along the stations where the parked rolling stock is located. Once they visually recognize the freight car numbers, they relay the information to the relevant station operators via transmitters. Initially, these operators manually record the numbers in special journals and then enter the data into a unified electronic registry system. As a result, railway freight shipments require significant human and time resources, with a high probability of error. This situation considerably slows down the speed of railway service and increases its costs. © 2023 IEEE.

关键词： Radio frequency identification (RFID)

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Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

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Computer Modeling in engineering & Sciences 2024年第10期141卷 689-711页

作者： Khushal Das Fazeel Abid Jawad Rasheed Kamlish Tunc Asuroglu Shtwai Alsubai Safeeullah Soomro Department of Computer Engineering Modeling Electronics and Systems EngineeringUniversity of CalabriaRende Cosenza87036Italy Department of Information Systems University of Management and TechnologyLahore54770Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Istanbul Nisantasi UniversityIstanbul34398Turkey Department of Computer Science COMSATS University IslamabadLahore CampusLahore54700Pakistan Faculty of Medicine and Health Technology Tampere UniversityTampere33720Finland Department of Computer Science College of Computer Engineering and Sciences in Al-KharjPrince Sattam Bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Second Department of Computer Science College of Engineering and ComputingGeorge Mason UniversityFairfaxVA 4418USA

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual *** works based on rich source language have been proposed;however,the work using poor resource language is still *** other SLs,the visuals of the Urdu Language are *** study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this *** existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited *** conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and *** enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise *** analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of ***,our model exhibited superior performance in Precision,Recall,and F1-score *** work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

关键词： Convolutional neural networks Pakistan sign language visual language

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Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex Tasks

arXiv

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arXiv 2025年

作者： Benyamin, Yarin Mordoch, Argaman Shperberg, Shahaf S. Stern, Roni Software and Information Systems Engineering Ben-Gurion University of the Negev Be’er Sheva Israel

Automated Planning algorithms require a model of the domain that specifies the preconditions and effects of each action. Obtaining such a domain model is notoriously hard. Algorithms for learning domain models exist, yet it remains unclear whether learning a domain model and planning is an effective approach for numeric planning environments, i.e., where states include discrete and numeric state variables. In this work, we explore the benefits of learning a numeric domain model and compare it with alternative model-free solutions. As a case study, we use two tasks in Minecraft, a popular sandbox game that has been used as an AI challenge. First, we consider an offline learning setting, where a set of expert trajectories are available to learn from. This is the standard setting for learning domain models. We used the Numeric Safe Action Model Learning (NSAM) algorithm to learn a numeric domain model and solve new problems with the learned domain model and a numeric planner. We call this model-based solution NSAM(+p), and compare it to several model-free Imitation Learning (IL) and Offline Reinforcement Learning (RL) algorithms. Empirical results show that some IL algorithms can learn faster to solve simple tasks, while NSAM(+p) allows solving tasks that require long-term planning and enables generalizing to solve problems in larger environments. Then, we consider an online learning setting, where learning is done by moving an agent in the environment. For this setting, we introduce the Reinforcement learning, Action Model learning, and Planning (RAMP) hybrid strategy. In RAMP, observations collected during the agent’s execution are used to simultaneously train an RL policy and learn a planning domain action model. This forms a positive feedback loop between the RL policy and the learned domain model. We demonstrate experimentally the benefits of using RAMP, showing that it finds more efficient plans and solves more problems than several RL baselines. © 2025, CC BY-NC

关键词： Adversarial machine learning

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning 15

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

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15th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2024

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering Al-Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331507589

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction. © 2024 IEEE.

关键词： Electronic health record

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RAMBO: Leaking Secrets from Air-Gap Computers by Spelling Covert Radio Signals from Computer RAM

arXiv

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arXiv 2024年

作者： Guri, Mordechai Ben-Gurion University of the Negev Department of Software and Information Systems Engineering Israel

Air-gapped systems are physically separated from external networks, including the Internet. This isolation is achieved by keeping the air-gap computers disconnected from wired or wireless networks, preventing direct or remote communication with other devices or networks. Air-gap measures may be used in sensitive environments where security and isolation are critical to prevent private and confidential information leakage. In this paper, we present an attack allowing adversaries to leak information from air-gapped computers. We show that malware on a compromised computer can generate radio signals from memory buses (RAM). Using software-generated radio signals, malware can encode sensitive information such as files, images, keylogging, biometric information, and encryption keys. With software-defined radio (SDR) hardware, and a simple off-the-shelf antenna, an attacker can intercept transmitted raw radio signals from a distance. The signals can then be decoded and translated back into binary information. We discuss the design and implementation and present related work and evaluation results. This paper presents fast modification methods to leak data from air-gapped computers at 1000 bits per second. Finally, we propose countermeasures to mitigate this out-of-band air-gap threat. Copyright © 2024, The Authors. All rights reserved.

