110023 research outputs found
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Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study
No full text[[abstract]]The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers
PPRP: Preserving-Privacy Route Planning Scheme in VANETs
No full text[[abstract]]Route planning helps a vehicle to share a message with the roadside units (RSUs) on its path in advance, which greatly speeds the authentication between the vehicle and the RSUs when the vehicle enters the RSUs’ coverage. In addition, since only a small amount of necessary information needs to be shared between the vehicle and the RSUs, route planning can reduce the storage overhead of the vehicle’s on-board unit (OBU) and the RSUs. However, the message sharing requires the assistance of the certification authority (CA), which will lead CA easily to obtain the vehicle’s planning route. Although CA knows the vehicle’s registration information and helps the vehicle to communicate with RSUs, it is unacceptable that the path of their vehicle is obtained by CA for most drivers. In fact, vehicle’s sensitive information such as planning route, starting time, stop place, should be privacy for others including CA. Inspired with the method of oblivious transfer, a preserving-privacy route planning scheme in VANETs is proposed in this article, in which, a vehicle deduces the information of RSUs on its path with the help of CA, while CA knows nothing about which RSUs’ information has been deduced by the vehicle. Later, fast authentication or other service is easily achieved between the vehicle and the RSUs (V2R) with the pre-shared information. After V2R authentication, vehicles could easily communicate with adjacent vehicles with the help of RSUs (V2V). Finally, compared with related schemes, performance evaluation illustrates the proposed scheme is better in terms of time consumption
Regulated Two-Dimension Deep Convolutional Neural Network-Based Power Quality Classifier for Microgrid
No full text[[abstract]]Due to the penetration of renewable energy and load variation in the microgrid, the diagnosis of power quality disturbances (PQD) is important to the operation stability and safety of the microgrid system. Once the power imbalance is present between the generation and the load demand, the fundamental frequency would deviate from the nominal value. As a result, the performance of the power quality classifier based on the neural network would be deteriorated since the deviation of fundamental frequency is not taken into account. In this paper, the regulated two-dimensional (2D) deep convolutional neural network (CNN)-based approach for PQD classification is proposed. In the data preprocessing stage, the IEC-based synchronizer is introduced to detect the deviation of fundamental frequency. In this way, the 2D grayscale image serving as the input of the deep CNN classifier can be accurately regulated. The obtained 2D image can effectively preserve information and waveform characteristics of the PQD signal. The experiment is implemented with datasets containing 14 different categories of PQD. According to this result, it is revealed that the regulated 2D deep CNN can improve the effectiveness of PQD classification in a real-time manner. Furthermore, the proposed method outperforms the methods in previous studies according to the field verification
Securing heterogeneous embedded devices against XSS attack in intelligent IoT system
No full text[[abstract]]Today, we are living in the realm of Internet of Things (IoT) where simple objects are embedded with the capabilities to understand and operate in its surroundings for offering distinct services to the users. These objects are shipped with their user interfaces that facilitate user to perform administrative activities on the devices using a web browser linked to the device's server. Cross-Site Scripting (XSS) is the most prevalent web application's vulnerability, exploited by an attacker to compromise the embedded devices. This research work is focused towards the development of an approach to defend against XSS attack to safeguard embedded devices deployed in intelligent IoT system. It performs identification through comparing injected strings with the blacklisted attack vectors and mitigates its harmful effects by implementing filtering method in an optimized fashion. It is a fog-enabled approach that operates locally to identify the compromised device within the IoT network. We demonstrate attack exploitation on two smart devices including digital IP Camera and wireless router and then tested the performance of our proposed approach on them. The experimental results highlight the efficacy of the approach as it attains an accuracy of 0.9 and above, on both the tested platforms
The NITRDrone Dataset to Address the Challenges for Road Extraction from Aerial Images
No full text[[abstract]]Recent years have witnessed a dramatic evolution in small-scale remote sensors such as Unmanned aerial vehicles (UAVs). Characteristics such as automatic flight control, flight time, and image acquisition have fueled various computer-vision tasks, providing better efficiency and usefulness than fixed viewing surveillance cameras. However, in constrained scenarios, the number of UAV-based aerial datasets is still low, which comparatively focuses on specific tasks such as image segmentation. In this paper, we present a high-resolution UAV-based image-dataset, named “NITRDrone” focusing on aerial image segmentation tasks especially extracting the road networks from the aerial images. The images and video sequences in this dataset are captured over different locations of the NITR campus area, covering around 650 acres. Thus, it provides many diversified scenarios to be considered while analyzing aerial images. In particular, the dataset is prepared to address the existing challenges in UAV-based aerial image segmentation problems. Extensive experiments have been conducted to prove the effectiveness of the proposed dataset to address the aerial segmentation problems through the existing state-of-the-art methodologies. Out of the considered baseline methodologies, U-Net performs the best with an intersection of union (IoU) of 0.77, followed DeepLabplusException (IoU: 0.74) and SegNet (IoU: 0.68). We hope the NITRDrone dataset will encourage the researchers while boosting the research and development in the visual analysis of UAV platforms. The NITRDrone dataset is available online at: [https://github.com/drone-vision/NITRDrone-Dataset]
