目前软件应用广泛,对软件可靠性要求越来越高。近几年云计算技术的研究逐渐增多,对于云平台的可靠性技术也有了新的要求。Amazon Web Services(AWS)提供了一整套云计算服务,用户能够构建复杂、可扩展的应用程序。AWS在最小成本情况下,...目前软件应用广泛,对软件可靠性要求越来越高。近几年云计算技术的研究逐渐增多,对于云平台的可靠性技术也有了新的要求。Amazon Web Services(AWS)提供了一整套云计算服务,用户能够构建复杂、可扩展的应用程序。AWS在最小成本情况下,为用户提供了一套构建容错的软件系统平台。在技术和性能等多方面的优势,被业界广泛认可和接受。该文主要研究Amazon Web Services云平台中的核心组件是如何提供可靠性技术的,分别对核心组件Amazon EC2,Amazon Simple Storage(S3),Elastic Storage Block(EBS),Elastic Load Balancing,Auto Scaling进行研究分析,为以后云计算平台的搭建和可靠性技术的研究提供依据。展开更多
Big data is usually unstructured, and many applications require theanalysis in real-time. Decision tree (DT) algorithm is widely used to analyzebig data. Selecting the optimal depth of DT is time-consuming process as ...Big data is usually unstructured, and many applications require theanalysis in real-time. Decision tree (DT) algorithm is widely used to analyzebig data. Selecting the optimal depth of DT is time-consuming process as itrequires many iterations. In this paper, we have designed a modified versionof a (DT). The tree aims to achieve optimal depth by self-tuning runningparameters and improving the accuracy. The efficiency of the modified (DT)was verified using two datasets (airport and fire datasets). The airport datasethas 500000 instances and the fire dataset has 600000 instances. A comparisonhas been made between the modified (DT) and standard (DT) with resultsshowing that the modified performs better. This comparison was conductedon multi-node on Apache Spark tool using Amazon web services. Resultingin accuracy with an increase of 6.85% for the first dataset and 8.85% for theairport dataset. In conclusion, the modified DT showed better accuracy inhandling different-sized datasets compared to standard DT algorithm.展开更多
Cloud computing has become one of the leading technologies in the world today.The benefits of cloud computing affect end users directly.There are several cloud computing frameworks,and each has ways of monitoring and ...Cloud computing has become one of the leading technologies in the world today.The benefits of cloud computing affect end users directly.There are several cloud computing frameworks,and each has ways of monitoring and providing resources.Cloud computing eliminates customer requirements such as expensive system configuration and massive infrastructure while improving dependability and scalability.From the user’s perspective,cloud computing makes it easy to upload multiagents and operate on different web services.In this paper,the authors used a restful web service and an agent system to discuss,deployments,and analysis of load performance parameters like memory use,cen-tral processing unit(CPU)utilization,network latency,etc.,both on localhost and an Amazon Web Service Elastic Cloud Computing(AWS-EC2)server.The Java Agent Development Environment(JADE)tool has been used to propose an archi-tecture and conduct a comparative study on both local and remote servers.JADE is an open-source tool for maintaining applications on AWS infrastructure.The focus of the study should be to reduce the complexity and time of load perfor-mance parameters by using an agent system on a cloud server instead of establish-ing a massive infrastructure on a local system,even for a small application.展开更多
Anomaly based approaches in network intrusion detection suffer from evaluation, comparison and deployment which originate from the scarcity of adequate publicly available network trace datasets. Also, publicly availab...Anomaly based approaches in network intrusion detection suffer from evaluation, comparison and deployment which originate from the scarcity of adequate publicly available network trace datasets. Also, publicly available datasets are either outdated or generated in a controlled environment. Due to the ubiquity of cloud computing environments in commercial and government internet services, there is a need to assess the impacts of network attacks in cloud data centers. To the best of our knowledge, there is no publicly available dataset which captures the normal and anomalous network traces in the interactions between cloud users and cloud data centers. In this paper, we present an experimental platform designed to represent a practical interaction between cloud users and cloud services and collect network traces resulting from this interaction to conduct anomaly detection. We use Amazon web services (AWS) platform for conducting our experiments.展开更多
During the last decade the emergence of Internet of Things(IoT)based applications inspired the world by providing state of the art solutions to many common problems.From traffic management systems to urban cities plan...During the last decade the emergence of Internet of Things(IoT)based applications inspired the world by providing state of the art solutions to many common problems.From traffic management systems to urban cities planning and development,IoT based home monitoring systems,and many other smart applications.Regardless of these facilities,most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets.In order to address this problem,this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.This hybrid model consists of;convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like;Raspberry Pi,Amazon Web services cloud,and GSM modems for remotely accessing and controlling of the home appliances.An android application is developed and deployed on Amazon Web Services(AWS)cloud for the remote monitoring of home appliances.A GSM device and Message queuing telemetry transport(MQTT)are integrated for communicating with the connected IoT devices to ensure the online and offline communication.For object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network model.The applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the light.Besides many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34%compared to the conventional recognition model(k nearest neighbours)recognition rate of 76%.展开更多
Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets, well-organized distributed network attacks, consist of a large number of bot...Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets, well-organized distributed network attacks, consist of a large number of bots that generate huge volumes of spam or launch Distributed Denial of Service （DDoS） attacks on victim hosts. New emerging botnet attacks degrade the status of Internet security further. To address these problems, a practical collaborative network security management system is proposed with an effective collaborative Unified Threat Management （UTM） and traffic probers. A distributed security overlay network with a centralized security center leverages a peer-to-peer communication protocol used in the UTMs collaborative module and connects them virtually to exchange network events and security rules. Security functions for the UTM are retrofitted to share security rules. In this paper, we propose a design and implementation of a cloud-based security center for network security forensic analysis. We propose using cloud storage to keep collected traffic data and then processing it with cloud computing platforms to find the malicious attacks. As a practical example, phishing attack forensic analysis is presented and the required computing and storage resources are evaluated based on real trace data. The cloud- based security center can instruct each collaborative UTM and prober to collect events and raw traffic, send them back for deep analysis, and generate new security rules. These new security rules are enforced by collaborative UTM and the feedback events of such rules are returned to the security center. By this type of close-loop control, the collaborative network security management system can identify and address new distributed attacks more quickly and effectively.展开更多
文摘Big data is usually unstructured, and many applications require theanalysis in real-time. Decision tree (DT) algorithm is widely used to analyzebig data. Selecting the optimal depth of DT is time-consuming process as itrequires many iterations. In this paper, we have designed a modified versionof a (DT). The tree aims to achieve optimal depth by self-tuning runningparameters and improving the accuracy. The efficiency of the modified (DT)was verified using two datasets (airport and fire datasets). The airport datasethas 500000 instances and the fire dataset has 600000 instances. A comparisonhas been made between the modified (DT) and standard (DT) with resultsshowing that the modified performs better. This comparison was conductedon multi-node on Apache Spark tool using Amazon web services. Resultingin accuracy with an increase of 6.85% for the first dataset and 8.85% for theairport dataset. In conclusion, the modified DT showed better accuracy inhandling different-sized datasets compared to standard DT algorithm.
