检索结果-内蒙古大学图书馆

Interaction-aware Trust Management Scheme for IoT systems With Machine Learning-Based Attack Detection

IEEE Internet of Things Journal 2025年第11期12卷 17169-17182页

作者： Farhat, Ali Eldosouky, Abdelrahman Ibnkahla, Mohamed Matrawy, Ashraf Carleton University Department of Systems and Computer Engineering OttawaON Canada Carleton University School of Information Technology OttawaON Canada

The recent Internet of Things (IoT) adoption has revolutionized various applications while introducing significant security and privacy challenges. Traditional security solutions are unsuitable for IoT systems due to their dynamicity, heterogeneity, and resource constraints. Trust-based solutions are emerging as promising alternatives due to their ability to track the dynamic behavior in IoT systems. However, existing trust management schemes are implemented at the device level, raising several challenges, including device modification that compromises certification and scalability, increased network overhead, and higher device resource utilization. To address these challenges, this paper proposes a novel trust management scheme that shifts its implementation to a higher layer in the IoT system, specifically to the IoT access layer (e.g., gateway). The proposed scheme establishes trust based on typical device interactions with the gateway without requiring additional information from the device. It relies on objective attributes spanning communication, security, and advanced dimensions to compute the trust value of an IoT device. Additionally, an Artificial Neural Network (ANN) is integrated to determine if the device acts maliciously or behaves normally. Simulation results demonstrate a notable improvement in the detection rate, primarily due to incorporating the proposed ANN, compared to the threshold-based approaches in the literature. Overall, the improvements highlight the significant advantage of the proposed scheme's robustness. © 2025 IEEE.

关键词： Gateways (computer networks)

来源：评论

学校读者我要写书评

暂无评论

DDoS Flood Detection and Mitigation using SDN and Network Ingress Filtering - an Experiment Report 4

DDoS Flood Detection and Mitigation using SDN and Network In...

引用

4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Marleau, Sebastien Rahman, Prianna Lung, Chung-Horng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350360721

Distributed Denial of Service (DDoS) attacks are a common security threat to overwhelm a target server. IP source spoofing is a common approach used for DDoS attacks. It is claimed that all large DDoS attacks require IP spoofing and around 20% of the Internet still allows IP spoofing. DDoS attacks can also be launched much more easily using low-cost Internet of Things (IoT) devices. DDoS attacks originating from IoT devices have increased 5-fold in a year. Software-defined networking (SDN) has been proposed as a new paradigm to reduce complexity. However, security issues are still challenging for SDN, as DDoS attacks on the controller could fail the entire network. A mechanism that can detect and mitigate DDoS attacks on SDN is crucial. Ingress filtering has been adopted to reduce the probability of exploiting a network to launch an attack. This, among other things, mandates the dropping of packets if the source IP address of incoming packets is inconsistent with the configured one. Best Current Practice 38 (BCP 38) is a network ingress filtering technique to prevent source IP spoofing. By validating incoming packets, their IP addresses, and possibly other attributes at the ingress devices, it provides a first line of defense against suspicious traffic. We present an approach to DDoS detection and mitigation using BCP 38 and SDN. To evaluate the effectiveness of the proposed approach, a simple method was designed to detect and mitigate DDoS floods using BCP 38 and SDN. Experiments were conducted using Mininet and the Ryu controller. The result demonstrated that the proposed approach corresponded to DDoS attacks with IP spoofing efficiently and effectively. © 2024 IEEE.

关键词： Controllers

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Real-Time Data Processing Architectures: Kafka versus MQTT Broker in IoT 4

Comparative Analysis of Real-Time Data Processing Architectu...

引用

4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Ho, Chung Lai Deryck Lung, Chung-Horng Mao, Zhengding Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350360721

The exponential growth of Internet of Things (IoT) devices highlights the critical need for robust and efficient messaging systems to ensure seamless communication and real-time data exchange. In this research, a thorough comparative analysis was conducted focusing on the real-time data processing architectures of two leading messaging brokers in the IoT domain: Apache Kafka and Message Queuing Telemetry Transport (MQTT) broker. Despite widespread adoption, Kafka and MQTT brokers exhibit uniqueness in their architectures, features, and functionality in real-time data processing scenarios. The study aims to unravel the strengths and weaknesses of each technology within the context of real-time data processing, providing valuable insights to optimize their IoT data workflows. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Blockchain-based Dual Identity Management and Authentication Framework for IoT Networks 10

A Blockchain-based Dual Identity Management and Authenticati...

