Engineering Commons^™

Cyberattacks And Security Of Cloud Computing: A Complete Guideline, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman

Research outputs 2022 to 2026

Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, …

Blmdp: A New Bi-Level Markov Decision Process Approach To Joint Bidding Andtask-Scheduling In Cloud Spot Market, Mona Naghdehforoushha, Mehdi Dehghan Takht Fooladi, Mohammad Hossein Rezvani, Mohammad Mehdi Gilanian Sadeghi

Turkish Journal of Electrical Engineering and Computer Sciences

In the cloud computing market (CCM), computing services are traded between cloud providers and consumers in the form of the computing capacity of virtual machines (VMs). The Amazon spot market is one of the most well-known markets in which the surplus capacity of data centers is auctioned off in the form of VMs at relatively low prices. For each submitted task, the user can offer a price that is higher than the current price. However, uncertainty in the market environment confronts the user with challenges such as the variable price of VMs and the variable number of users. An appropriate …

Cloud-Based Machine Learning And Sentiment Analysis, Emmanuel C. Opara

Electronic Theses and Dissertations

The role of a Data Scientist is becoming increasingly ubiquitous as companies and institutions see the need to gain additional insights and information from data to make better decisions to improve the quality-of-service delivery to customers. This thesis document contains three aspects of data science projects aimed at improving tools and techniques used in analyzing and evaluating data. The first research study involved the use of a standard cybersecurity dataset and cloud-based auto-machine learning algorithms were applied to detect vulnerabilities in the network traffic data. The performance of the algorithms was measured and compared using standard evaluation metrics. The second …

Bibliometric Review On Iot Based System For Remote Downloading On Microcontroller, Arundhati Bandopadhyaya Ms., Diya Dodwad Ms., Diviyanshi Gupta Ms., Surya Koyyana Mr., Parag Narkhede Mr., Shripad Deshpande Mr.

Library Philosophy and Practice (e-journal)

Working with sensors has become an area of expertise within the domain of electronic engineering. On a day to day basis, thousands of sensors are put to use around us, for instance, in smoke alarms, speedometers, motors, computers, radiators etc. to accumulate sensor data, cables that connect the sensors to the bottom station after which the information is worked on. But cabling is often expensive especially when handling large scale industrial applications ^[5]. For an equivalent reason, low-cost wireless networks came into the picture recently and are in high demand. A research paper we found during our review of …

A Novel Data Placement Strategy To Reduce Data Traffic During Run-Time, Sridevi Sridhar, Rhymend Uthariaraj Vaidyanathan

Turkish Journal of Electrical Engineering and Computer Sciences

High impact scientific applications processed in distributed data centers often involve big data. To avoid the intolerable delays due to huge data movements across data centers during processing, the concept of moving tasks to data was introduced in the last decade. Even after the realization of this concept termed as data locality, the expected quality of service was not achieved. Later, data colocality was introduced where data groupings were identified and then data chunks were placed wisely. However, the aspect of the expected data traffic during run time is generally not considered while placing data. To identify the expected data …

Distributed Denial Of Service Attack Detection In Cloud Computing Using Hybridextreme Learning Machine, Gopal Singh Kushwah, Virender Ranga

Turkish Journal of Electrical Engineering and Computer Sciences

One of the major security challenges in cloud computing is distributed denial of service (DDoS) attacks. In these attacks, multiple nodes are used to attack the cloud by sending huge traffic. This results in the unavailability of cloud services to legitimate users. In this research paper, a hybrid machine learning-based technique has been proposed to detect these attacks. The proposed technique is implemented by combining the extreme learning machine (ELM) model and the blackhole optimization algorithm. Various experiments have been performed with the help of four benchmark datasets namely, NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, to evaluate the …

Energy And Performance-Optimized Scheduling Of Tasks In Distributed Cloud And Edge Computing Systems, Haitao Yuan

