检索结果-内蒙古大学图书馆

10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Bouhoula, Sirine Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Department of Software and IT Engineering Montréal Canada Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350373011

Mobile Edge Computing (MEC) and Edge Robotics have recently emerged as transformative technologies, revolutionizing industries by enabling real-time processing, decision-making, and automation at the network edge. However, the dynamicity induced by the system's conditions and specifically the mobility poses a challenge for optimally deciding where to execute a given computational task. As a response, we develop an intelligent algorithm for dynamic sensor task offloading tailored to the unique requirements of MEC-enabled robotic environments. Specifically, we first introduce the environmental dynamics including a sensor task's end-to-end delay and the robots' mobility and energy consumption and provide mathematical formulations to model these dynamics. Then, we mathematically formulate the optimization problem and its MDP counterpart and we propose a Deep Reinforcement Learning (DRL)-based computational offloading strategy to jointly optimize Quality of Service (QoS) and energy consumption through robot trajectory planning and fine-grained task allocation. Through hand-picked representative simulation scenarios, we demonstrate the superiority of our proposed mechanism in enhancing the overall system performance, specifically in optimizing task execution, reducing energy consumption, and mitigating transmission delays, compared to various baseline approaches. © 2024 IEEE.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation Framework for Insider Threat Detection Using Machine Learning 2

Performance Evaluation Framework for Insider Threat Detectio...

引用

2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Abdallah, Habiba Emad-Eldin Abd-Elkader, Habiba Hamed Mohamed, Kholoud Khaled Abd-Elmoniem, Mohamed El-Assal, Nizar Walid Mohamed, Salma Mohsen Said, Samar A. Salem, Sameh A. Helwan University Faculty of Engineering Egypt Helwan University Department of Computer and Systems Engineering Egypt

ISBN: (纸本)9798350362633

Nowadays, malicious behavior identification is considered a significant and challenging issue in cybersecurity. To mitigate this problem, an effective detection system can be a promising candidate to facilitate precise and rapid detection of a malicious insider. In this paper, a performance evaluation framework for insider threat detection using machine learning is proposed. This framework employs the effects of several machine learning models to assist in identifying insider threats. The proposed framework applied XGboost, SVM, RF, KNN, MLP, and LR to detect insider threats. The machine learning framework is evaluated across the CERT r5.2 dataset. After that, the results of these models are compared with evaluation metrics to determine which model is the most effective for insider detection at different granularity levels. Furthermore, we have empirically shown that XGBoost outperforms competitive algorithms and can achieve a range precision of 95.24%-97.85%, recall of 97.34%-99.30%, F1 score of 96.79%-98.81 %, and an AUC of 97.04%-98.63% for insider detection. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Narrator identification by querying Sanad graph and utilizing the NarratorsKG on AR-Sanad 280K-v2 dataset

引用

Neural Computing and Applications 2024年第36期36卷 23169-23180页

作者： Mahmoud, Somaia Nabil, Emad Saif, Omar Torki, Marwan Department of Computer and Systems Engineering Alexandria University Alexandria Egypt Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Faculty of Hadith and Islamic Studies Islamic University of Madinah Madinah Saudi Arabia

Narrator disambiguation is a field within hadith science that studies unidentified narrators in hadith narration chains, also known as sanads. Sanads can be represented as graphs, with the nodes representing the narrators and the edges representing their relationships in the chain. The current methods for resolving the narrator disambiguation problem do not utilize the graph structure of the sanad, but by leveraging this structure, we can apply graph computational and deep learning techniques to identify narrators. This paper introduces a method that utilizes the sanad graph structure to identify all narrators in a given sanad. Our two-stage approach begins by generating a query embedding and identifying the top k narrator entities closest to the query embedding. We then use AraBERT to re-rank the top k narrators and make the final prediction. Our method achieves 94.6% accuracy on the validation set of AR-Sanad 280K dataset. Additionally, we present AR-Sanad 280K-v2, an updated dataset that represents real hadiths more accurately. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Structured Query Language

来源：评论

学校读者我要写书评

暂无评论

CAFFI: A Cybersecurity Assessment Framework for Fault Injection Threats in Smart IoT Devices 6

CAFFI: A Cybersecurity Assessment Framework for Fault Inject...

