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نتیجه جستجو - NTRU

تعداد مقالات یافته شده: 157
ردیف عنوان نوع
1 Internet of Things-enabled Passive Contact Tracing in Smart Cities
ردیابی تماس غیرفعال با قابلیت اینترنت اشیا در شهرهای هوشمند-2022
Contact tracing has been proven an essential practice during pandemic outbreaks and is a critical non-pharmaceutical intervention to reduce mortality rates. While traditional con- tact tracing approaches are gradually being replaced by peer-to-peer smartphone-based systems, the new applications tend to ignore the Internet-of-Things (IoT) ecosystem that is steadily growing in smart city environments. This work presents a contact tracing frame- work that logs smart space users’ co-existence using IoT devices as reference anchors. The design is non-intrusive as it relies on passive wireless interactions between each user’s carried equipment (e.g., smartphone, wearable, proximity card) with an IoT device by uti- lizing received signal strength indicators (RSSI). The proposed framework can log the iden- tities for the interacting pair, their estimated distance, and the overlapping time duration. Also, we propose a machine learning-based infection risk classification method to char- acterize each interaction that relies on RSSI-based attributes and contact details. Finally, the proposed contact tracing framework’s performance is evaluated through a real-world case study of actual wireless interactions between users and IoT devices through Bluetooth Low Energy advertising. The results demonstrate the system’s capability to accurately cap- ture contact between mobile users and assess their infection risk provided adequate model training over time. © 2021 Elsevier B.V. All rights reserved.
keywords: بلوتوث کم انرژی | ردیابی تماس | اینترنت اشیا | طبقه بندی خطر عفونت | Bluetooth Low Energy | Contact Tracing | Internet of Things | Infection Risk Classification
مقاله انگلیسی
2 Is the Internet of Things a helpful employee? An exploratory study of discourses of Canadian farmers
آیا اینترنت اشیا یک کارمند مفید است؟ بررسی اکتشافی گفتمان های کشاورزان کانادایی-2022
The increasing global population and the growing demand for high-quality products have called for the modernization of agriculture. “Internet of Things” is one of the technologies that is pre- dicted to offer many solutions. We conducted a discourse analysis of 19 interviews with farmers in Ontario, Canada, asking them to describe their experience of working with IoT and related technologies. One main discourse with two opposing tendencies was identified: farmers recognize their relationship with IoT and related technology and view technology as a kind of “employee”, but some tend to emphasize (1) an optimistic view which is discourse of technology is a “Helpful Employee”; while others tend to emphasize (2) a pessimistic view which is a discourse of tech- nology is an “Untrustworthy Employee”. We examine these tendencies in the light of the literature on organizational behavior and identify potential outcomes of these beliefs. The results suggest that a farmer’s style of approaching technology can be assessed on a similar scale as managers’ view of their employees and provide a framework for further research.
keywords: فناوری اینترنت اشیا | کشاورزی | تحلیل گفتمان | سبک استفاده از تکنولوژی | Internet of things technology | Agriculture | Discourse analysis | Style of use of technology
مقاله انگلیسی
3 PortiK: A computer vision based solution for real-time automatic solid waste characterization – Application to an aluminium stream
PortiK: یک راه حل مبتنی بر بینایی کامپیوتری برای شناسایی خودکار زباله جامد در زمان واقعی - کاربرد در جریان آلومینیوم-2022
In Material Recovery Facilities (MRFs), recyclable municipal solid waste is turned into a precious commodity. However, effective recycling relies on effective waste sorting, which is still a challenge to sustainable develop- ment of our society. To help the operations improve and optimise their process, this paper describes PortiK, a solution for automatic waste analysis. Based on image analysis and object recognition, it allows for continuous, real-time, non-intrusive measurements of mass composition of waste streams. The end-to-end solution is detailed with all the steps necessary for the system to operate, from hardware specifications and data collection to su- pervisory information obtained by deep learning and statistical analysis. The overall system was tested and validated in an operational environment in a material recovery facility. PortiK monitored an aluminium can stream to estimate its purity. Aluminium cans were detected with 91.2% precision and 90.3% recall, respectively, resulting in an underestimation of the number of cans by less than 1%. Regarding contaminants (i.e. other types of waste), precision and recall were 80.2% and 78.4%, respectively, giving an 2.2% underestimation. Based on five sample analyses where pieces of waste were counted and weighed per batch, the detection results were used to estimate purity and its confidence level. The estimation error was calculated to be within ±7% after 5 minutes of monitoring and ±5% after 8 hours. These results have demon- strated the feasibility and the relevance of the proposed solution for online quality control of aluminium can stream.
