Elastic Compute Cloud Research Articles

CSMD: Container state management for deployment in cloud data centers

As the containers are lightweight in resource usage, they are preferred for cloud and edge computing service deployment. Containers serve the requests whenever a user sends a query; however, they remain idle when no user request comes. Again, improving the consolidation ratio of container deployments is essential to ensure fewer servers are used in a cloud data center with an optimal resource balance. To increase the consolidation ratio of a cloud data center, in this paper, we propose a system called Container State Management for Deployment (CSMD) to manage the container states. CSMD uses an algorithm to checkpoint the idle containers so that their resources can be released. The new containers are deployed using the released resources in a server. In addition, CSMD uses an algorithm to check the container status periodically, and the containers are resumed from the checkpoint state when the user requests them. We evaluate CSMD in Amazon Elastic Compute Cloud (Amazon EC2) by performing efficient state management of the containers. The experiments in the Amazon cloud show that the proposed CSMD system is superior to the existing algorithms as the proposed system increases the consolidation ratio of data centers.

Virtual Private Cloud (VPC) & Elastic Compute Cloud (EC2) with Remote Access Using PuTTY Terminal on Ubuntu in a Private VPN

One of the computer technologies that is commonly used by agencies/companies is the internet network which functions as a bridge to connect between one point to another. Apart from having the advantage of being a medium for long-distance communication, the internet also has a lack of security. One of the weakness found is the internet as a transmission medium for important or confidential data. One effort that can be made to overcome this problem is to build a private network in a public network service, which is often known as a Virtual Private Network (VPN). In this research, the VPN was built by combining a Virtual Private Cloud (VPC) with Elastic Compute Cloud (EC2) and using Ubuntu's Terminal in PuTTY Gen as remote access using network protocols for remote server needs from remote control.

PENGUJIAN KINERJA WEB SERVER ELASTIC CLOUD COMPUTE (EC2) FREE TIER PADA AMAZON WEB SERVICE (AWS) MENGGUNAKAN JMETER

The research aims to evaluate the capacity of Amazon Web Service's (AWS) Elastic Cloud Compute (EC2) Free Tier Web Server using the JMeter testing tool. The objective is to ascertain the extent of the server's load-bearing capabilities. Tests were conducted, loading 500 users within a 10-second range and 1000 users within a 1-second range. Despite being a free service, test results showed the server's capability to handle high loads without errors (0% error rate). These findings illustrate the free service's ability to maintain stable performance under significant user loads. The utilization of EC2 Free Tier on AWS demonstrates its capacity to handle heavy tasks without compromising service quality. From about 5 steps in each of two tests, the value for sample time / response time in the initial test of the Amazon Web Service Web server is 59 / ms (millisecond) and the average latency value in the second test, namely 7.213 /ms (millisecond). Both of these gained CPU Utilization from minum, average and maximum with final rate are zero Error. This outcome concludes that the free server possesses robust and reliable capabilities, instilling confidence in an effective and efficient server solution even under high usage levels without additional costs.

Unveiling AWS Spot Instance Trends: An Empirical Illustration

Cloud services are growing at a very fast rate. One of the most successful cloud service providers is AWS. Its services, such as virtualization with the assistance of Elastic Compute Cloud, have made a lot of the tasks a lot easier to do. Cloud computing offers the option of renting computer systems rather than purchasing them outright. Having many possibilities for renting also brings advantages, depending on the kind of job being done. Spot instances are those that are suitable for tasks that are not mission-critical. This research article will talk about the geographical growth of AWS as well as spot instances. In this article, we will develop an algorithm for the extraction of bulk data from the Amazon cloud about spot instances. When the data have been retrieved, they will be analyzed based on a number of different characteristics, such as the areas they are located in or the operating system that is provided. This data may be put to use for a variety of decision-making scenarios, some of which we may encounter in the not-too-distant future when using the Amazon platform. These may assist users in making decisions on the selection of various spot occurrences.

XBot2D: towards a robotics hybrid cloud architecture for field robotics.

