2021 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第9页

Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response 学习大地理区域事件预测模型用于应急响应

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-06-15 DOI: 10.1109/SMARTCOMP52413.2021.00091

S. Vazirizade, Ayan Mukhopadhyay, Geoffrey Pettet, S. E. Said, H. Baroud, A. Dubey

{"title":"Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response","authors":"S. Vazirizade, Ayan Mukhopadhyay, Geoffrey Pettet, S. E. Said, H. Baroud, A. Dubey","doi":"10.1109/SMARTCOMP52413.2021.00091","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00091","url":null,"abstract":"Emergency Response Management (ERM) necessitates the use of models capable of predicting the spatial-temporal likelihood of incident occurrence. These models are used for proactive stationing in order to reduce overall response time. Traditional methods simply aggregate past incidents over space and time; such approaches fail to make useful short-term predictions when the spatial region is large and focused on fine-grained spatial entities like interstate highway networks. This is partially due to the sparsity of incidents with respect to space and time. Further, accidents are affected by several covariates. Collecting, cleaning, and managing multiple streams of data from various sources is challenging for large spatial areas. In this paper, we highlight how this problem is being solved in collaboration with the Tennessee Department of Transportation (TDOT) to improve ERM in the state of Tennessee. Our pipeline, based on a combination of synthetic resampling, clustering, and data mining techniques, can efficiently forecast the spatio-temporal dynamics of accident occurrence, even under sparse conditions. Our pipeline uses data related to roadway geometry, weather, historical accidents, and traffic to aid accident forecasting. To understand how our forecasting model can affect allocation and dispatch, we improve and employ a classical resource allocation approach. Experimental results show that our approach can noticeably reduce response times and the number of unattended incidents in comparison to current approaches followed by first responders. The developed pipeline is efficacious, applicable in practice, and open-source.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126742674","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

OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices OODIn:异构移动设备的优化设备上推理框架

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-06-08 DOI: 10.1109/SMARTCOMP52413.2021.00021

Stylianos I. Venieris, I. Panopoulos, I. Venieris

{"title":"OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices","authors":"Stylianos I. Venieris, I. Panopoulos, I. Venieris","doi":"10.1109/SMARTCOMP52413.2021.00021","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00021","url":null,"abstract":"Radical progress in the field of deep learning (DL) has led to unprecedented accuracy in diverse inference tasks. As such, deploying DL models across mobile platforms is vital to enable the development and broad availability of the next-generation intelligent apps. Nevertheless, the wide and optimised deployment of DL models is currently hindered by the vast system heterogeneity of mobile devices, the varying computational cost of different DL models and the variability of performance needs across DL applications. This paper proposes OODIn, a framework for the optimised deployment of DL apps across heterogeneous mobile devices. OODIn comprises a novel DL-specific software architecture together with an analytical framework for modelling DL applications that: (1) counteract the variability in device resources and DL models by means of a highly parametrised multi-layer design; and (2) perform a principled optimisation of both model- and system-level parameters through a multi-objective formulation, designed for DL inference apps, in order to adapt the deployment to the user-specified performance requirements and device capabilities. Quantitative evaluation shows that the proposed framework consistently outperforms status-quo designs across heterogeneous devices and delivers up to 4.3× and 3.5× performance gain over highly optimised platform- and model-aware designs respectively, while effectively adapting execution to dynamic changes in resource availability.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123164125","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: 11

Analyzing Machine Learning Approaches for Online Malware Detection in Cloud 云环境下在线恶意软件检测的机器学习方法分析

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-05-19 DOI: 10.1109/SMARTCOMP52413.2021.00046