keywords: امکانات بازیابی مواد | شناسایی مواد زائد جامد | یادگیری عمیق | شبکه عصبی عمیق | بینایی کامپیوتر | Material recovery facilities | MRF | Solid waste characterization | Deep-learning | Deep neural network | Computer vision
مقاله انگلیسی
4 Post-Quantum Blockchain-Based Data Sharing for IoT Service Providers
به اشتراک گذاری داده های مبتنی بر بلاک چین پسا کوانتومی برای ارائه دهندگان خدمات اینترنت اشیا-2022
Quantum technologies have made significant advances and are likely to lead to important security challenges and threats to networks in the near future. On the other hand, sharing the huge amount of data from the Internet of Things (IoT) in the context of data as a service could provide new revenue streams for infrastructure providers and service providers. However, post-quantum computing exposes the entire data sharing ecosystem to a new set of security risks. In this article, we propose a novel blockchain-based system for data sharing in the post-quantum era. The proposed system facilitates data sharing among multiple organizations while meeting compliance and regulatory requirements via private blockchain. We implemented the proposed architecture and information flow using three blockchain networks (namely Hyperledger Fabric, Ethereum, and Quorum) and selected NTRU as our quantum resistant security algorithm (QRSA) to compare the parallelization performance of Toom-Cook’s and Karatsuba’s computation methods. Experimental results show that parallel computation has a positive impact when the security level of QRSAs is lowered, and the transaction time savings is almost 50 percent in favor of Quorum. Finally, we outline the main challenges and potential solutions.
مقاله انگلیسی
5 Post-Quantum Era in V2X Security: Convergence of Orchestration and Parallel Computation
دوران پسا کوانتومی در امنیت V2X: همگرایی ارکستراسیون و محاسبات موازی-2022
Along with the potential emergence of quantum computing, safety and security of new and complex communication services such as automated driving need to be redefined in the post-quantum era. To ensure reliable, continuous, and secure operation of these scenarios, quantum-resistant security algorithms (QRSAs) that enable secure connectivity must be integrated into the network management and orchestration systems of mobile networks. This article explores a roadmap study of post-quantum era convergence with cellular connectivity using the Service & Computation Orchestrator (SCO) framework for enhanced data security in radio access and backhaul transmission with a particular focus on vehicle-to-everything services. Using NTRU as a QSRA, we show that the parallelization performance of the Toom-Cook and Karatsuba computation methods can vary based on different CPU load conditions through extensive simulations, and that the SCO framework can facilitate the selection of the most efficient computation for a given QRSA. Finally, we discuss the evaluation results, identify the current standardization efforts, and present possible directions for the coexistence of post-quantum and mobile network connectivity through an SCO framework that leverages parallel computing.
مقاله انگلیسی
6 A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
بررسی جامع تشخیص حملات سایبری: مجموعه داده‌ها، روش‌ها، چالش‌ ها و جهت‌گیری‌های تحقیقاتی آینده-2022
Rapid developments in network technologies and the amount and scope of data transferred on networks are increasing day by day. Depending on this situation, the density and complexity of cyber threats and attacks are also expanding. The ever-increasing network density makes it difficult for cybersecurity professionals to monitor every movement on the network. More frequent and complex cyberattacks make the detection and identification of anomalies in network events more complex. Machine learning offers various tools and techniques for automating the detection of cyber attacks and for rapid prediction and analysis of attack types. This study discusses the approaches to machine learning methods used to detect attacks. We examined the detection, classification, clustering, and analysis of anomalies in network traffic. We gave the cyber-security focus, machine learning methods, and data sets used in each study we examined. We investigated which feature selection or dimension reduction method was applied to the data sets used in the studies. We presented in detail the types of classification carried out in these studies, which methods were compared with other methods, the performance metrics used, and the results obtained in tables. We examined the data sets of network attacks presented as open access. We suggested a basic taxonomy for cyber attacks. Finally, we discussed the difficulties encountered in machine learning applications used in network attacks and their solutions
Keywords: Cyber attacks | Machine learning | Deep learning | Geometric deep learning | Cyber security | Adversarial machine learning | Intrusion detection
مقاله انگلیسی
7 A survey on blockchain, SDN and NFV for the smart-home security
مروری بر بلاک چین، SDN و NFV برای امنیت خانه های هوشمند-2022
Due to millions of loosely coupled devices, the smart-home security is gaining the attention of industry professionals, attackers, and academic researchers. The smart home is a typical home where many sensors, actuators, and IoT devices are used to automate home users’ daily activities. Although a smart home provides comfort, safety, and satisfaction to users, it opens up multiple challenging security issues when automating and offering intelligent services. Recent studies have investigated not only blockchain but SDN and NFV to address these challenges. We present a comprehensive survey on blockchain, SDN, and NFV for smart-home security. The paper also proposes a new architecture of the smart-home security. First, we describe the features of the smart home and its current security issues. Next, we outline the characteristics of blockchain, SDN, and NFV, including their contribution to improving the smart-home security. While SDN enhances the management and access control of the home network by providing a programmable controller to home nodes, NFV implements the functions of network appliances (e.g., network monitoring, firewall) as virtual machines and ensures the high availability of the network. Blockchain reinforces IoT data’s privacy, integrity, and security and improves the trust in transactions among untrusted IoT devices. Finally, we discuss open issues and challenges in the field and propose recommendations towards high-level security for the smart home.
