2022 IEEE International Symposium on Technologies for Homeland Security (HST)最新文献_第2页

Defining Time-Varying Metrics of Task Conflict in Human/Robot Teams Using Simulated Agents 用模拟agent定义人/机器人团队任务冲突的时变度量

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10025432

Audrey Balaska, J. Rife

{"title":"Defining Time-Varying Metrics of Task Conflict in Human/Robot Teams Using Simulated Agents","authors":"Audrey Balaska, J. Rife","doi":"10.1109/HST56032.2022.10025432","DOIUrl":"https://doi.org/10.1109/HST56032.2022.10025432","url":null,"abstract":"In this paper, we consider a simulated search & rescue application as context for introducing a novel monitoring concept that continually assesses the level of task conflict for a human-robot team. We define task conflict to mean inconsistent mental models of the task, including information about the agents, environment, and the task itself. In order to demonstrate a proof of concept, we used an agent-based modeling approach that simulates information fusion using a Bayesian algorithm. To represent nominal differences in the inferences made by each agent, we randomly perturbed the inputs to the Bayesian algorithm, with levels of randomization chosen to reflect the relevant existing literature regarding human performance. Using simulated nominal data, we generated a time-dependent conflict threshold. Then, this threshold was tested by injecting simulated anomalies and evaluating how often conflict was detected. The high resulting detection rate and the evidenced robustness of the simulation to parameter variation suggest the potential of the monitoring approach for future human-subject testing.","PeriodicalId":162426,"journal":{"name":"2022 IEEE International Symposium on Technologies for Homeland Security (HST)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127712509","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

Secure, Robust, Wide Area Communication for the Electric Power Industry 电力工业的安全、稳健、广域通信

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10024986

M. Adamiak, H. Falk, Chuck DuBose

引用次数: 0

Disruption-Robust Community Detection Using Consensus Clustering in Complex Networks 基于共识聚类的复杂网络中断鲁棒社区检测

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10024983

Md Taufique Hussain, Arif M. Khan, A. Azad, Samrat Chatterjee, R. Brigantic, M. Halappanavar

引用次数: 0

Hyperdimensional Computing Encoding Schemes for Improved Image Classification 改进图像分类的超维计算编码方案

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10024980

Victor Miranda, Olivia G. d'Aliberti

引用次数: 2

Leveraging Intermediate Node Evaluation to Secure Approximate Computing for AI Applications 利用中间节点评估来确保人工智能应用的近似计算

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10025430

Pruthvy Yellu, N. Chennagouni, Qiaoyan Yu

{"title":"Leveraging Intermediate Node Evaluation to Secure Approximate Computing for AI Applications","authors":"Pruthvy Yellu, N. Chennagouni, Qiaoyan Yu","doi":"10.1109/HST56032.2022.10025430","DOIUrl":"https://doi.org/10.1109/HST56032.2022.10025430","url":null,"abstract":"Artificial Intelligence (AI) has been widely applied to homeland security to speed up target recognition, threat analysis, and decision-making. The intensive computation required by AI approaches could be an obstacle that prevents AI from achieving real-time responses. Approximate computing techniques that leverage accuracy for better performance have the potential to accelerate the computation in AI. However, since the AI techniques are applied in homeland security applications, which have high requirements for piracy and security, it is critical to deploy the approximation methods in a secure way. In this work, we analyze the stealthiness of the attacks in an approximate computing system and reveal that the primary outputs are not the best location to detect the presence of attacks. We propose an intermediate node evaluation-based attack detection (INEAD) method to examine the attacks in approximate computing systems. Our case studies on approximate Finite Impulse Response (FIR) filter and artificial neural network (ANN) show that intermediate nodes are better position for attack detection than the primary output. We observe that the attack detection speed has increased by 80% when INEAD method is deployed in FIR filter. The compile time for attack detection can be reduced by 52.7% for the case of ANN when our INEAD method is deployed.","PeriodicalId":162426,"journal":{"name":"2022 IEEE International Symposium on Technologies for Homeland Security (HST)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128319928","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

Human Trafficking Interdiction Problem: A Data Driven Approach to Modeling and Analysis 阻止人口贩运问题:数据驱动的建模和分析方法

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10025431

A. Sen, S. Adeniye, K. Basu, S. Ravishankar, J. Sefair, D. Roe-Sepowitz, E. Helderop, T. Grubesic, A. B. Sen

引用次数: 0

A Comprehensive 2D + 3D Dataset for Benchmarking Hyperspectral Imaging Systems 一个全面的2D + 3D数据集的基准高光谱成像系统