The NITRDrone Dataset to Address the Challenges for Road Extraction from Aerial Images
No full text[[abstract]]Recent years have witnessed a dramatic evolution in small-scale remote sensors such as Unmanned aerial vehicles (UAVs). Characteristics such as automatic flight control, flight time, and image acquisition have fueled various computer-vision tasks, providing better efficiency and usefulness than fixed viewing surveillance cameras. However, in constrained scenarios, the number of UAV-based aerial datasets is still low, which comparatively focuses on specific tasks such as image segmentation. In this paper, we present a high-resolution UAV-based image-dataset, named “NITRDrone” focusing on aerial image segmentation tasks especially extracting the road networks from the aerial images. The images and video sequences in this dataset are captured over different locations of the NITR campus area, covering around 650 acres. Thus, it provides many diversified scenarios to be considered while analyzing aerial images. In particular, the dataset is prepared to address the existing challenges in UAV-based aerial image segmentation problems. Extensive experiments have been conducted to prove the effectiveness of the proposed dataset to address the aerial segmentation problems through the existing state-of-the-art methodologies. Out of the considered baseline methodologies, U-Net performs the best with an intersection of union (IoU) of 0.77, followed DeepLabplusException (IoU: 0.74) and SegNet (IoU: 0.68). We hope the NITRDrone dataset will encourage the researchers while boosting the research and development in the visual analysis of UAV platforms. The NITRDrone dataset is available online at: [https://github.com/drone-vision/NITRDrone-Dataset]
The System Adoption Evaluation of RFID Safety Management System on Campus
No full text[[abstract]]In this article, we propose a campus safety management system based on radio frequency identification (RFID) technology. In this work, the evaluation based on the system adoption was used to establish a campus-safety management system model and related implementation issues. With the implementation of the RFID system for campus safety management, the system could save the administrators' time and improve the efficiency of service quality. In addition, it helps to prevent the occurrence of security incidents on campus. The critical success factors of the system include the participation of students and parents. Finally, the evaluation on the system adoption is made by questioners. The results show the first three factors affecting the administrators in the school to decide whether the system is used the convenience of use, agreement of students' parents, and the relative advantage. However, school competition and school reorganization could not be the preferred factors to adopt the system
A Study on Customers’ Usage Intention of the Mobile Payment Based on Technology Acceptance Model - The Mediation Effect of Transaction Security
No full text[[abstract]]在台灣為順應金融自由化潮流，與金融體系的日趨完善，造就支付工具演進，掀起塑膠貨幣熱潮，由於網路盛行，搭載著智慧型行動裝置的興起，與近來FINTECH(Financial Technology，財務科技)議題灼熱，金融機構為取得網路商務經濟脈絡的先機，造就行動支付業務產生，也為軟硬體廠商、行動電信業者、銀行團帶來新的挑戰與商機。為協議相關產業對未來產品改良與行銷策略之參考，藉以達成普及市場之目的，本研究試圖探討消費者對近端行動支付採用意圖的驅動因素，並根據研究的發現提學術理論與管理意涵。本研究以Davis(1989)之科技接受模式作為主要架構，再結合交易安全觀點共同探討與行為意圖之間的關係。本研究採用問卷收集資料，其中共收回423份，其中有效問卷400份，有效回收率為94.5%。研究結果發現，在行為意圖方面，知覺易用性是影響消費者對行動支付之行為意圖的最主要因素，而知覺有用性、使用態度亦與行為意圖呈現正相關。此外，近端行動支付使用者對行動支付的「交易安全認知」對於使用者的「使用態度」及「行為意願」具有中介的效果[[abstract]]For following the trend of financial liberalization, cause the evolution of payment tools and the tide of plastic currency with the mature financial system in Taiwan. With the popularity of the Internet, the rise of smart mobile devices and the recent hot issues in financial technology (FINTECH), financial institutions have made great effort in mobile payment services which bring new challenges and business to hardware manufacturers, software manufacturers, mobile telecom operators, telecommunications companies and banks for getting a head start. This study attempts to explore consumers' driving factors in intentions of mobile payment, furthermore, we can advance the theoretical theory and managerial implications based on the findings of the study. This study takes Technology Acceptance Mode from Davis (1989) as the main structure, combined with the view of transactional security to explore the relationship. In addition, there were 423 questionnaires collected in this study, and 400 were valid questionnaires, so the effective response rate was 94.5%.The result shows that perceptual ease of use is the most important factor in influencing consumers' usage intention of the mobile payment, furthermore, perceptual usefulness and attitude toward use are also positively correlated with behavioral intention. Besides, the mobile payment users’ perceived security of transaction has the mediation effect on attitude toward use and behavioral intention
Springer International Handbooks of Education. Second Handbook of English Language Teaching
No full text運用過渡設計理念探索後疫情體驗設計專業之變革
No full text[[abstract]]本研究預期之成果，（1）為後疫情的新常態創造可適應與良好的新體驗。（2）後疫情之常態的生活與工作，能依反推式方法逐步達成未來目標，並建構出具體可行的解決方案與策略措施。（3）過渡設計的具體實踐，以驗證過渡設計能解決未來難以預測且複雜的問題。（4）過渡設計之未來解決問題方案，能引領設計專業在問題解決上突破現有的框架，以程序式、方式與方法以及措施策略等予以解決問題，而非僅以型態及色彩作為設計結果。本研究的主要貢獻，在設計教育面，驗證一新的未來設計教育可能的新學科「過渡設計」；在設計專業面上，為創造新未來設計提供一新的設計理念與思維；在後疫情時期則為未來的社會與環境提出一新措施，供超前部署。 [[note]]科技部[[note]]2022-08-01～2023-07-3