Jeffrey Kimmell, Mahmoud Abdelsalam, Maanak Gupta

{"title":"Analyzing Machine Learning Approaches for Online Malware Detection in Cloud","authors":"Jeffrey Kimmell, Mahmoud Abdelsalam, Maanak Gupta","doi":"10.1109/SMARTCOMP52413.2021.00046","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00046","url":null,"abstract":"The variety of services and functionality offered by various cloud service providers (CSP) have exploded lately. Utilizing such services has created numerous opportunities for enterprises infrastructure to become cloud-based and, in turn, assisted the enterprises to easily and flexibly offer services to their customers. The practice of renting out access to servers to clients for computing and storage purposes is known as Infrastructure as a Service (IaaS). The popularity of IaaS has led to serious and critical concerns with respect to the cyber security and privacy. In particular, malware is often leveraged by malicious entities against cloud services to compromise sensitive data or to obstruct their functionality. In response to this growing menace, malware detection for cloud environments has become a widely researched topic with numerous methods being proposed and deployed. In this paper, we present online malware detection based on process level performance metrics, and analyze the effectiveness of different baseline machine learning models including, Support Vector Classifier (SVC), Random Forest Classifier (RFC), K-Nearest Neighbor (KNN), Gradient Boosted Classifier (GBC), Gaussian Naive Bayes (GNB) and Convolutional Neural Networks (CNN). Our analysis conclude that neural network models can most accurately detect the impact malware have on the process level features of virtual machines in the cloud, and therefore are best suited to detect them. Our models were trained, validated, and tested by using a dataset of 40,680 malicious and benign samples. The dataset was complied by running different families of malware (collected from VirusTotal) in a live cloud environment and collecting the process level features.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128232026","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: 18

Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors 基于储层的射频数据边缘训练，以提供智能高效的物联网频谱传感器

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-04-01 DOI: 10.1109/SMARTCOMP52413.2021.00023

S. Kokalj-Filipovic, P. Toliver, William Johnson, Rob Miller

{"title":"Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors","authors":"S. Kokalj-Filipovic, P. Toliver, William Johnson, Rob Miller","doi":"10.1109/SMARTCOMP52413.2021.00023","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00023","url":null,"abstract":"Current radio frequency (RF) sensors at the Edge lack the computational resources to support practical, in-situ training for intelligent spectrum monitoring. This is true for sensor data classification in general. We propose a solution via Deep Delay Loop Reservoir Computing (DLR), a processing architecture that supports general machine learning algorithms on compact mobile devices by leveraging delay-loop reservoir computing in combination with innovative electro-optical hardware. With both digital and photonic realizations of our design of the loops, DLR delivers reductions in form factor, hardware complexity and latency, compared to the State-of-the-Art (SoA). The main impact of the reservoir is to project the input data into a higher dimensional space of reservoir state vectors in order to linearly separate the input classes. Once the classes are well separated, traditionally complex, power-hungry classification models are no longer needed for the learning process. Yet, even with simple classifiers based on Ridge regression (RR), the complexity grows at least quadratically with the input size. Hence, the hardware reduction required for training on compact devices is in contradiction with the large dimension of state vectors. DLR employs a RR-based classifier to exceed the SoA accuracy, while further reducing power consumption by leveraging the architecture of parallel (split) loops. We present DLR architectures composed of multiple smaller loops whose state vectors are linearly combined to create a lower dimensional input into Ridge regression. We demonstrate the advantages of using DLR for two distinct applications: RF Specific Emitter Identification (SEI) for IoT authentication, and wireless protocol recognition for IoT situational awareness.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121542499","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

Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems 基于深度rbf网络的汽车信息物理系统异常检测

2021 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2021-03-25 DOI: 10.1109/SMARTCOMP52413.2021.00028

Matthew P. Burruss, Shreyas Ramakrishna, A. Dubey

{"title":"Deep-RBF Networks for Anomaly Detection in Automotive Cyber-Physical Systems","authors":"Matthew P. Burruss, Shreyas Ramakrishna, A. Dubey","doi":"10.1109/SMARTCOMP52413.2021.00028","DOIUrl":"https://doi.org/10.1109/SMARTCOMP52413.2021.00028","url":null,"abstract":"Deep Neural Networks (DNNs) are widely used in automotive Cyber-Physical Systems (CPSs) to implement autonomy related tasks. However, these networks have exhibited erroneous predictions to anomalous inputs that manifest either due to Out-of-Distribution (OOD) data or adversarial attacks. To detect these anomalies, a separate DNN called assurance monitor is used in parallel to the controller DNN, increasing the resource burden and latency. We hypothesize that a single network that can perform controller predictions and anomaly detection is necessary to reduce the resource requirements. Deep-Radial Basis Function (RBF) networks provide a rejection class alongside the class predictions, which can be used for anomaly detection. However, the use of RBF activation functions limits the applicability of these networks to only classification tasks. In this paper, we discuss the steps involved in detecting anomalies in CPS regression and classification tasks. Further, we design deep-RBF networks using popular DNNs such as NVIDIA DAVE-II and ResNet20 and then use the resulting rejection class for detecting physical and data poison adversarial attacks. We show that the deep-RBF network can effectively detect these attacks with limited resource requirements.","PeriodicalId":330785,"journal":{"name":"2021 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134539001","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