2020 30th International Telecommunication Networks and Applications Conference (ITNAC)最新文献_第2页

A Study of Reference Node Placement based on 4- quadrant Node Selection in Ultra Wideband Indoor Positioning System 超宽带室内定位系统中基于四象限节点选择的参考节点放置研究

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315162

Kriangkrai Maneerat, Krisada Chinda, Charuwalee Suwatthikul, L. Klinkusoom, L. Kovavisaruch, K. Kaemarungsi

引用次数: 2

Features of Human-Centred Algorithm Design 以人为本算法设计的特点

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315169

M. Cherrington, David Airehrour, Joan Lu, Qiang Xu, David Cameron-Brown, Ihaka Dunn

引用次数: 1

Design of microstrip antenna and RF circuit for robust wireless communication of IoT devices under partial shield installation 部分屏蔽下物联网设备鲁棒无线通信微带天线和射频电路设计

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315107

Bo Hu, Benjamin Fisher, Alirio Guerra, J. Mo

引用次数: 0

Feature-Based Adversarial Attacks Against Machine Learnt Mobile Malware Detectors 针对机器学习移动恶意软件检测器的基于特征的对抗性攻击

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315144

M. Shahpasand, Len Hamey, M. Kâafar, Dinusha Vatsalan

{"title":"Feature-Based Adversarial Attacks Against Machine Learnt Mobile Malware Detectors","authors":"M. Shahpasand, Len Hamey, M. Kâafar, Dinusha Vatsalan","doi":"10.1109/ITNAC50341.2020.9315144","DOIUrl":"https://doi.org/10.1109/ITNAC50341.2020.9315144","url":null,"abstract":"The success of Machine Learning (ML) techniques in security applications, such as malware detection, is highly criticized for their vulnerability to Adversarial Examples (AE): perturbed input samples (e.g. malware) can mislead ML to produce an adversary’s desired output (e.g. benign class label). AEs against ML models are broadly studied in the computer vision domain where the adversary perturbs the pixel values of an image such that the change is not perceptible, but the resulting image is misclassified by the model. We investigate the effectiveness of attack techniques proposed in the image domain to attack ML classifiers in the context of mobile malware detection. Since the feature vector representation of samples is often used in ML, a simplified evaluation of ML classifiers’ robustness to AEs is to study feature-based attack models, where the adversary perturbs the input features. We compare the methods, trade-offs, and gaps for such attack models and show that generative models (e.g. GANs) outperform a selection of existing attacks in terms of attack success rate but apply large distortion to the original sample. We also describe how we use the generated samples for increasing a classifier’s robustness through adversarial training.","PeriodicalId":131639,"journal":{"name":"2020 30th International Telecommunication Networks and Applications Conference (ITNAC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134106245","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: 0

Network Anomaly Detection Using LightGBM: A Gradient Boosting Classifier 基于LightGBM的网络异常检测:梯度增强分类器

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315049

Md. Khairul Islam, Prithula Hridi, Md. Shohrab Hossain, Husnu S. Narman

{"title":"Network Anomaly Detection Using LightGBM: A Gradient Boosting Classifier","authors":"Md. Khairul Islam, Prithula Hridi, Md. Shohrab Hossain, Husnu S. Narman","doi":"10.1109/ITNAC50341.2020.9315049","DOIUrl":"https://doi.org/10.1109/ITNAC50341.2020.9315049","url":null,"abstract":"Anomaly detection systems are significant in recognizing intruders or suspicious activities by detecting unseen and unknown attacks. In this paper, we have worked on a benchmark network anomaly detection dataset UNSW-NB15, that reflects modern-day network traffic. Previous works on this dataset either lacked a proper validation approach or followed only one evaluation setup which made it difficult to compare their contributions with others using the same dataset but with different validation steps. In this paper, we have used a machine learning classifier LightGBM to perform binary classification on this dataset. We have presented a thorough study of the dataset with feature engineering, preprocessing, feature selection. We have evaluated the performance of our model using different experimental setups (used in several previous works) to clearly evaluate and compare with others. Using ten-fold cross-validation on the train, test, and combined (training and test) dataset, our model has achieved 97.21%, 98.33%, and 96.21% f1_scores, respectively. Also, the model fitted only on train data, achieved 92.96% f1_score on the separate test data. So our model also provides significant performance on unseen data. We have presented complete comparisons with the prior arts using all performance metrics available on them. And we have also shown that our model outperformed them in most metrics and thus can detect network anomalies better.","PeriodicalId":131639,"journal":{"name":"2020 30th International Telecommunication Networks and Applications Conference (ITNAC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128284501","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: 9