文摘Cloud computing has become one of the leading technologies in the world today.The benefits of cloud computing affect end users directly.There are several cloud computing frameworks,and each has ways of monitoring and providing resources.Cloud computing eliminates customer requirements such as expensive system configuration and massive infrastructure while improving dependability and scalability.From the user’s perspective,cloud computing makes it easy to upload multiagents and operate on different web services.In this paper,the authors used a restful web service and an agent system to discuss,deployments,and analysis of load performance parameters like memory use,cen-tral processing unit(CPU)utilization,network latency,etc.,both on localhost and an Amazon Web Service Elastic Cloud Computing(AWS-EC2)server.The Java Agent Development Environment(JADE)tool has been used to propose an archi-tecture and conduct a comparative study on both local and remote servers.JADE is an open-source tool for maintaining applications on AWS infrastructure.The focus of the study should be to reduce the complexity and time of load perfor-mance parameters by using an agent system on a cloud server instead of establish-ing a massive infrastructure on a local system,even for a small application.
文摘Anomaly based approaches in network intrusion detection suffer from evaluation, comparison and deployment which originate from the scarcity of adequate publicly available network trace datasets. Also, publicly available datasets are either outdated or generated in a controlled environment. Due to the ubiquity of cloud computing environments in commercial and government internet services, there is a need to assess the impacts of network attacks in cloud data centers. To the best of our knowledge, there is no publicly available dataset which captures the normal and anomalous network traces in the interactions between cloud users and cloud data centers. In this paper, we present an experimental platform designed to represent a practical interaction between cloud users and cloud services and collect network traces resulting from this interaction to conduct anomaly detection. We use Amazon web services (AWS) platform for conducting our experiments.
基金supported by Department of Accounting and Information Systems,College of Business and Economics,Qatar University,Doha,Qatar and Department of Computer Science,University of Swabi,KP,Pakistanfunded by Qatar University Internal Grant under Grant No.IRCC-2020-009.
文摘During the last decade the emergence of Internet of Things(IoT)based applications inspired the world by providing state of the art solutions to many common problems.From traffic management systems to urban cities planning and development,IoT based home monitoring systems,and many other smart applications.Regardless of these facilities,most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets.In order to address this problem,this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.This hybrid model consists of;convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like;Raspberry Pi,Amazon Web services cloud,and GSM modems for remotely accessing and controlling of the home appliances.An android application is developed and deployed on Amazon Web Services(AWS)cloud for the remote monitoring of home appliances.A GSM device and Message queuing telemetry transport(MQTT)are integrated for communicating with the connected IoT devices to ensure the online and offline communication.For object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network model.The applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the light.Besides many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34%compared to the conventional recognition model(k nearest neighbours)recognition rate of 76%.
基金supported by the National Key Basic Research and Development (973) Program of China(Nos.2011CB302805,2011CB302505,2012CB315801,and2013CB228206)the National Natural Science Foundation of China(No.61233016)supported by Intel Research Councils UPO program with the title of Security Vulnerability Analysis Based on Cloud Platform
文摘Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets, well-organized distributed network attacks, consist of a large number of bots that generate huge volumes of spam or launch Distributed Denial of Service （DDoS） attacks on victim hosts. New emerging botnet attacks degrade the status of Internet security further. To address these problems, a practical collaborative network security management system is proposed with an effective collaborative Unified Threat Management （UTM） and traffic probers. A distributed security overlay network with a centralized security center leverages a peer-to-peer communication protocol used in the UTMs collaborative module and connects them virtually to exchange network events and security rules. Security functions for the UTM are retrofitted to share security rules. In this paper, we propose a design and implementation of a cloud-based security center for network security forensic analysis. We propose using cloud storage to keep collected traffic data and then processing it with cloud computing platforms to find the malicious attacks. As a practical example, phishing attack forensic analysis is presented and the required computing and storage resources are evaluated based on real trace data. The cloud- based security center can instruct each collaborative UTM and prober to collect events and raw traffic, send them back for deep analysis, and generate new security rules. These new security rules are enforced by collaborative UTM and the feedback events of such rules are returned to the security center. By this type of close-loop control, the collaborative network security management system can identify and address new distributed attacks more quickly and effectively.
