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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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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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A semantic model for risk management in distributed agile software development environment

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International Journal of System Assurance engineering and Management 2025年 1-36页

作者： Jugdoyal, Mokshda Nagowah, Soulakshmee D. Software and Information Systems Department Faculty of Information Communication and Digital Technologies University of Mauritius Reduit Mauritius

software quality and software risk management are tightly intertwined. software risk management refers to the management of risks that may arise during the development of software-intensive systems. These risks may impact on the schedule, the software quality, or both. Risk management in a dispersed, agile software development environment is a challenge for big software firms. Teams having different characteristics and levels of experience are spread throughout various regions or countries. Thus, the process of risk management takes longer and is more challenging as those teams grow. Semantic models in the form of ontologies have been widely used in the field of software engineering and even software risk management to model the data for a certain domain, encourage knowledge sharing and reuse, and improve semantic interoperability. This paper examines existing risk management ontologies in the Distributed Agile software Development (DASD). It additionally proposes an ontology entitled RiskDASD for the risk management process in the DASD environment in an attempt to solve certain issues and challenges in existing literature. By providing a proper and shareable representation of data among distributed teams on software risk management, the proposed ontology aims to improve on software quality. The proposed ontology reuses concepts from pre-existing ontologies. RiskDASD has been evaluated using tools and metrics and by domain experts from industry. © The Author(s) under exclusive licence to The Society for Reliability engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.

关键词： Risk management

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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.

关键词：

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Demand side management with wireless channel impact in IoT-enabled smart grid system

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Digital Communications and Networks 2025年第2期11卷 493-504页

作者： Md.Farhad Hossain Kumudu S.Munasinghe Nishant Jagannath Khandakar Tanvir Ahmed Md.Nabid Hasan Ibrahim Elgendi Abbas Jamalipour Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology School of Information Technology and Systems University of Canberra School of Electrical and Information Engineering The University of Sydney

Demand Side Management(DSM) is a vital issue in smart grids, given the time-varying user demand for electricity and power generation cost over a day. On the other hand, wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid. The design of any DSM system using a wireless network must consider the wireless link impairments, which is missing in existing literature. In this paper, we propose a DSM system using a Real-Time Pricing(RTP) mechanism and a wireless Neighborhood Area Network(NAN) with data transfer uncertainty. A Zigbee-based Internet of Things(IoT) model is considered for the communication infrastructure of the NAN. A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link. The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users, decision-makers, and energy providers. A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices. Simulation results indicate that the proposed system benefits users and energy providers. Furthermore, experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN, which can substantially impact the performance of the proposed DSM system. Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price, user welfare, and provider welfare.

关键词： Smart grid Real time pricing Demand side management Wireless communications Zigbee

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Leveraging Geographic information systems and Remote Sensing for Enhanced Water Resource Management in Kazakhstan 4

Leveraging Geographic Information Systems and Remote Sensing...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Hamada, Mohamed Ahmed Tolkyn, Orden Hamada, Gehad M. International It University Information Systems Department Almaty Kazakhstan International It University Computer Systems and Software Engineering Department Almaty Kazakhstan

ISBN: (纸本)9798350387490

Kazakhstan's deputy member is vast, and is one of sparse population, scattering and creating an overwhelming challenge to map and assess the water resources. Most of the waters are in remote and poorly equipped areas, making their monitoring by conventional means arduous. This includes costly and inefficient monitoring that results in a blurred condition of the water resources. Such a predicament stands in the way of effective management and decision-making regarding water resources. Therefore, we used Sentinel-3 satellite images to assess the spatiotemporal dynamics of water resources in Kazakhstan and obtained the results through GIS analysis. The main case of the region for case studies reflecting problems in the management of water resources in the country is the northern part of the Aral Sea. It presents a study characterized by in-depth data analysis and mapping that provided insight into the spatial trends of water resources, their variability, and potential areas of concern. The present research found that GIS and remote sensing technologies have emerged as a strong water resource observation platform. They show promise in the resource sector decision support. The development thus justifies the integration of these technology tools within the water resource management practices to ensure sustainable water use in Kazakhstan. The publication has equally availed a model for similarly water-stressed regions regarding technology-driven solutions in matters of environmental sustainability and resource management. © 2024 IEEE.

关键词： information management

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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Training Tacotron 2 for Text-to-Speech Synthesis in Kreole Language: Methodology and Audio Processing 4

Training Tacotron 2 for Text-to-Speech Synthesis in Kreole L...

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4th International Conference on Next Generation Computing Applications, NextComp 2024

作者： Ujoodha, Kousraj Baby, Gobin Rahimbux Sahib, Nuzhah Gooda University of Mauritius Department of Software and Information Systems Mauritius

ISBN: (纸本)9798331517403

This paper presents a methodology for training Tacotron 2 for text-to-speech synthesis in the Kreole language, aiming to improve access to textual information for visually impaired individuals. This paper discusses Tacotron 2's architecture, reviews relevant literature, and presents experimental implementations. By synthesizing current research trends serves as a valuable resource for TTS advancement. We trained the Tacotron 2 model to map input text sequences to Mel-spectrograms representing speech segments using a dataset of 25 audio recordings matched to their corresponding text transcripts. Segmenting audio data, using Google Colab for model training, and using HiFi-GAN as a vocoder for audio synthesis were all parts of the training process. The completeness, repeatability, and clarity of the synthesized speech were assessed in the listening assessments. Tacotron 2 performed admirably, generating intelligible sounds for 13 of 25 words despite the small sample. This study addresses the future and focuses Tacotron 2's promise in low-resource language environments. © 2024 IEEE.

关键词： Audio recordings

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Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

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Computers, Materials & Continua 2024年第7期80卷 341-359页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Daniyar Sultan Nurzhan Mukazhanov Madina Suleimenova Maigul Zhekambayeva Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050040Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050040Kazakhstan Department of Software Engineering Satbayev UniversityAlmaty050013Kazakhstan

This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart *** escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and *** model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG *** integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia *** on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.

关键词： CNN BiGRU ensemble deep learning ECG arrhythmia heart disease

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