Nowadays, robotics applications requiring the execution of complex tasks in real-world scenarios are still facing many challenges related to highly unstructured and dynamic environments in domains such as emergency response and search and rescue where robots have to operate for prolonged periods trading off computational performance with increased power autonomy and vice versa. In particular, there is a crucial need for robots capable of adapting to such settings while at the same time providing robustness and extended power autonomy. A possible approach to overcome the conflicting demand of a computational performing system with the need for long power autonomy is represented by cloud robotics, which can boost the computational capabilities of the robot while reducing the energy consumption by exploiting the offload of resources to the cloud. Nevertheless, the communication constraint due to limited bandwidth, latency, and connectivity, typical of field robotics, makes cloud-enabled robotics solutions challenging to deploy in real-world applications. In this context, we designed and realized the XBot2D software architecture, which provides a hybrid cloud manager capable of dynamically and seamlessly allocating robotics skills to perform a distributed computation based on the current network condition and the required latency, and computational/energy resources of the robot in use. The proposed framework leverage on the two dimensions, i.e., 2D (local and cloud), in a transparent way for the user, providing support for Real-Time (RT) skills execution on the local robot, as well as machine learning and A.I. resources on the cloud with the possibility to automatically relocate the above based on the required performances and communication quality. XBot2D implementation and its functionalities are presented and validated in realistic tasks involving the CENTAURO robot and the Amazon Web Service Elastic Computing Cloud (AWS EC2) infrastructure with different network conditions.

A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study.

The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADG. This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time. The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15). The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was "acceptable," with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype. A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights into its creation. This study provides a valuable example of the development of a novel, evidence-based app for personalized dietary advice on food choices using recent advancements in computer vision. A revised version of this prototype is currently under development.

Towards Proactive Risk-Aware Cloud Cost Optimization Leveraging Transient Resources

Low-cost transient resources such as Amazon's Elastic Compute Cloud (EC2) Spot instances can be opportunistically leveraged to reduce the ongoing costs of cloud applications. However, they are susceptible to unilateral revocations by the vendor making them a risky proposition for long-running applications with strict performance requirements. It is challenging to effectively balance the cost savings that transient resources provide with the associated revocation risk which, if realised, can impact application performance. To address this challenge, we propose an approach for risk-aware cloud cost optimization that is inspired by the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">portfolio diversification</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contract diversification</i> mitigates the revocation risk by procuring the required compute capacity as a mixed portfolio of transient and non-transient resources. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Resource diversification</i> further diversifies the risk by using multiple transient resource types. Using our approach, consumers can leverage contract and resource diversification to proactively (re-)configure their application's resource portfolio to handle workload and resource price fluctuations while minimizing ongoing cost and keeping ongoing revocation risk within tolerable limits. Simulative evaluation using three real-world workload traces and Amazon's EC2 offerings demonstrate that our proposed approach can achieve meaningful cost savings compared to the baseline costs, while significantly reducing the portfolio's exposure to revocation risk.

Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology.

The COVID-19 pandemic has reiterated the need for cohesive, collective, and deliberate societal efforts to address inherent inefficiencies in our health systems and overcome decision-making gaps using real-time data analytics. To achieve this, decision makers need independent and secure digital health platforms that engage citizens ethically to obtain big data, analyze and convert big data into real-time evidence, and finally, visualize this evidence to inform rapid decision-making. The objective of this study is to develop replicable and scalable jurisdiction-specific digital health dashboards for rapid decision-making to ethically monitor, mitigate, and manage public health crises via systems integration beyond health care. The primary approach in the development of the digital health dashboard was the use of global digital citizen science to tackle pandemics like COVID-19. The first step in the development process was to establish an 8-member Citizen Scientist Advisory Council via Digital Epidemiology and Population Health Laboratory's community partnerships. Based on the consultation with the council, three critical needs of citizens were prioritized: (1) management of household risk of COVID-19, (2) facilitation of food security, and (3) understanding citizen accessibility of public services. Thereafter, a progressive web application (PWA) was developed to provide daily services that address these needs. The big data generated from citizen access to these PWA services are set up to be anonymized, aggregated, and linked to the digital health dashboard for decision-making, that is, the dashboard displays anonymized and aggregated data obtained from citizen devices via the PWA. The digital health dashboard and the PWA are hosted on the Amazon Elastic Compute Cloud server. The digital health dashboard's interactive statistical navigation was designed using the Microsoft Power Business Intelligence tool, which creates a secure connection with the Amazon Relational Database server to regularly update the visualization of jurisdiction-specific, anonymized, and aggregated data. The development process resulted in a replicable and scalable digital health dashboard for decision-making. The big data relayed to the dashboard in real time reflect usage of the PWA that provides households the ability to manage their risk of COVID-19, request food when in need, and report difficulties and issues in accessing public services. The dashboard also provides (1) delegated community alert system to manage risks in real time, (2) bidirectional engagement system that allows decision makers to respond to citizen queries, and (3) delegated access that provides enhanced dashboard security. Digital health dashboards for decision-making can transform public health policy by prioritizing the needs of citizens as well as decision makers to enable rapid decision-making. Digital health dashboards provide decision makers the ability to directly communicate with citizens to mitigate and manage existing and emerging public health crises, a paradigm-changing approach, that is, inverting innovation by prioritizing community needs, and advancing digital health for equity. RR1-10.2196/46810.