An Efficient Machine Learning Algorithm For Spatial Tracking Of Correlated Signals In Wireless Sensor Field 一种无线传感器领域相关信号空间跟踪的高效机器学习算法

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315016

H. Alasti

{"title":"An Efficient Machine Learning Algorithm For Spatial Tracking Of Correlated Signals In Wireless Sensor Field","authors":"H. Alasti","doi":"10.1109/ITNAC50341.2020.9315016","DOIUrl":"https://doi.org/10.1109/ITNAC50341.2020.9315016","url":null,"abstract":"An efficient machine learning algorithm based on stochastic gradient is proposed and discussed for spatial tracking of correlated spatial signals from the sensor observations in wireless sensor field. The proposed algorithm can be used for environmental monitoring applications such as efficient temporal monitoring of temperature in hot island, or efficient monitoring of the distribution of pollutant gasses in wide areas for example terrain of large cities, etc. The proposed algorithm is computationally efficient and is low cost. In this paper the number of reporting sensors in tracking of signal is defined as cost. The spatial signal is compressed into a number of its isocontours at specific levels and the sensors whose sensor readings are in given margin of these contour levels, report their sensor readings to the fusion center (FC). The algorithm is done in two phases of spatial modeling and spatial tracking, where it uses the correlation between the spatial signal before and after variation of the spatial signal and updates the new set of contour levels for spatial tracking. The proposed machine learning algorithm finds the modeling parameters during the spatial modeling phase, and then updates them in spatial tracking phase. The performance analysis of the proposed algorithm shows that it successfully tracks the spatial variations of the signal at low cost with similar modeling performance to the spatial modeling.","PeriodicalId":131639,"journal":{"name":"2020 30th International Telecommunication Networks and Applications Conference (ITNAC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129188264","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: 2

An End-to-End Medical Emergency Response System to Support Elderly People 支援长者的端到端医疗紧急应变系统

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315129

Md. Akbar Hossain, S. K. Ray, Seyed Reza Shahamiri, M. D. Ahmed, G. Singh, Rose Arts

{"title":"An End-to-End Medical Emergency Response System to Support Elderly People","authors":"Md. Akbar Hossain, S. K. Ray, Seyed Reza Shahamiri, M. D. Ahmed, G. Singh, Rose Arts","doi":"10.1109/ITNAC50341.2020.9315129","DOIUrl":"https://doi.org/10.1109/ITNAC50341.2020.9315129","url":null,"abstract":"This paper proposes the concept and preliminary design of an end-to-end medical emergency response system (EEMERS) to support and help elderly people in the community who live alone. The system integrates the informal caregivers, like the neighbors, friends, and family, with the traditional formal caregivers, such as the paramedics, ambulance and medical professionals. The informal caregivers act as the first responders to attend a patient in case of a medical emergency situation before the arrival of an ambulance or other medical services. An overview of the different modules of the EEMERS, the technological details and the end-to-end process flow of the system are discussed in this work. Moreover, the selection of the most appropriate informal caregiver to attend a medical emergency situation depends on a list of pre-defined contexts and is an important part of EEMERS. This work also discusses the preliminary validation results of the informal caregiver selection based on three machine learning algorithms, namely, Logistic Regression, Support Vector Machine, Nave Bayes. Finally, the paper provides a brief overview of the basic proof-of-concept implementation of the system.","PeriodicalId":131639,"journal":{"name":"2020 30th International Telecommunication Networks and Applications Conference (ITNAC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114718145","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: 0