关键词： information leakage

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A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第3期16卷 136-150页

作者： Alsarhan, Ayoub Al-Ghuwairi, Abdel-Rahman Alshdaifat, Esra’a Idhaim, Hasan alkhawaldeh, Omar Department of Information Technology Hashemite University Zarqa Jordan Software Engineering Department Hashemite University Zarqa Jordan Computer Information Systems Department Hashemite University Zarqa Jordan Hashemite University Zarqa Jordan

Cloud security vulnerabilities have recently become more prevalent around the world, posing a threat to cloud service providers’ (CSPs) ability to respond to client demands. In cloud market, the requests are announced by the client nodes to their CSP. A malicious node can alter a client’s request, resulting in the next cloud market collapse, decreased reliability, and data leaking. To identify malicious nodes in the cloud market, a novel fuzzy multiple criterion decision making scheme is suggested. Authentication test, trust level, traffic size, and node activity levels are all taken into consideration simultaneously as the major criteria for identifying malicious nodes. For each node, the CSP uses fuzzy Integral to generate a composite value based on these criteria. The malicious node is then removed from the cloud market using this composite value. The simulation results demonstrated the potential of the proposed method to prevent nodes in the cloud market from running malware or software that can be used to degrade quality of service by exhausting resources in the cloud market © 2022,International Journal of Interactive Mobile *** Rights Reserved

关键词： Intrusion detection

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Detection of Compromised Functions in a Serverless Cloud Environment

SSRN

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SSRN 2024年

作者： Lavi, Danielle Brodt, Oleg Mimran, Dudu Elovici, Yuval Shabtai, Asaf Dept. of Software and Information Systems Engineering Ben-Gurion University of the Negev

Serverless computing is an emerging cloud paradigm with serverless functions at its core. While serverless environments enable software developers to focus on developing applications without the need to actively manage the underlying runtime infrastructure, they open the door to a wide variety of security threats that can be challenging to mitigate with existing methods. Existing security solutions do not apply to all serverless architectures, since they require significant modifications to the serverless infrastructure or rely on third-party services for the collection of more detailed data. In this paper, we present an extendable serverless security threat detection model that leverages cloud providers’ native monitoring tools to detect anomalous behavior in serverless applications. Our model aims to detect compromised serverless functions by identifying post-exploitation abnormal behavior related to different types of attacks on serverless functions, and therefore, it is a last line of defense. Our approach is not tied to any specific serverless application, is agnostic to the type of threats, and is adaptable through model adjustments. To evaluate our model’s performance, we developed a serverless cybersecurity testbed in an AWS cloud environment, which includes two different serverless applications and simulates a variety of attack scenarios that cover the main security threats faced by serverless functions. Our evaluation demonstrates our model’s ability to detect all implemented attacks while maintaining a negligible false alarm rate. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms

arXiv

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arXiv 2024年

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

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STFL: Utilizing a Semi-Supervised, Transfer-Learning, Federated-Learning Approach to Detect Phishing URL Attacks

STFL: Utilizing a Semi-Supervised, Transfer-Learning, Federa...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ido Sakazi Edita Grolman Yuval Elovici Asaf Shabtai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Phishing attacks are continually changing, so machine learning detection models must be continuously updated by collecting new URL data from users without compromising their privacy. Existing approaches for the detection of phishing URLs have unrealistic assumptions: (1) representative URL datasets are available in a centralized location, (2) users’ URL entries are labeled, (3) users’ unique behavioral patterns are ignored, and (4) users’ data are identically and independently distributed (IID data). This paper presents a semi-supervised, transfer-learning (TL), federated-learning (FL) approach for detecting phishing URL attacks, a novel approach that does not hold the above assumptions. We train a bidirectional long short-term memory (Bi-LSTM) autoencoder network across multiple decentralized edge devices (using FL) containing unlabeled data samples without sharing them (the process is privacy-preserving). A centralized server collects the updated Bi-LSTM autoencoder networks from the users' devices and aggregates them into a global Bi-LSTM autoencoder network using the FedAVG algorithm. The server then performs TL in order to use the autoencoder that learns the patterns from the global Bi-LSTM autoencoder networks and induces a classification model. The method is evaluated using three benchmark datasets and compared to state-of-the-art URL phishing detection methods that utilize centralized learning (CL) and FL. Our experiments show that our proposed approach achieves higher results based on the F1 score compared to the state-of-the-art method.

关键词： Uniform resource locators Performance evaluation Data privacy Federated learning Phishing Image edge detection Neural networks

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Utilizing Organizational Patterns to Build High-Performing Remote teams 28

Utilizing Organizational Patterns to Build High-Performing R...

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Toldy, Kristián Khail, Waheedullah Sulaiman Vranic, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Helmholtz-Zentrum Berlin für Materialien Energie Gmbh Germany

ISBN: (纸本)9798400700408

In recent years, remote work has become a more popular option for companies across various industries. While remote work provides numerous benefits, such as flexibility and increased work-life balance, it also presents unique challenges that can hinder team collaboration, productivity, and overall success. In order to overcome these obstacles, it is essential to adopt effective organizational patterns that can help build high-performing remote teams. Building a high-performing remote team requires a set of strategies with some key focus points to be kept in mind such as communication, trust, goals, collaboration, and performance. For all of these, there have been an extensive research and publication of observed patterns to which we have also contributed with a new set of these four patterns: Use Common Language, Discuss Publicly, Lights, Camera, Action!, and Praise in Public, Correct in Private. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Computer programming

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