引用

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

作者： Hussein, Dana Haj Houlahan, Ethan Janelle-Goode, Alexandre Lumsden, Thomas Ibnkahla, Mohamed Carleton University Department of Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350373011

The widespread adoption of Internet of Things (IoT) devices has introduced an era of unparalleled connectivity and data-driven innovation. Nevertheless, this rapid proliferation introduced significant security challenges. The limited computational capabilities of IoT devices impose constraints on the implementation of robust security mechanisms, rendering them susceptible to malicious attacks. Additionally, the dynamic nature of IoT systems which allows multiple administrators to add or remove IoT devices, necessitates the establishment of an identity management system. This system is crucial for managing both devices and system users, ensuring the traceability of device registration activities, and maintaining accountability of system users. Furthermore, traditional centralized authentication systems encounter scalability issues and high costs associated with centralized servers. To address these challenges, this paper proposes a Dual Identity Management and Authentication (DIMA) framework, leveraging blockchain technology. The proposed framework addresses two primary issues. Firstly, it assigns dual identities to each IoT node, facilitating a secure authentication process and regulating network access. Secondly, it supports the traceability of system device registration activities, enabling accountability for system users and ensuring a secure administrator-in-the-loop device identification process. Moreover, experimental testing and security analysis are undertaken to validate the usability and ascertain the critical security strengths of the DIMA framework. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

COVID-19 Classification Using Pre-Trained Models and Disease Severity Score Masks 1

COVID-19 Classification Using Pre-Trained Models and Disease...

引用

1st International Conference on Computing, Internet of Things and Microwave systems, ICCIMS 2024

作者： Nehary, Ebrahim A. Rajan, Sreeraman Rossa, Carlos Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350351736

The significance of early detection of COVID-19 has been widely acknowledged as a means of reducing its spread and mortality rates among patients. Deep learning techniques for COVID-19 classification based on ultrasound (US) data have been extensively employed. However, detecting COVID-19 based on US images continues to be challenging primarily due to limited datasets with noisy and low-resolution images. This study investigates methods to enhance classification performance by incorporating disease severity score masks while training pretrained models enhanced with self-attention mechanisms. The disease severity scores range from 0 for healthy lung tissue to 1 for initial signs of abnormality, and 2 and 3 for advanced pathological artifacts. These masks and their corresponding US images are employed as inputs to pre-trained models for feature extraction. Subsequently, features extracted from the masks are utilized to recalibrate features obtained from US images using self-attention mechanisms. The proposed method achieves classification accuracy of 95.4, 90.4, 95%, 83%, and 92% when using pre-trained models VGG16, NASNet-Mobile, MobileNet_V2, ResNet50, and Xception, respectively. Further, all pre-trained models yield a low standard deviation of less than 5%. The results demonstrate that incorporating disease severity masks improves the classification performance, thus offering promising techniques for enhancing COVID-19 detection using ultrasound imaging. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Event Sourcing in Jason: Event-Driven State Reconstruction for BDI Agents

Event Sourcing in Jason: Event-Driven State Reconstruction f...

引用

2024 IEEE International Conference on Agents, ICA 2024

作者： Davies, Curtis Esfandiari, Babak Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798331539917

Event sourcing is a software design pattern that uses a log of events as the ground truth for recreating an application's state and for auditing purposes. In the context of agents, event sourcing uses event data captured from an agent's runtime, allowing for complex queries to be made about its behaviour during a mission. We present a proof-of-concept of this insight in the form of an 'agent simulator' for Jason (a BDI agent framework) to replay an agent's decision-making process and reconstruct its state at any given point in time. Through this process, we identify discrepancies between Jason's formal semantics and its Java implementation, leading us to propose revised semantic rules that accurately reflect an agent's reasoning cycle during runtime and provide a foundation for event-based explainability in agent-based systems. © 2024 IEEE.

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate 38

LookHere: Vision Transformers with Directed Attention Genera...

引用

38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Fuller, Anthony Kyrollos, Daniel G. Yassin, Yousef Green, James R. Department of Systems and Computer Engineering Carleton University OttawaON Canada

High-resolution images offer more information about scenes that can improve model accuracy. However, the dominant model architecture in computer vision, the vision transformer (ViT), cannot effectively leverage larger images without finetuning - ViTs poorly extrapolate to more patches at test time, although transformers offer sequence length flexibility. We attribute this shortcoming to the current patch position encoding methods, which create a distribution shift when extrapolating. We propose a drop-in replacement for the position encoding of plain ViTs that restricts attention heads to fixed fields of view, pointed in different directions, using 2D attention masks. Our novel method, called LookHere, provides translation-equivariance, ensures attention head diversity, and limits the distribution shift that attention heads face when extrapolating. We demonstrate that LookHere improves performance on classification (avg.↑ 1.6%), against adversarial attack (avg.↑ 5.4%), and decreases calibration error (avg.↓ 1.5%) - on ImageNet without extrapolation. With extrapolation, LookHere outperforms the current SoTA position encoding method, 2D-RoPE, by 21.7% on ImageNet when trained at 2242 px and tested at 10242 px. Additionally, we release a high-resolution test set to improve the evaluation of high-resolution image classifiers, called ImageNet-HR. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Pedestrian Detection: An Explainable Approach 23