MLIDS: Handling Raw High-Dimensional CAN Bus Data Using Long Short-Term Memory Networks for Intrusion Detection in In-Vehicle Networks MLIDS:利用长短期记忆网络处理原始高维CAN总线数据，用于车载网络入侵检测

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315024

Araya Kibrom Desta, Shuji Ohira, Ismail Arai, K. Fujikawa

引用次数: 10

Software-Defined Vehicular Networks: A Cooperative Approach for Computational Offloading 软件定义的车辆网络:一种计算卸载的协作方法

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315146

S. Shah, M. Gregory, Shuo Li

{"title":"Software-Defined Vehicular Networks: A Cooperative Approach for Computational Offloading","authors":"S. Shah, M. Gregory, Shuo Li","doi":"10.1109/ITNAC50341.2020.9315146","DOIUrl":"https://doi.org/10.1109/ITNAC50341.2020.9315146","url":null,"abstract":"Conventional vehicular systems are gradually evolving into intelligent transportation systems. Multi-access Edge Computing (MEC) has become an important component of the software-defined vehicular networks that benefit from the introduction of reliable and low latency 5G networks and Wi-Fi. MEC offers a variety of smart services closer to the access networks leveraged by vehicles. The intelligent and connected vehicles demand access to compute-intensive applications that require compute and storage resources in addition to what is available in vehicles today. The computational burden on the connected vehicles can be significantly reduced by offloading to the MEC nodes and to the cloud. However, existing computational offloading schemes are challenged by the fast-moving vehicles, frequent handovers and subtle differences to the scenarios designed to support mobile phone handovers. The paper presents a software-defined vehicular edge computing architecture that copes with the mobility challenges by utilising the Software Defined Networking paradigm to perform traffic flow management for information collection and intelligent management of connected vehicles and networks.","PeriodicalId":131639,"journal":{"name":"2020 30th International Telecommunication Networks and Applications Conference (ITNAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116550186","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: 0

Energy-Efficient Systems for Smart Sensor Communications 智能传感器通信的节能系统

2020 30th International Telecommunication Networks and Applications Conference (ITNAC) Pub Date : 2020-11-25 DOI: 10.1109/ITNAC50341.2020.9315030

Z. M. Hussain

{"title":"Energy-Efficient Systems for Smart Sensor Communications","authors":"Z. M. Hussain","doi":"10.1109/ITNAC50341.2020.9315030","DOIUrl":"https://doi.org/10.1109/ITNAC50341.2020.9315030","url":null,"abstract":"A wireless sensor network (WSN) is a communication network with ad hoc configuration consisting of a number of tiny, low-power, low-cost sensors which are normally distributed in a decentralized fashion and have limited processing capability. WSNs have found a wide range of applications such as industrial process control, healthcare monitoring, surveillance, forest fire detection, natural disaster detection, target tracking, among many other applications. It is known that WSNs are resource-constrained, hence, energy efficiency is crucial for all applications of WSNs to extend the life span of the sensors' batteries. The most energy consuming operation in WSN is data communication, hence, it is important to reduce amount of data transmission through WSNs without significantly affecting the transferred information. In this paper we will focus on two directions of data-efficient signal representations that are expected to provide WSNs with sufficient energy control. The first direction is the use of intelligent short word-length (SWL) systems via embedded sigma-delta modulation, and the second direction is to use compressive sensing (CS) with chaotic sequences. If security is a factor, then CS via chaos can support secure communication in addition to its main function as a technique for data compression.","PeriodicalId":131639,"journal":{"name":"2020 30th International Telecommunication Networks and Applications Conference (ITNAC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120979502","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: 9