Dissertations

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo

Electronic Thesis and Dissertation Repository

Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand …

Fog Computing Enabling Industrial Internet Of Things: State-Of-The-Art And Research Challenges, Rabeea Basir, Saad Qaisar, Mudassar Ali, Monther Aldwairi, Muhammad Ikram Ashraf, Aamir Mahmood, Mikael Gidlund

All Works

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a …

Development Of Smart Semiconductor Manufacturing: Operations Research And Data Science Perspectives, Marzieh Khakifirooz, Mahdi Fathi, Kan Wu

Bagley College of Engineering Publications and Scholarship

With advances in information and telecommunication technologies and data-enabled decision making, smart manufacturing can be an essential component of sustainable development. In the era of the smart world, semiconductor industry is one of the few global industries that are in a growth mode to smartness, due to worldwide demand. The important opportunities that can boost the cost reduction of productivity and improve quality in wafer fabrication are based on the simulations of actual environment in Cyber-Physical Space and integrate them with decentralized decision-making systems. However, this integration faced the industry with novel unique challenges. The stream of the data from …

Cloud-Supported Machine Learning System For Context-Aware Adaptive M-Learning, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq

Turkish Journal of Electrical Engineering and Computer Sciences

It is a knotty task to amicably identify the sporadically changing real-world context information of a learner during M-learning processes. Contextual information varies greatly during the learning process. Contextual information that affects the learner during a learning process includes background knowledge, learning time, learning location, and environmental situation. The computer programming skills of learners improve rapidly if they are encouraged to solve real-world programming problems. It is important to guide learners based on their contextual information in order to maximize their learning performance. In this paper, we proposed a cloud-supported machine learning system (CSMLS), which assists learners in learning practical …

A Novel Resource Clustering Model To Develop An Efficient Wireless Personal Cloud Environment, Kowsigan Mohan, Balasubramanie Palanisamy

Turkish Journal of Electrical Engineering and Computer Sciences

In the current era, cloud computing is the major focus of distributed computing and it helps in satisfying the requirements of the business world. It provides facilities on demand under all the parameters of the computing, such as infrastructure, platform, and software, across the globe. One of the major challenges in the cloud environment is to cluster the resources and schedule the jobs among the resource clusters. Many existing approaches failed to provide an optimal solution for job scheduling due to inefficient clustering of resources. In the proposed system, a novel algorithm called resource differentiation based on equivalence node potential …

Storage Systems For Mobile-Cloud Applications, Nafize R. Paiker

Dissertations

Mobile devices have become the major computing platform in todays world. However, some apps on mobile devices still suffer from insufficient computing and energy resources. A key solution is to offload resource-demanding computing tasks from mobile devices to the cloud. This leads to a scenario where computing tasks in the same application run concurrently on both the mobile device and the cloud.

This dissertation aims to ensure that the tasks in a mobile app that employs offloading can access and share files concurrently on the mobile and the cloud in a manner that is efficient, consistent, and transparent to locations. …

From Geographically Dispersed Data Centers Towards Hierarchical Edge Computing, Abbas Kiani

Dissertations

Internet scale data centers are generally dispersed in different geographical regions. While the main goal of deploying the geographically dispersed data centers is to provide redundancy, scalability and high availability, the geographic dispersity provides another opportunity for efficient employment of global resources, e.g., utilizing price-diversity in electricity markets or utilizing locational diversity in renewable power generation. In other words, an efficient approach for geographical load balancing (GLB) across geo-dispersed data centers not only can maximize the utilization of green energy but also can minimize the cost of electricity. However, due to the different costs and disparate environmental impacts of the …

Security Evaluation Of Virtualized Computing Platforms, Ganesh Reddy Gunnam

Theses and Dissertations

In this thesis, security experiments were conducted to evaluate embedded security protocol performance of two leading server operating systems, Apple’s MAC OS server LION Vs. Microsoft’s Windows server 2012 R2 OS under different types of security attack. Furthermore, experiments were conducted to understand and evaluate the effect of virtualization using Hyper-V with Windows 2012 R2 OS on MAC hardware platform. For these experiments, connection rate, connection latency, non-paged pool allocations and processor core utilization for different OS, virtual machines, and under different traffic types were measured.