High Availability and Fault Tolerance in AWS

With the increasing adoption of cloud computing, ensuring high availability and fault tolerance has become paramount for organizations. Amazon Web Services (AWS) offers a robust infrastructure for hosting applications, but it requires careful architectural design and implementation to achieve desired levels of availability and fault tolerance. This research paper explores two innovative concepts, namely Cloud Fractal and Decentralized Replication and Orchestration, and their application in achieving high availability and fault tolerance in AWS. We present a comprehensive analysis of these concepts and provide practical guidelines for their implementation in real-world scenarios. Our findings demonstrate the effectiveness of Cloud Fractal and Decentralized Replication and Orchestration in enhancing the reliability and resilience of AWS deployments. Keywords: Fault Isolation, High Availability, Load Balancing, Auto-scaling, Disaster Recovery, Data Replication, Backup and Restore, Fault Tolerance, Cloud Computing, Amazon Elastic Compute Cloud (EC2), Amazon Relational Database Service (RDS), Amazon Simple Storage Service (S3), AWS Elastic Load Balancer (ELB), Complexity, Operational Management, Cloud Fractal, AWS Lambda, Amazon SQS, Decentralized Replication and Orchestration (DOI).

To transcode or not? A machine learning based edge video caching and transcoding strategy

The variable network conditions of end-users demand different resolutions, formats, and bitrate versions of videos to be delivered over the network. Fetching each video from the Content Delivery Network (CDN) burdens all network layers. A promising solution is to leverage Mobile Edge Computing (MEC). This paper presents a Machine Learning based caching and transcoding model, which helps release the burden on the backhaul links of the network. The purposed scheme contains a task scheduler and time estimator. The time estimator predicts the job’s transcoding time based on the Virtual Machines (VMs) load. The task scheduler maps the transcoding task to different VMs regarding the cost feasibility, Quality of Service (QoS) of the users, and the cost-to-performance ratio of VMs. For this purpose, we prepare a dataset of 500 videos and transcode each video in every lower representation using Amazon Elastic Compute Cloud (EC2). The time estimator is trained on 77% of the video dataset containing more than 80,000 transcoding time data of different videos. The simulation results show that the proposed scheme outperforms its counterparts in terms of cost, average delay perceived by the user, and backhaul burden.

CAMPUS CLOUD: EMPOWERING UNIVERSITY MANAGEMENT WEB APPLICATION WITH CLOUD-HOSTED DOCKER TECHNOLOGY ON AWS

Abstract—In this article I have deployed a web-based applica- tion to Docker using Amazon web service (EC2 Instance). Elastic Cloud Compute service is used to the run the docker applications on scalable clusters. Amazon EC2 is fully managed and secured Container orchestration service that makes to deploy, manage and scale Dockerized web application. Docker containers on an EC2 instance to run a university management system website on the Amazon Web Services (AWS) cloud. For educational institutions, a university management system is a crucial tool that enables administrators to manage student records, course information, and other crucial data. The introduction to Docker, a containerization technology that makes it simple to build and manage web applications, follows a discussion of the advantages of hosting a university management system website on the cloud. The next section of the paper focuses on the AWS EC2 service and its capabilities, such as its capability for flexible computing and scalability. The paper also offers a step-by-step tutorial on how to use Docker to deploy a university administration system website on an EC2 instance, including instructions on how to set up the EC2 instance, install and configure Docker, and deploy the website code in a Docker container. Elastic Load Balancer and Auto Scaling, two AWS services, are used in the process of setting up security and scalability for the website. The benefits of using Docker to host a university administration system website on AWS EC2 are outlined in the paper’s conclusion, along with predictions for the development of cloud-based web application hosting. This research paper offers a thorough overview of the steps involved in hosting a university management system website using Docker on AWS EC2, making it a useful tool for educational institutions looking to improve their information management procedures and take advantage of cloud computing and Docker- Containerization.

Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS)

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our cameras system to capture the images and upload them to the Amazon Simple Storage Service (AWS S3) cloud. Then two detectors were running, Haar cascade and multitask cascaded convolutional neural networks (MTCNN), at the Amazon Elastic Compute (AWS EC2) cloud, after that the output results of these two detectors are compared using accuracy and execution time. Then the classified non-permission images are uploaded to the AWS S3 cloud. The validation accuracy of the offline augmentation face detection classification model reached 98.81%, and the loss and mean square error were decreased to 0.0176 and 0.0064, respectively. The execution time of all AWS cloud systems for one image when using Haar cascade and MTCNN detectors reached three and seven seconds, respectively.

