2021 55th Annual Conference on Information Sciences and Systems (CISS)最新文献_第2页

AoI-Driven Statistical Delay and Error-Rate Bounded QoS Provisioning for URLLC in the Finite Blocklength Regime 有限块长度下URLLC的aoi驱动统计延迟和错误率有界QoS提供

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400231

Xi Zhang, Jingqing Wang, H. Poor

{"title":"AoI-Driven Statistical Delay and Error-Rate Bounded QoS Provisioning for URLLC in the Finite Blocklength Regime","authors":"Xi Zhang, Jingqing Wang, H. Poor","doi":"10.1109/CISS50987.2021.9400231","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400231","url":null,"abstract":"Inspired by the new and dominating traffic services - ultra-reliable and low latency communications (URLLC), finite blocklength coding (FBC) has been developed to support delay and error-rate bounded quality-of-services (QoS) provisioning for time-sensitive wireless applications by using short-packet data communications. On the other hand, the age of information (AoI) has recently emerged as a new dimension of QoS performance metric in terms of the freshness of updated information. Since the status updates normally consist only of a small number of information bits but warrant ultra-low latency, exploring AoI in the finite blocklength regime creates another promising solution for supporting URLLC services. However, how to efficiently integrate and implement the above new techniques for statistical delay and error-rate bounded QoS provisioning in the finite blocklength regime has neither been well understood nor thoroughly studied. To overcome these challenges, we propose the AoI-driven statistical delay and error-rate bounded QoS provisioning schemes which leverage the AoI technique as a key QoS performance metric to efficiently support URLLC in the finite blocklength regime. First, we build up the AoI-metric based modeling frameworks in the finite blocklength regime. Second, we apply the stochastic network calculus (SNC) to characterize the upper-bounded peak AoI violation probability. Third, we jointly optimize the peak AoI violation probability and ∊-effective capacity and characterize their tradeoff in supporting statistical delay and error-rate bounded QoS provisioning for URLLC. Finally, we conduct the extensive simulations to validate and evaluate our developed schemes in the finite blocklength regime.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115116575","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

Anticipatory Thinking: A Testing and Representation Challenge for Self-Driving Cars 预见性思维:自动驾驶汽车的测试和表征挑战

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400212

Leilani H. Gilpin

引用次数: 2

Attack Detection and Countermeasures for Autonomous Navigation 自主导航攻击检测与对策

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400224

Md Tanvir Arafin, K. Kornegay

{"title":"Attack Detection and Countermeasures for Autonomous Navigation","authors":"Md Tanvir Arafin, K. Kornegay","doi":"10.1109/CISS50987.2021.9400224","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400224","url":null,"abstract":"Advances in artificial intelligence, machine learning, and robotics have profoundly impacted the field of autonomous navigation and driving. However, sensor spoofing attacks can compromise critical components and the control mechanisms of mobile robots. Therefore, understanding vulnerabilities in autonomous driving and developing countermeasures remains imperative for the safety of unmanned vehicles. Hence, we demonstrate cross-validation techniques for detecting spoofing attacks on the sensor data in autonomous driving in this work. First, we discuss how visual and inertial odometry (VIO) algorithms can provide a root-of-trust during navigation. Then, we develop examples for sensor data spoofing attacks using the open-source driving dataset. Next, we design an attack detection technique using VIO algorithms that cross-validates the navigation parameters using the IMU and the visual data. Following, we consider hardware-dependent attack survival mechanisms that support an autonomous system during an attack. Finally, we also provide an example of spoofing survival technique using on-board hardware oscillators. Our work demonstrates the applicability of classical mobile robotics algorithms and hardware security primitives in defending autonomous vehicles from targeted cyber attacks.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127275865","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: 3

Localizing Power-Grid Forced Oscillations Based on Harmonic Analysis of Synchrophasor Data 基于同步相量数据谐波分析的电网强迫振荡定位

