2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)最新文献_第6页

Model of a Virtual Firewall Based on Stochastic Petri Nets 基于随机Petri网的虚拟防火墙模型

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548250

Luis Zabala, Rubén Solozabal, A. Ferro, Bego Blanco

{"title":"Model of a Virtual Firewall Based on Stochastic Petri Nets","authors":"Luis Zabala, Rubén Solozabal, A. Ferro, Bego Blanco","doi":"10.1109/NCA.2018.8548250","DOIUrl":"https://doi.org/10.1109/NCA.2018.8548250","url":null,"abstract":"This paper presents a modeling of a virtual firewall, based on Stochastic Petri Nets (SPN) to analyze the performance in terms of throughput and delay. The firewall is part of a virtualized multitenant environment. To ensure that every tenant is protected against intrusion attempts, per tenant firewalls are a must. With this protection, each virtual environment located on a multitenant cloud is isolated and data is protected in case of an attack originating from within the cloud. Petri nets introduce some interactions that we have in the real system; we refer to sequential and parallel processing, concurrency, limited resources and mutual exclusion in shared resource access. The modeling is assessed by simulation and results are compared in different scenarios. The modeling also allows us to evaluate necessary resources that must be allocated to achive the desired throughput. Once the impact of key parameters on the global system performance is analyzed, results under several scenarios indicate that our proposal would succeed in an efficient resource allocation scheme in terms of throughput and delay.","PeriodicalId":268662,"journal":{"name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123000588","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

Device and User Management for Smart Homes 智能家居的设备和用户管理

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548328

Alejandro Mazuera-Rozo, S. Rueda

引用次数: 1

Cluster Load Estimation for Stateless Schedulers in Datacenters 数据中心中无状态调度器的集群负载估计

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548337

R. Alshahrani, H. Peyravi

引用次数: 1

A Practical Application of a Dataset Analysis in an Intrusion Detection System 数据集分析在入侵检测系统中的实际应用

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548316

Diego Fernández, Laura Vigoya, Fidel Cacheda, F. J. Nóvoa, Manuel F. López-Vizcaíno, V. Carneiro

引用次数: 5

Delivery Delay and Mobile Faults 交付延迟和移动故障

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548345

Dimitris Sakavalas, Lewis Tseng

引用次数: 6

A Holistic Approach to Efficient HPC, Networking and Storage for Science 高效HPC、网络和科学存储的整体方法

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548320

A. Bode

引用次数: 0

RadIoT: Radio Communications Intrusion Detection for IoT - A Protocol Independent Approach 物联网无线通信入侵检测——协议独立方法

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548286

Jonathan Roux, E. Alata, G. Auriol, M. Kaâniche, V. Nicomette, Romain Cayre

{"title":"RadIoT: Radio Communications Intrusion Detection for IoT - A Protocol Independent Approach","authors":"Jonathan Roux, E. Alata, G. Auriol, M. Kaâniche, V. Nicomette, Romain Cayre","doi":"10.1109/NCA.2018.8548286","DOIUrl":"https://doi.org/10.1109/NCA.2018.8548286","url":null,"abstract":"Internet-of-Things (IoT) devices are nowadays massively integrated in daily life: homes, factories, or public places. This technology offers attractive services to improve the quality of life as well as new economic markets through the exploitation of the collected data. However, these connected objects have also become attractive targets for attackers because their current security design is often weak or flawed, as illustrated by several vulnerabilities such as Mirai, Blueborne, etc. This paper presents a novel approach for detecting intrusions in smart spaces such as smarthomes, or smartfactories, that is based on the monitoring and profiling of radio communications at the physical layer using machine learning techniques. The approach is designed to be independent of the large and heterogeneous set of wireless communication protocols typically implemented by connected objects such as WiFi, Bluetooth, Zigbee, Bluetooth-Low-Energy (BLE) or proprietary communication protocols. The main concepts of the proposed approach are presented together with an experimental case study illustrating its feasibility based on data collected during the deployment of the intrusion detection approach in a smart home under real-life conditions.","PeriodicalId":268662,"journal":{"name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122323993","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: 14