引用

6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Sadek, Mina G. Salem, Sameh A. Helwan University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt Ntra Egypt

ISBN: (纸本)9798350378511

The rapid increase in employing smart Internet of Things (IoT) systems in different domains, including critical national infrastructure and vital services has raised the need for robust security measures, especially against physical fault injection attacks. These attacks, involving techniques like voltage glitches and electromagnetic interference, can disrupt device operations, leading to unauthorized access, data extraction, or the disabling of security features, introducing a significant threat to the integrity and reliability of critical IoT-based applications. This paper proposes a novel framework for assessing the security of IoT devices against fault injection attacks. The framework employs multiple techniques, specifically voltage and electromagnetic attacks, to evaluate system resilience by targeting different fault models such as instruction skipping and bypassing security features. It is designed to be extensible for future fault models and microcontroller targets in the future. The framework is validated on the NXP LPC1343 microcontroller, demonstrating its effectiveness in bypassing debug interface protection. This framework enables systematic evaluation of IoT device robustness, helping identify vulnerabilities and develop more effective countermeasures to enhance the security of embedded systems and IoT devices. © 2024 IEEE.

关键词： Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

Efficient Cache Performance Equivalent 2D-Texel to Memory Mapping Identification for Embedded GPUs

引用

IEEE Access 2025年 13卷 95094-95121页

作者： El-Mahdy, Ahmed Elteir, Marwa K. Shata, Kholoud Giza Egypt Egypt–Japan University of Science and Technology Department of Computer Science and Engineering New Borg El-Arab City21934 Egypt Alexandria University Department of Computer and Systems Engineering Alexandria21544 Egypt New Borg El-Arab City Egypt

With the increasing trend of utilizing smart devices, exploiting embedded GPUs becomes plausible to provide for intense computations that approach workstation performance, opening the way for complex, server-grade applications on such devices. Although general-purpose development frameworks exist, they generally abstract implementation details making it difficult for further performance exploitation. One such critical aspect is the texture memory hierarchy design parameters, which are primarily trade secrets. Unfortunately, standard cache hierarchy identification methods are not applicable, due to utilizing logical 2D-texel-to-memory mapping to exploit the 2D locality of access prior to physical cache access. This paper presents, for the first time, a parameterized model capable of describing the underlying multi-dimensional tiling layouts governing this mapping. Although it can be shown that reverse-engineering the model to obtain the exact parameters is an undecidable problem, we strive to obtain a corresponding set of parameters that results in the same cache behavior. In particular, such parameters define the texture order resulting in a linear order that traverses the caches in memory contiguous blocks. This study proposes a reverse-engineering observation method that exploits the contiguity of tiled cached regions and cache set conflicts to efficiently reveal such parameters. The complexity of the method is O(n2), where n is the number of bits in one of the texture buffer dimensions, ensuring practical applicability. Furthermore, optimization of a benchmark workload—via input data and memory access pattern alignment with the inferred multidimensional tiling layout—yields up to a 2.22× speedup. © 2013 IEEE.

关键词： Reverse engineering

来源：评论

学校读者我要写书评

暂无评论

Deepfake Detection: Emerging Techniques and Evolving Challenges 12

Deepfake Detection: Emerging Techniques and Evolving Challen...

引用

12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Natarajan, Krishnaraj Mathur, Abeer Bhargava, Kshitiz Singh, Manvendra Tejpal, Moulik Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387315

Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most applications for these techniques or tools are in the fields of entertainment and education, some individuals with unlawful intent have also been benefited from them. These individuals use such techniques for various purposes, including the spread of misleading information and unnecessary propaganda, the incitement of political instability, hate and unrest, as well as for purposes of torture and blackmail. These high-quality and convincing manipulated images, audio, or videos are commonly referred to as ‘Deepfakes’. Since then, various solutions to the problems raised by Deepfakes have been proposed in academic studies. This literature review contains relevant publications that offered a variety of approaches to give an updated summary of the research activities in different types of Deepfake attacks, their detection, and countermeasures. It also assesses the effectiveness of the detection capabilities of different techniques with various datasets and algorithms applied in Deepfake detection, while also outlining the various benefits and drawbacks of various methodologies. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Deepfake Video Detection Using Inception_Resnet_V2: A Convolutional Neural Network Approach 16