Chaos-Based Encryption Keys And Neural Key-Store For Cloud-Hosted Data Confidentiality, N.N Mosola, M.T Dlamini, Jonathan Blackledge, J.H.P Eloff, H.S Venter

Conference papers

Cloud computing brings flexible and cost-effective services. However, security concerns plague the cloud. Data confidentiality is one of the security concerns inhibiting the adoption of cloud computing. This concern stems from various cyberattacks directed towards gaining unauthorised access to cloud-bound or cloud-hosted data. This paper proposes a clientend encryption and key management system to curb cyberattacks that target compromising data confidentiality on the cloud.

Hadoop Framework Implementation And Performance Analysis On A Cloud, Göksu Zeki̇ye Özen, Mehmet Tekerek, Rayi̇mbek Sultanov

Turkish Journal of Electrical Engineering and Computer Sciences

The Hadoop framework uses the MapReduce programming paradigm to process big data by distributing data across a cluster and aggregating. MapReduce is one of the methods used to process big data hosted on large clusters. In this method, jobs are processed by dividing into small pieces and distributing over nodes. Parameters such as distributing method over nodes, the number of jobs held in a parallel fashion, and the number of nodes in the cluster affect the execution time of jobs. The aim of this paper is to determine how the numbers of nodes, maps, and reduces affect the performance of …

Designing A Vm-Level Vertical Scalability Service In Current Cloud Platforms: A New Hope For Wearable Computers, Mustafa Kaiiali

Turkish Journal of Electrical Engineering and Computer Sciences

Public clouds are becoming ripe for enterprise adoption. Many companies, including large enterprises, are increasingly relying on public clouds as a substitute for, or a supplement to, their own computing infrastructures. On the other hand, cloud storage service has attracted over 625 million users. However, apart from the storage service, other cloud services, such as the computing service, have not yet attracted the end users' interest for economic and technical reasons. Cloud service providers offers horizontal scalability to make their services scalable and economical for enterprises while it is still not economical for the individual users to use their computing …

Qos Recommendation In Cloud Services, Xianrong Zheng, Li Da Xu, Sheng Chai

Information Technology & Decision Sciences Faculty Publications

As cloud computing becomes increasingly popular, cloud providers compete to offer the same or similar services over the Internet. Quality of service (QoS), which describes how well a service is performed, is an important differentiator among functionally equivalent services. It can help a firm to satisfy and win its customers. As a result, how to assist cloud providers to promote their services and cloud consumers to identify services that meet their QoS requirements becomes an important problem. In this paper, we argue for QoS-based cloud service recommendation, and propose a collaborative filtering approach using the Spearman coefficient to recommend cloud …

Cepsim: Modelling And Simulation Of Complex Event Processing Systems In Cloud Environments, Wilson A. Higashino, Miriam Am Capretz, Luiz F. Bittencourt

Electrical and Computer Engineering Publications

The emergence of Big Data has had profound impacts on how data are stored and processed. As technologies created to process continuous streams of data with low latency, Complex Event Processing (CEP) and Stream Processing (SP) have often been related to the Big Data velocity dimension and used in this context. Many modern CEP and SP systems leverage cloud environments to provide the low latency and scalability required by Big Data applications, yet validating these systems at the required scale is a research problem per se. Cloud computing simulators have been used as a tool to facilitate reproducible and repeatable …

Information-Theoretic Secure Outsourced Computation In Distributed Systems, Zhaohong Wang

Theses and Dissertations--Electrical and Computer Engineering

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir's secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using …

Design, Analysis, Implementation And Evaluation Of Real-Time Opportunistic Spectrum Access In Cloud-Based Cognitive Radio Networks, Nimish Sharma