Pedestrian Detection: An Explainable Approach

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Pang, Hongbo Huang, Changcheng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350374889

In the rapidly advancing field of autonomous technologies, particularly in transportation and surveillance, there is a critical need for accurate and reliable pedestrian detection systems. While deep learning models have achieved remarkable success in this area, their 'black box' nature often concerns their trustworthiness and practical applicability. Our study introduces a modified YOLOv5s model, augmented with a Global Attention Mechanism and enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM) for improved explainability. This approach maintains the high accuracy typical of deep learning models while significantly increasing their transparency. Our modified YOLOv5s architecture is tailored for real-time applications, achieving an inference speed of 9.4 milliseconds on a single NVIDIA A100 GPU. Incorporating Grad-CAM provides a more intuitive interpretation of the model's decisions, potentially increasing user confidence in its predictions. We evaluate our enhanced framework on the CityPersons dataset, demonstrating improvements in precision, recall, and mAP@0.5 compared to the baseline YOLOv5s model. Our results indicate a promising advancement in pedestrian detection, balancing high performance with improved explainability. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Energy Sustainability in Dense Radio Access Networks via High Altitude Platform Stations

IEEE Networking Letters

引用

IEEE Networking Letters 2024年第1期6卷 21-25页

作者： Salamatmoghadasi, Maryam Mehrabian, Amir Yanikomeroglu, Halim Carleton University Department of Systems and Computer Engineering OttawaK1S 5B6 Canada

The growing demand for radio access networks (RANs) driven by advanced wireless technology and the ever-increasing mobile traffic, faces significant energy consumption challenges that threaten sustainability. To address this, an architecture referring to the vertical heterogeneous network (vHetNet) has recently been proposed. Our study seeks to enhance network operations in terms of energy efficiency and sustainability by examining a vHetNet configuration, comprising a high altitude platform station (HAPS) acting as a super macro base station (SMBS), along with a macro base station (MBS) and a set of small base stations (SBSs) in a densely populated area. By intelligently managing SBSs' sleep mode and employing HAPS's potentials and additional capacity, our approach aims to minimize vHetNet energy consumption. The proposed method dynamically determines which SBSs to switch off based on the traffic load of SBSs, MBS, and HAPS. This innovative approach offers a flexible and promising solution to enhance network sustainability, energy efficiency, and capacity utilization without compromising the user quality-of-service (QoS). We show that our proposed method offers a scalable solution with comparable performance to exhaustive search (ES) as the optimal solution in terms of energy efficiency. Furthermore, incorporating HAPS, significantly improves grid power consumption, compared to having no offloading, reducing it by 30% for a large number of SBSs. © 2019 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

3D Indoor Positioning Using the 2D-MUSIC Algorithm

3D Indoor Positioning Using the 2D-MUSIC Algorithm

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Hassan, Payam Pourzadeh Marsland, Ian Smith, Roland Kerr, Ron Iun, Edwin Lambadaris, Ioannis Carleton University Department of Systems and Computer Engineering Ottawa Canada Ericsson Canada Ottawa Canada

ISBN: (纸本)9798350351255

This paper presents an advanced implementation of the single-snapshot 2D-MUSIC algorithm, enhanced by cross-linear antenna arrays, to improve uplink indoor positioning accuracy using OFDM-based 5G networks. The proposed method adeptly handles multipath interference and non-line-of-sight (NLoS) conditions, demonstrating significant advancements over traditional techniques. Our method not only accurately determines the position of user equipment in two dimensions but also extends to 3D positioning. A novel aspect of our research includes addressing the challenge of unknown time of departure, which often complicates the time of flight calculations necessary for precise localization. By using two strategically placed cross-linear antenna arrays, our system compensates for this uncertainty, providing reliable and precise location estimates even without perfect transmitter-receiver synchronization. Simulation results validate the robustness of our approach, showing exceptional localization accuracy and promising potential for complex IoT applications within industrial settings. Provided the separation between multipath components in either range or angle is sufficiently large, the algorithm is capable of detecting the transmitter with sub-centimeter accuracy. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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：