2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)最新文献_第4页

Implementation and Evaluation of Flow-level Network Simulator for Large-scale ICN Networks 大规模ICN网络流级网络模拟器的实现与评价

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00113

Soma Yamamoto, Ryo Nakamura, H. Ohsaki

引用次数: 0

Reliable Visibility Algorithms for Emergency Stop Systems in Smart Industries 智能工业中紧急停车系统的可靠可见性算法

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00021

Gabriele Capannini, Jane F. Carlson, R. Mellander

引用次数: 0

Harnessing Digital Twin Security Simulations for systematic Cyber Threat Intelligence 利用数字孪生安全模拟系统网络威胁情报

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00129

Marietheres Dietz, Daniel Schlette, G. Pernul

{"title":"Harnessing Digital Twin Security Simulations for systematic Cyber Threat Intelligence","authors":"Marietheres Dietz, Daniel Schlette, G. Pernul","doi":"10.1109/COMPSAC54236.2022.00129","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00129","url":null,"abstract":"Understanding cybersecurity threats, attacks, and incidents is crucial for organizations to perform preventive or re-active measures. Nevertheless, detailed Cyber Threat Intelligence (CTI) is reluctantly shared. Digital twins, the virtual counterparts of real-world assets, offer security simulation capabilities. The simulation of attack scenarios on industrial control systems (ICS) with digital twins yields valuable threat information. In our work, we outline the systematic steps towards a structured threat report starting with digital twin security simulations: We first present the course of action and define formal requirements for framework deployment. We then conduct an attack simulation with a prototypical digital twin application to evaluate our frame-work. Using the STIX2.1 standard, we assist CTI generation by providing utility tools guiding through the process steps. Our experimental results show that a STIX2.1 CTI report can be systematically constructed with the opportunity to customize according to the use case at hand. Adding digital twin security simulations to the list of CTI sources can provide shareable CTI and help organizations improve their security posture.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"60 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120825045","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 4C Model for Hyflex Classrooms Hyflex教室的4C模型

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00029

Henry C. B. Chan, Yi Dou, Yue Jiang, Ping Li

引用次数: 2

Finding Your Feet In Cybersecurity: A Reddit Text Mining Approach 在网络安全中找到你的脚:Reddit文本挖掘方法

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00080

Prerit Datta, Moitrayee Chatterjee

引用次数: 0

BitBooster: Effective Approximation of Distance Metrics via Binary Operations BitBooster:通过二进制操作有效逼近距离度量

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00036

Yorick Spenrath, Marwan Hassani, B. van Dongen

引用次数: 1

Vulnerability Detection in Smart Contracts Using Deep Learning 基于深度学习的智能合约漏洞检测

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00197

Saroj Gopali, Z. Khan, Bipin Chhetri, Bimal Karki, A. Namin

引用次数: 0

Mask and respirator detection: analysis and potential solutions for a frequently ill-conditioned problem 口罩和呼吸器检测:分析和潜在的解决方案，对一个经常生病的问题

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00165

A. C. Marceddu, R. Ferrero, B. Montrucchio

{"title":"Mask and respirator detection: analysis and potential solutions for a frequently ill-conditioned problem","authors":"A. C. Marceddu, R. Ferrero, B. Montrucchio","doi":"10.1109/COMPSAC54236.2022.00165","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00165","url":null,"abstract":"During the coronavirus pandemic, the mask detection problem has become of particular interest. Usually, the goal is to create a system that can detect whether or not a person is wearing a mask or respirator. However, this tends to trivialize a problem that hides a greater complexity. In fact, people wear masks or respirators in various ways, many of which are incorrect. This makes the problem ill-conditioned and creates a bias compared to training cases, with the consequence that these systems have a considerably lower accuracy when used in practice. We claim that focusing on the ways in which a mask can be worn and classifying the problem not as binary but at least as ternary, thus adding an intermediate class containing all those ways in which a mask or respirator can be worn incorrectly, could help address this problem. For this reason, this paper describes and puts to the proof the Ways to Wear a Mask or a Respirator Database (WWMR-DB). It has a fine classification of the most common ways in which a mask or respirator is worn, which can be used to test how mask detection systems work in cases that resemble the real ones more. It was used to test a neural network, the ResNet-152, which was trained on less fine databases, like the Face-Mask Label Dataset and the MaskedFace-Net. The mixed results denote the shortcomings of these databases and the need to enhance them or resort to finer databases.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128665820","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

Mental workload classification from non-invasive fNIRs signals through deep convolutional neural network 基于深度卷积神经网络的非侵入性近红外信号心理负荷分类

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00230

Vamsi Krishna Dhulipalla, Md Abdullah Al Hafiz Khan

{"title":"Mental workload classification from non-invasive fNIRs signals through deep convolutional neural network","authors":"Vamsi Krishna Dhulipalla, Md Abdullah Al Hafiz Khan","doi":"10.1109/COMPSAC54236.2022.00230","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00230","url":null,"abstract":"Classification of mental workload has always been considered a crucial task in the literature related to brain mem-ory. People perform various tasks and have multiple cognitive workloads. This mental workload can be sensed in a non-intrusive way using Functional near-infrared spectroscopy (fNIRS) sig-nals. fNIRS is a photosensitive brain examining method which uses near-infrared spectroscopy to measure aspects of brain functions and activities. In this work, we focus on classifying segmented mental workload from fNIRS signals. We propose a deep convolutional neural (DCNN) network to classify mental workload. We evaluate our model performance using the publicly available large-scale open-access dataset, “Tufts fNIRS to Mental Workload (fNIRS2MW)” that consists of 68 participants per-forming n-back tasks where increased n represents the intensity of the mental workload. Our proposed deep convolutional neural network (DCNN) comprises six convolutional layers. Our DCNN achieves a performance gain of 28 % and 4 % comparing the state-of-the-art models EEGnet and Deep ConvNet, respectively.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129823080","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

Software Engineering in Digital Transformation and Diversity: Preliminary Literature Review 数字化转型与多样性中的软件工程:初步文献综述

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00083

H. Washizaki

引用次数: 1