Keywords: Smart homes | IoT | Privacy | Security | Trust | Blockchain | SDN | NFV
مقاله انگلیسی
8 A Two-layer Fog-Cloud Intrusion Detection Model for IoT Networks
مدل تشخیص نفوذ مه-ابر دو لایه برای شبکه های اینترنت اشیا-2022
The Internet of Things (IoT) and its applications are becoming ubiquitous in our life. However, the open deployment environment and the limited resources of IoT devices make them vulnerable to cyber threats. In this paper, we investigate intrusion detection techniques to mitigate attacks that exploit IoT security vulnerabilities. We propose a machine learning-based two-layer hierarchical intrusion detection mechanism that can effectively detect intrusions in IoT networks while satisfying the IoT resource constraints. Specifically, the proposed model effectively utilizes the resources in the fog layer of the IoT network by efficiently deploying multi-layered feedforward neural networks in the fog-cloud infrastructure for detecting network attacks. With a fog layer into the picture, analysis is dynamically distributed across the fog and cloud layer thus enabling real-time analytics of traffic data closer to IoT devices and end-users. We have performed extensive experiments using two publicly available datasets to test the proposed approach. Test results show that the proposed approach outperforms existing approaches in multiple performance metrics such as accuracy, precision, recall, and F1-score. Moreover, experiments also justified the proposed model in terms of improved service time, lower delay, and optimal energy utilization.
keywords: Fog computing | Intrusion detection | IoT network | Machine learning | Security
مقاله انگلیسی
9 Beacon Non-Transmission attack and its detection in intelligent transportation systems
حمله عدم انتقال Beacon و تشخیص آن در سیستم های حمل و نقل هوشمند-2022
Message dropping by intermediate nodes as well as RF jamming attack have been studied widely in ad hoc networks. In this paper, we thoroughly investigate a new type of attack in intelligent transportation systems (ITS) defined as Beacon Non-Transmission (BNT) attack in which attacker is not an intermediate vehicle, but rather a source vehicle. In BNT attack, a vehicle suppresses the transmissions of its own periodic beacon packets to get rid of the automated driving misbehavior detection protocols running in ITS, or to mount a Denial-ofService (DoS) attack to cripple the traffic management functionality of ITS. Considering BNT attack as a critical security threat to ITS, we propose two novel and lightweight techniques to detect it. Our first technique bases its detection by assuming a certain distribution of the number of beacons lost from a vehicle while accounting for loss due to channel-error. However, it fails to classify shortish BNT attacks wherein amount of denial and channel-error loss are comparable. Our second technique, suitable for identifying both shortish and longish BNT attacks, considers beacon loss pattern of a vehicle as a time-series data and employs autocorrelation function (ACF) to determine the existence of an attack. In order to trade-off detection accuracy for equitable use of limited computational resources, we propose a random inspection model in which the detection algorithm is executed at random time instances and for randomly selected set of vehicles. We have performed extensive simulations to evaluate the performance of proposed detection algorithms under random inspection and a practical attacker model. The results obtained corroborate the lightweight nature of both techniques, and the efficacy of ACF based technique over simple threshold based technique in terms of higher detection accuracy as well as smaller reaction delay.
keywords: DoS attack | Intrusion detection | Driving anomaly detection | Beacon | Autocorrelation function | Intelligent Transportation Systems
مقاله انگلیسی
10 Detection of moving objects using thermal imaging sensors for occupancy estimation
تشخیص اجسام متحرک با استفاده از سنسورهای تصویربرداری حرارتی برای تخمین اشغال-2022
Thermal imaging sensors have been increasingly integrated in a wide range of smart building and Internet of Things systems. Low-resolution thermal imaging sensors are especially suitable for applications that require non-intrusive monitoring with proper privacy protection. In this paper, we present an in-depth investigation of a low-resolution thermal imaging sensor (i.e., Melexis MLX90640) focusing on algorithm design issues and solutions when detecting moving objects. This type of sensors are designed to operate with a two-subpage chessboard reading pattern, which gives rise to blob displacements across two subpages when target objects are in motion. We have conducted systematic characterization of the sensor and demonstrated issues through experimental measurements and analysis. We have also proposed a subpage bilinear interpolation method and an enhanced sensor data preprocessing method for occupancy estimation with moving objects. The performance of the proposed method is analyzed by training and testing classification algorithms using two datasets collected with objects of different moving speeds. Our performance results indicate that the proposed method could be used for occupancy estimation in various smart building and Internet of Things applications.
keywords: طبقه بندی | حسگر مادون قرمز | اینترنت اشیا | یادگیری ماشین | برآورد اشغال | ساختمان های هوشمند | Classification | Infrared array sensor | Internet of Things | Machine learning | Occupancy estimation | Smart buildings
مقاله انگلیسی
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