Real-Time SLAM Mobile Robot and Navigation Based on Cloud-Based Implementation

This study investigates the feasibility of a mobile robot navigating and discovering its location in unknown environments, followed by the creation of maps of these navigated environments for future use. First, a real mobile robot named TurtleBot3 Burger was used to achieve the simultaneous localization and mapping (SLAM) technique for a complex environment with 12 obstacles of different sizes based on the Rviz library, which is built on the robot operating system (ROS) booted in Linux. It is possible to control the robot and perform this process remotely by using an Amazon Elastic Compute Cloud (Amazon EC2) instance service. Then, the map to the Amazon Simple Storage Service (Amazon S3) cloud was uploaded. This provides a database to display maps and use them at any time for navigation without the need to redraw the map. This map can be accessed by using an authentication process (username and password) supervised by the cloud server administrator. After that, using the serverless image handler (SIH), with the aid of this solution, you can change the size of images, change the color of the background, format them, or add watermarks. Experiment results demonstrated the ability to build a map of an unknown location in a complex environment and use it for navigation tasks on a real mobile robot via remote control. It also showed the success of the process of storing the map for future use and the process of modifying the map using SIH.

AMAZON S3 STATIC WEBSITE HOSTING

Nowadays, especially in period of energetic crisis, it is very important to get reliable data storage and uninterrupted access to websites. Considering that the world’s mainstream is using the clouds services for educational, scientific, business purposes it is important to explore features of hosting websites in major cloud service providers. One of the leader’s vendors in cloud servicing is Amazon with its Web Services (AWS). AWS offers hosting solutions such as Lightsail, Amplify Console, Simple Storage Service (S3) and Elastic Cloud Computing, from simple static websites to complicated dynamic, The article deals with the method of hosting static websites. Simple Storage Service was used for this purpose. The method has many benefits: the service is simple, cheap, scales well, manages everything and has great integration with other AWS services. This method is the best for websites that do not contain server-side scripting, like PHP or ASP.NET. It is ideal for websites that change infrequently, such as personal, promo, startup websites, websites of small businesses and organizations. The article focuses on how set permissions, properties and policies of AWS S3 bucket for hosting a website. But usage of S3 bucket alone provides only http connection. That’s why to deploy secure connection and speed up delivery of the static content was proposed to use AWS CloudFront service. CloudFront is content delivery network service, that uses the latest version of Transport Layer Security Protocol and saves files in the cache for 24 hours. The article shows how to configure CloudFront distribution to serve https requests for an Amazon S3 bucket. Configuration S3 bucket REST API endpoint was used as the most secure case. As a result, latency was reduced, security was improved due to traffic encryption and the content of S3 bucket was kept private.

A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

The use of Amazon Web Services is growing rapidly as more users are adopting the technology. It has various functionalities that can be used by large corporates and individuals as well. Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions. In this research work, sentiment analysis is performed on the AWS Elastic Compute Cloud (EC2) through Twitter data. The data is managed to the EC2 by using elastic load balancing. The collected data is subjected to preprocessing approaches to clean the data, and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative sentiments. High accuracy of 94.17% is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

FLCD: A Flexible Low Complexity Design of Coded Distributed Computing

We propose a flexible low complexity design (FLCD) of coded distributed computing (CDC) with empirical evaluation on Amazon Elastic Compute Cloud (Amazon EC2). CDC can expedite MapReduce like computation by trading increased map computations to reduce communication load and shuffle time. A main novelty of FLCD is to utilize the design freedom in defining map and reduce functions to develop asymptotic homogeneous systems to support varying intermediate values (IV) sizes under a general MapReduce framework. Compared to existing designs with constant IV sizes, FLCD offers greater flexibility in adapting to network parameters and significantly reduces the implementation complexity by requiring fewer input files and shuffle groups. The FLCD scheme is the first proposed low-complexity CDC design that can operate on a network with an arbitrary number of nodes and computation load. We perform empirical evaluations of the FLCD by executing the TeraSort algorithm on an Amazon EC2 cluster. This is the first time that theoretical predictions of the CDC shuffle time are validated by empirical evaluations. The evaluations demonstrate a 2.0 to 4.24 speedup compared to conventional uncoded MapReduce, a 12% to 52% reduction in total time, and a wider range of operating network parameters compared to existing CDC schemes.

A Proposed Architecture for Local-Host and AWS with Multiagent System

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, central 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 architecture 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 performance parameters by using an agent system on a cloud server instead of establishing a massive infrastructure on a local system, even for a small application.