On the Fly Detection of the Top-K Items in the Distributed Sliding Window Model 分布式滑动窗口模型中Top-K项的动态检测

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548097

E. Anceaume, Yann Busnel, Vasile Cazacu

{"title":"On the Fly Detection of the Top-K Items in the Distributed Sliding Window Model","authors":"E. Anceaume, Yann Busnel, Vasile Cazacu","doi":"10.1109/NCA.2018.8548097","DOIUrl":"https://doi.org/10.1109/NCA.2018.8548097","url":null,"abstract":"This paper presents a new algorithm that detects on the fly the $k$ most frequent items in the sliding window model. This algorithm is distributed among the nodes of the system. It is inspired by a recent and innovative approach, which consists in associating a stochastic value correlated with the item's frequency instead of trying to estimate its number of occurrences. This stochastic value corresponds to the number of consecutive heads in coin flipping until the first tail occurs. The original approach was to retain just the maximum of consecutive heads obtained by an item, since an item that often occurs will have a higher probability of having a high value. While effective for very skewed data distributions, the correlation is not tight enough to robustly distinguish items with comparable frequencies. To address this important issue, we propose to combine the stochastic approach together with a deterministic counting of items. Specifically, in place of keeping the maximum number of consecutive heads obtained by an item, we count the number of times the coin flipping process of an item has exceeded a given threshold. This threshold is defined by combining theoretical results in leader election and coupon collector problems. Results on simulated data show how impressive is the detection of the top-k items in a large range of distributions.","PeriodicalId":268662,"journal":{"name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134441379","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

Tamper-Proof Incentive Scheme for Mobile Crowdsensing Systems 移动众测系统防篡改激励方案

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548093

Diogo Calado, M. Pardal

引用次数: 3

TorBot Stalker: Detecting Tor Botnets Through Intelligent Circuit Data Analysis 僵尸跟踪:通过智能电路数据分析检测僵尸网络

2018 IEEE 17th International Symposium on Network Computing and Applications (NCA) Pub Date : 2018-11-01 DOI: 10.1109/NCA.2018.8548313

Oluwatobi Fajana, Gareth Owenson, Ella Haig

{"title":"TorBot Stalker: Detecting Tor Botnets Through Intelligent Circuit Data Analysis","authors":"Oluwatobi Fajana, Gareth Owenson, Ella Haig","doi":"10.1109/NCA.2018.8548313","DOIUrl":"https://doi.org/10.1109/NCA.2018.8548313","url":null,"abstract":"Botnets are collections of infected computers that are controlled centrally by a botmaster, often for sending spam or launching denial of service attacks. The task to take down these botnets is often a cat and mouse game with operators frequently changing domains for their control infrastructure. More recently, operators have moved to using Tor, a pseudo-anonymous network for hosting services whereby identification is difficult. Additionally, because connections to the Tor network are encrypted, we cannot use traditional methods like Domain Name System (DNS) and traffic signatures to detect infected hosts. In this paper, we introduce TorBot Stalker: the first mechanism for detecting, de-anonymizing, and destroying Tor botnets. We use machine learning to analyse and fingerprint the timings and frequency of Tor network circuit data when routing botnet traffic, and build a detection mechanism that is able to identify infected hosts at the Tor network border, in real-time, while preserving the privacy of legitimate users. TorBot Stalker can be implemented at any node in the Tor network and can differentiate between botnets and legitimate applications like Internet Relay Chat (IRC) coming from the same host. Experimental data demonstrates an accuracy of 99% with few false positives. We then apply the technique at the entry to the Tor network to measure the fraction of traffic which is for botnet. We observed that Torbot Stalker is able to de-anonymize real botnets in the Tor network and further identify infected hosts and control servers.","PeriodicalId":268662,"journal":{"name":"2018 IEEE 17th International Symposium on Network Computing and Applications (NCA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128102438","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: 8