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10024982

Abigail Stone, S. P. Rao, Srijith Rajeev, K. Panetta, S. Agaian

{"title":"A Comprehensive 2D + 3D Dataset for Benchmarking Hyperspectral Imaging Systems","authors":"Abigail Stone, S. P. Rao, Srijith Rajeev, K. Panetta, S. Agaian","doi":"10.1109/HST56032.2022.10024982","DOIUrl":"https://doi.org/10.1109/HST56032.2022.10024982","url":null,"abstract":"Hyperspectral images are represented by numerous narrow wavelength bands in the visible and near-infrared parts of the electromagnetic spectrum. As hyperspectral imagery gains traction for general computer vision tasks, there is an increased need for large and comprehensive datasets for use as training data. Recent advancements in sensor technology allow us to capture hyperspectral data cubes at higher spatial and temporal resolution. However, there are few publicly available multi-purpose hyperspectral datasets captured in outdoor terrestrial conditions. Furthermore, there are no publicly available datasets that include 3D mesh representations of objects captured in outdoor scenes. This article introduces the first hyperspectral dataset of 3D objects and terrestrial outdoor scenes, the Tufts Outdoor Hyper-spectral Dataset (TOHS Dataset). The dataset includes 100 2D + 3D hyperspectral scenes, each containing 164 spectral bands. The contributions of this work are 1) Detailed description of the content, acquisition procedure, and benchmark results on state-of-the-art neural networks for 3D object scenes in the Tufts Hyperspectral Database; 2) The first-of-its-kind hyperspectral 3D dataset of outdoor objects that will be publicly available to researchers worldwide, which will allow for the assessment and creation of more robust, consistent, and adaptable AI algorithms; and 3) a comprehensive and up-to-date review on hyperspectral systems and datasets.","PeriodicalId":162426,"journal":{"name":"2022 IEEE International Symposium on Technologies for Homeland Security (HST)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115666170","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

A Distributed Privacy-Preserving Integrity Verification Framework for the Smart Grid 面向智能电网的分布式隐私保护完整性验证框架

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10025444

Gaurav S. Wagh, S. Mishra

{"title":"A Distributed Privacy-Preserving Integrity Verification Framework for the Smart Grid","authors":"Gaurav S. Wagh, S. Mishra","doi":"10.1109/HST56032.2022.10025444","DOIUrl":"https://doi.org/10.1109/HST56032.2022.10025444","url":null,"abstract":"Smart grid functionalities, such as real-time monitoring and load balancing, require smart metering data collection at frequent time intervals. There are several threats to this data collection process, including passive threats to customers' privacy and active threats to metering data integrity. Customer privacy can be breached with the fine grained metering data collection, and without appropriate measures for integrity verification, the metering data can be exploited to hamper the smart grid functionalities. Distributed privacy-preserving frameworks are more robust than centralized frameworks against privacy threats. Several distributed privacy-preserving frameworks for smart metering data exist in the literature. However, these frameworks assume a semi-honest threat model that does not consider threats to data integrity. This paper introduces a distributed framework under a malicious adversarial model. The proposed framework is capable of verifying metering data's integrity while maintaining customer privacy. We evaluate our framework's performance via simulation and show its feasibility for real-world deployments. We also evaluate the framework's resilience to active and passive attacks, followed by a comparative analysis with existing related frameworks in the literature.","PeriodicalId":162426,"journal":{"name":"2022 IEEE International Symposium on Technologies for Homeland Security (HST)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131488627","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

Secure Robotic Vehicles: Vulnerabilities and Mitigation Strategies 安全机器人车辆:漏洞和缓解策略

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10025449

A. Hristozov, Eric Dietz, E.T. Matson, J. Gallagher, M. Rogers

{"title":"Secure Robotic Vehicles: Vulnerabilities and Mitigation Strategies","authors":"A. Hristozov, Eric Dietz, E.T. Matson, J. Gallagher, M. Rogers","doi":"10.1109/HST56032.2022.10025449","DOIUrl":"https://doi.org/10.1109/HST56032.2022.10025449","url":null,"abstract":"Robotic vehicles are becoming more widespread and used in many industries, including agriculture, manufacturing, and defense. They are safety-critical systems because of the fact that they are mobile, autonomous, and can operate in hazardous environments. The focus on robotic systems in the last several decades has been to add new complex functionality, in many cases using artificial intelligence. Many of these new technologies are fairly sophisticated and expose robotic vehicles to new vulnerabilities, especially when vehicles need to operate autonomously. Security and safety are connected and preventing intentional attacks on mobile robots improves safety and allows robots to complete their missions in challenging and hostile environments. In addition, robots that move can and need to adapt and counteract adversarial attacks and adapt to sensor and actuator faults. Robotic vehicles are also real-time systems; their ability to function is determined by their ability to maintain these characteristics all the time. In this work, we present the major classes of attacks on robotic vehicles and analyze the existing and propose some new mitigation strategies to counteract the attacks. Our scope is on robotic vehicles in general, with a specialized focus on UAVs as a class of vehicles receiving more attention and presenting significant security challenges. We discuss strategies based on simplex architecture, enforcers, partitioning, redundancy, self-adaptation, and dynamic architectures during run-time.","PeriodicalId":162426,"journal":{"name":"2022 IEEE International Symposium on Technologies for Homeland Security (HST)","volume":"420 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126708735","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

A Framework for Using Ensemble Species Distribution Models for Geographic Attribution in Forensic Palynology 综合物种分布模型在法医孢粉学地理归因中的应用框架

2022 IEEE International Symposium on Technologies for Homeland Security (HST) Pub Date : 2022-11-14 DOI: 10.1109/HST56032.2022.10025427

Haoyu Wang, Jennifer A. Miller, T. Grubesic, Shalene Jha

引用次数: 1