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400267

Sandip Roy, Wenyun Ju, Neeraj Nayak, B. Lesieutre

引用次数: 7

On the Smoothing of Deep Networks 关于深度网络的平滑

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400285

Vincent Roulet, Zaïd Harchaoui

引用次数: 0

Deep Learning-Based Anomaly Detection in LAN from Raw Network Traffic Measurement 基于深度学习的局域网原始网络流量异常检测

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400241

Yuwei Sun, H. Ochiai, H. Esaki

{"title":"Deep Learning-Based Anomaly Detection in LAN from Raw Network Traffic Measurement","authors":"Yuwei Sun, H. Ochiai, H. Esaki","doi":"10.1109/CISS50987.2021.9400241","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400241","url":null,"abstract":"The digitalization occurring in various industries is bringing more information transmitted through networks. More resilient and efficient network traffic monitoring systems are in high demand to safeguard network flows. In this article, we presented a combined approach of anomaly detection in LAN based on raw network traffic observation and measurement, the collected data being converted to regulated chunks of 480 bits. A network traffic dataset including multi-type anomalies from a honeypot device in LAN was employed, with a total of two weeks' data. By further integrating the representation with supervised learning and knowledge-based labeling methods, we aim to classify raw network traffic thus detecting anomaly from raw data measurement without using manually crafted features. We conducted the model training against accuracy and evaluated the scheme based on a separated validation set against a metric of precision. Finally, we achieved a validation precision score of 0.980 for detecting ARP flooding, a score of 0.801 for detecting malicious SMB, and a score of 0.815 for detecting TCP SYN flooding respectively.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125466278","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: 4

Optimizing Sensor Locations to Improve the Worst Case Detection Performance of Sensor Detection Systems 优化传感器位置以提高传感器检测系统的最坏情况检测性能

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400220

Ryan Vegrzyn, Benedito J. B. Fonseca

引用次数: 1

A Hybrid Approach to Joint Estimation of MIMO Channel and Antenna Impedance Matrices 一种MIMO信道和天线阻抗矩阵联合估计的混合方法

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400229

Shaohan Wu

引用次数: 4

Quantitative Analysis System for Bacterial Cells in SEM Image using Deep Learning 基于深度学习的SEM图像细菌细胞定量分析系统

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400322

Yasuki Kakishita, Arkadip Ray, Hideharu Hattori, A. Hisada, Y. Ominami, J. Baudoin, J. B. Khalil, D. Raoult

{"title":"Quantitative Analysis System for Bacterial Cells in SEM Image using Deep Learning","authors":"Yasuki Kakishita, Arkadip Ray, Hideharu Hattori, A. Hisada, Y. Ominami, J. Baudoin, J. B. Khalil, D. Raoult","doi":"10.1109/CISS50987.2021.9400322","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400322","url":null,"abstract":"In this paper we propose a system to analyze bacteria from a given Scanning Electron Microscope (SEM) image of the bacterial sample. Thousands of bacteria lives in the human gut and recent studies have shown that the quantitative features of the microbiome, such as co-existence ratio of different bacteria, can be indicative of the health condition in humans. Conventional bacteria analysis methods using microscopic images, can only be used to examine a single bacteria colony. In contrast, we propose a novel system to morphologically analyze the bacteria from SEM images. By this, we expect to enable a rapid analysis of the human gut bacteria ratio, in which various type of bacteria are mixed. However, to achieve an automatic and accurate count of the bacteria in the SEM images, it is important to accurately identify the bacteria regions, separate the connected bacteria regions and classify them. To address this, we propose a system that includes a segmentation, separation and a classification module. Our system achieves more than 90% recall for all of original three datasets that we have created. Subsequently, we show the comparison results between another state-of-the-art segmentation method and our system, and we empirically report that our system has a better performance.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"50 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113956800","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

A Spike-based Cellular-Neural Network Architecture for Spatiotemporal filtering 一种基于脉冲的细胞神经网络时空滤波结构

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400308

Jonah P. Sengupta, M. Villemur, A. Andreou

引用次数: 2