E-Commerce Website Hosting Using AWS Cloud Services

The primary extent of this task is to comprehend how to best use different distributed computing administrations to get great execution for less expense. Amazon surrenders a marvelous arrangement of web helps that enables fashioners to make dynamic and generous applications. Conveying on AWS can save you time, money and work diverged from building and staying aware of additional customary frameworks. Amazon Web Services offers a wide plan of overall cloud-based things including process, limit, informational collections, assessment, coordinating, flexible, engineer gadgets, the board contraptions, IoT, security, and try applications: on- demand, open quite promptly, with pay-all the more just as expenses emerge esteeming. From data warehousing to association instruments, lists to content transport, more than 200 AWS organizations are available. New organizations can be provisioned quickly, without the blunt capital expense. Today, AWS gives a significantly trustworthy, flexible, negligible cost establishment stage in the cloud that controls a colossal number of associations in 190 countries all over the planet. This licenses adventures, new organizations, little and medium-sized associations and clients in the public region to get to the construction blocks they need to answer quickly to changing business essentials. Amazon EC2 (Elastic Compute Cloud), VPC (Virtual Private Cloud), IAM (Identity And Access Management), Cloud Watch, CDN (Content Delivery Network) and Route 53 are used here since it is very popular in giving extraordinary disseminated figuring organizations. In this report, I'll tell you, step by step, through the procedure of using the AWS Marketplace to game plan and produce another AWS Cloud server. In this task we furthermore used progression gadgets like GIT and Jenkins for robotization.

Know Time to Die – Integrity Checking for Zero Trust Chiplet-based Systems Using Between-Die Delay PUFs

Industry trends are moving toward increasing use of chiplets as a replacement for monolithic fabrication in many modern chips. Each chiplet is a separately-produced silicon die, and a system-on-chip (SoC) is created by packaging the chiplets together on a silicon interposer or bridge. Chiplets enable IP reuse, heterogeneousintegration, and better ability to leverage cost-appropriate process nodes. Yet, creating systems from separately produced components also brings security risks to consider, such as the possibility of die swapping, or susceptibility to interposer probing or tampering. In a zero-trust security posture, a chiplet should not blindly assume it is operating in a friendly environment.In this paper we propose a delay-based PUF for chiplets to verify system integrity. Our technique allows a single chiplet to initiate a protocol with its neighbors to measure unique variations in the propagation delays of incoming signals as part of an integrity check. We prototype our design on Xilinx Ultrascale+ FPGAs, which are constructed as multi-die systems on a silicon interposer, and which also emulate the general features of other industrial chiplet interfaces. We perform experiments on, and compare data from, dozens of Ultrascale+ FPGAs by making use of Amazon’s Elastic Compute Cloud (EC2) F1 instances as a testing platform. The PUF cells are shown to reject clock and temperature variation as common mode, and each cell produces approximately 5 ps of unique delay variation. For a design with 144 PUF cells, we measure the mean within-class and between-class distances to be 68.3 ps and 847.7 ps, respectively. The smallest between-class distance of 686.0 ps exceeds the largest within-class distance of 124.0 ps by more than 5x under nominal conditions, and the PUF is shown to be resilient to environmental changes. Our findings indicate the PUF can be used for authentication, and is potentially sensitive enough to detect picosecond-scale timing changes due to tampering.

A practical framework for cyber defense generation, enforcement and evaluation

It is challenging to enforce and evaluate cyber-defenses for large networks. The current state-of-the-art approaches on defense enforcement and evaluations are manually performed by a security expert and they are executed separately without using a defined workflow. Moreover, the cyber defense generation, enforcement, and evaluation require costs, time, and effort of the security experts to effectively identify attacks and the best set of defenses to deploy. To effectively address these challenges, we propose a novel defense automation framework named CD-GEE (Cyber Defense Generation, Enforcement and Evaluation). CD-GEE can perform automated Cyber defense generation, defense enforcement and security evaluation without human intervention. CD-GEE works in the following five phases: (1) host/network data collection, (2) graphical security models construction, (3) cyber defense generation & selection, (4) defense deployment, and (5) defense evaluation and report. To show the usability and applicability of CD-GEE, we perform experiments on different networks including Amazon’s Elastic Compute Cloud. Besides, we also evaluate the performance and the effectiveness of the defenses deployed via our proposed framework and tool. We show that the CD-GEE measures the security posture of the network before and after defense deployment and evaluation. We also showed that our proposed framework performed better than without the framework in enhancing the security of networks on both the Amazon Elastic Compute Cloud and simulation network environment.