2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)最新文献

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PWDGAN: Generating Adversarial Malicious URL Examples for Deceiving Black-Box Phishing Website Detector using GANs PWDGAN:利用gan生成欺骗黑箱网络钓鱼网站检测器的敌对恶意URL示例

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690540

Trinh Nguyen Bac, Phan The Duy, V. Pham

{"title":"PWDGAN: Generating Adversarial Malicious URL Examples for Deceiving Black-Box Phishing Website Detector using GANs","authors":"Trinh Nguyen Bac, Phan The Duy, V. Pham","doi":"10.1109/ICMLANT53170.2021.9690540","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690540","url":null,"abstract":"In recent years, the Internet has witnessed a significant increase in phishing attacks. These attacks are not merely deceiving Internet users to get their sensitive information, but phishing attacks are developing more and more sophisticated, using many new techniques to try to bypass the traditional defense solution. With the help of machine learning and deep learning algorithms, there are researched solutions and software products to help improve the ability to detect phishing attacks. In this article, we build a model based on generative adversarial network (GAN) – a deep learning-based framework to conduct black-box attacks using Phishtank and Alexa datasets that try to evade and bypass ML-based phishing detectors. We apply PWDGAN to carry out the attacks against the ML-based black-box classifiers and then deceive successfully these detectors with the rate of detecting malicious samples value of approximately 0% after 500 training epochs. The results of the paper demonstrate the effectiveness of GAN adoption in creating new patterns that can evade and bypass phishing detectors. These newly generated patterns can serve as material for future research in phishing website detection and improve the ability to detect novel anomaly attacks.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125533803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A CNN-based algorithm for selecting tree-of-interest images acquired by UAV 基于cnn的无人机感兴趣树图像选择算法

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690556

I. Salazar-Reque, D. Arteaga, Kevin Guerra Huamán, S. H. Huamán Bustamante

{"title":"A CNN-based algorithm for selecting tree-of-interest images acquired by UAV","authors":"I. Salazar-Reque, D. Arteaga, Kevin Guerra Huamán, S. H. Huamán Bustamante","doi":"10.1109/ICMLANT53170.2021.9690556","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690556","url":null,"abstract":"In recent years, the rapid development of unmanned aerial vehicles (UAV) and high-resolution cameras offered an important source of fine-grained imagery. This rich information has the potential to be used in many agricultural and forestry applications. Many of these applications require the monitoring of individuals trees along time. Thus, a crucial step is to select the best images of a specific tree from a big number of images acquired by the UAV. If made by hand, this is a very time-consuming activity and using geolocation metadata is not enough to tackle this problem. In this work, we present an algorithm to automate this process. The algorithm uses image geolocation data as a first filter for selections and tree segmentations to refine and select the best images. We used a convolutional neural network (CNN) to generate tree segmentations which achieved an accuracy of 0.98 when compared with manually segmented images. To test the image selection algorithm, we collected a total of 4807 RGB images in six different flights over an agricultural field with 144 avocado trees. We compare the selection algorithm outcomes with human selections per tree. The algorithm achieved an average true positive rate (TPR) of 0.88 for the selection of the three best images.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128690352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Development of a teleoperated robot based on an experimental design for sterilization in Honduras 基于洪都拉斯消毒实验设计的遥控机器人的研制

2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT) Pub Date : 2021-12-16 DOI: 10.1109/ICMLANT53170.2021.9690534

J. L. Ordoñez-Ávila, Fredy Danilo Bonilla, E. D. García

{"title":"Development of a teleoperated robot based on an experimental design for sterilization in Honduras","authors":"J. L. Ordoñez-Ávila, Fredy Danilo Bonilla, E. D. García","doi":"10.1109/ICMLANT53170.2021.9690534","DOIUrl":"https://doi.org/10.1109/ICMLANT53170.2021.9690534","url":null,"abstract":"The coronavirus disease (COVID-19) took the world by surprise; even the most developed countries are having troubles containing this virus. Stopping the infections and reducing the risks caused by this virus and other bacteria became the priority of many countries. For which, many countries applied various technologies during the pandemic. The aim of this research is to develop a UV-C teleoperated robot to sterilize hospital operating rooms based on an experimental design. This development was obtained after applying the V method and some experimentation tests. This V method allows to break down a mechatronic product into systems, subsystems, and parts. The main systems developed were the sterilization system and the locomotion system. The independent variables are radio of exposition and sterilization time. The dependent variable of this study was the percentage of sterilization wich is affected by the robot mobility, time and distance. For the development of the project, SolidWorks was used for simulations of locomotion, and structure design, Matlab for visualization of graphs and Excel Solver for the application of a non-linear model of reduced gradient for optimization of the variables. The sterilization system was based on the collection of experimental data from different tests resulting in a UV-C dose of 1252 J/m2. Based on the optimization, a sterilization time of 8.2 minutes and less than 100 cm was obtained for a sterilization of 99%. It can be teleoperated with radio frequency and camera. According to the mobility and sterilization tests that were developed experimentally, the robot could be teleoperated to sterilize operating rooms in hospitals.","PeriodicalId":127769,"journal":{"name":"2021 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132939843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

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