Deepfake Video Detection Using Inception_Resnet_V2: A Convol...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Praveen Kumar, Mandala Kumari, V Valli Lakshmi Durga, K Swathi Andhra University College of Engineering Department of Computer Science and Systems Engineering Visakhapatnam530003 India

ISBN: (纸本)9798331505264

Deepfake videos, generated by sophisticated AI algorithms, pose significant challenges to the authenticity and trustworthiness of multimedia content on the internet. In this study, we propose a deepfake video detection system utilizing the Inception_ResNet_v2 architecture, a deep convolutional neural network renowned for its effectiveness in image classification tasks. The system is trained and evaluated on the Deepfake Challenge Dataset sourced from Kaggle, comprising a diverse set of real and manipulated video clips. We preprocess the dataset, extracting frames from each video and augmenting them to enhance model generalization. The Inception_ResNet_v2 model is then fine-tuned using transfer learning, leveraging pre-trained weights from ImageNet to expedite convergence and improve performance. Through extensive experimentation and evaluation, we demonstrate the efficacy of our approach in accurately distinguishing between genuine and deep-fake videos. The proposed system achieves promising results in detecting deep-fake videos, showcasing its potential utility in combating the proliferation of synthetic media and safeguarding the integrity of digital content. Our findings underscore the importance of leveraging advanced machine learning techniques for addressing the evolving challenges posed by deep-fake technology. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study on Deep Learning based Segmentation Techniques for Tamil Inscriptions 8

A Comparative Study on Deep Learning based Segmentation Tech...

引用

8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poornimathi, K. Muralibhaskaran, V. Priya, L. Rajalakshmi Engineering College Department of Computer Science and Business Systems India Rajalakshmi Engineering College Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Information Technology India

ISBN: (纸本)9798350376425

The digitization and preservation of Tamil inscriptions are crucial for safeguarding the rich cultural heritage they represent. This study presents an in-depth evaluation of deep learning-based segmentation methods specifically designed for the complex and ancient Tamil script. Evaluate several cutting-edge deep learning models, including Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, and attention-based mechanisms, using a dataset of Tamil inscriptions. The evaluation criteria encompass segmentation accuracy, computational efficiency, and the ability to handle the noise and degradation common in historical artifacts. Our findings indicate that traditional methods often falter when faced with the complexities of Tamil script, whereas advanced deep learning techniques markedly enhance segmentation quality. This improvement facilitates more precise transcription and analysis. The comparison emphasizes the benefits and challenges of each method, delivering important insights into their effectiveness for cultural heritage preservation and paving the way for future research in the digitization of ancient scripts. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Introduction

Advances in Industrial Control

引用

Advances in Industrial Control 2023年 1-18页

作者： Shi, Yang Shen, Chao Wei, Henglai Zhang, Kunwu Department of Mechanical Engineering University of Victoria VictoriaBC Canada Department of Systems and Computer Engineering Carleton University OttawaON Canada

This chapter provides the introductory knowledge about autonomous marine vehicle (AMV) systems. Literature reviews on the motion control of AMVs and model predictive control (MPC) are provided, and the motivations and main contributions of the book are summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Countering Deepfakes using an Improved Advanced CNN and its Ensemble with Pretrained Models 3

Countering Deepfakes using an Improved Advanced CNN and its ...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Mathur, Abeer Tejpal, Moulik Bhargava, Kshitiz Natarajan, Krishnaraj Singh, Manvendra Vellore Institute of Technology School of Computer Science & Engineering Department of Software Systems Vellore India Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India

ISBN: (纸本)9798350369083

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models - MobileNet and Xception - with a novel CNN architecture, the Advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：