Electronic Theses and Dissertations

Opportunistic spectrum access in cognitive radio network is proposed for remediation of spectrum under-utilization caused by exclusive licensing for service providers that are intermittently utilizing spectrum at any given geolocation and time. The unlicensed secondary users (SUs) rely on opportunistic spectrum access to maximize spectrum utilization by sensing/identifying the idle bands without causing harmful interference to licensed primary users (PUs). In this thesis, Real-time Opportunistic Spectrum Access in Cloud-based Cognitive Radio Networks (ROAR) architecture is presented where cloud computing is used for processing and storage of idle channels. Software-defined radios (SDRs) are used as SUs and PUs that identify, report, …

Elastic Highly Available Cloud Computing, Hassan Hawilo

Electronic Thesis and Dissertation Repository

High availability and elasticity are two the cloud computing services technical features. Elasticity is a key feature of cloud computing where provisioning of resources is closely tied to the runtime demand. High availability assure that cloud applications are resilient to failures. Existing cloud solutions focus on providing both features at the level of the virtual resource through virtual machines by managing their restart, addition, and removal as needed. These existing solutions map applications to a specific design, which is not suitable for many applications especially virtualized telecommunication applications that are required to meet carrier grade standards. Carrier grade applications typically …

Data Security And Privacy In Smart Grid, Yue Tong

Doctoral Dissertations

This dissertation explores novel data security and privacy problems in the emerging smart grid.

The need for data security and privacy spans the whole life cycle of the data in the smart grid, across the phases of data acquisition, local processing and archiving, collaborative processing, and finally sharing and archiving. The first two phases happen in the private domains of an individual utility company, where data are collected from the power system and processed at the local facilities. When data are being acquired and processed in the private domain, data security is the most critical concern. The key question is …

Resilient And Trustworthy Dynamic Data-Driven Application Systems (Dddas) Services For Crisis Management Environments, Youakim Badr, Salim Hariti, Youssif Al-Nashif, Erik Blasch

Electrical & Computer Engineering Faculty Publications

Future crisis management systems needresilient and trustworthy infrastructures to quickly develop reliable applications and processes, andensure end-to-end security, trust, and privacy. Due to the multiplicity and diversity of involved actors, volumes of data, and heterogeneity of shared information;crisis management systems tend to be highly vulnerable and subjectto unforeseen incidents. As a result, the dependability of crisis management systems can be at risk. This paper presents a cloud-based resilient and trustworthy infrastructure (known as rDaaS) to quickly develop secure crisis management systems. The rDaaS integrates the Dynamic Data-Driven Application Systems (DDDAS) paradigm into a service-oriented architecture over cloud technology and provides …

Synthesis Of Real-Time Cloud Applications For Internet Of Things, Slawomir Bak, Radoslaw Czarnecki, Stanislaw Deniziak

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents the methodology for the synthesis of real-time applications working in the ``Internet of Things'' environment. We propose the client-server architecture, where embedded systems act as smart clients and the Internet application is a server of the system. The architecture of the application conforms to the cloud computing model. Since centralized systems are prone to bottlenecks caused by accumulation of transmissions or computations, we propose the distributed architecture of the server and the methodology that constructs this architecture using Internet resources supported by a cloud provider. We assume that the function of the server is specified as a …

Online Virtual Network Provisioning In Distributed Cloud Computing Data Centers, Khaled Mefarh Alhazmi

Electronic Thesis and Dissertation Repository

Efficient virtualization methodologies constitute the core of cloud computing data center implementation. Clients are attracted to the cloud model by the ability to scale available resources dynamically and the flexibility in payment options. However, performance hiccups can push them to return to the buy-and-maintain model. Virtualization plays a key role in the synchronous management of the thousands of servers along with clients' data residing on them. To achieve seamless virtualization, cloud providers require a system that performs the function of virtual network mapping. This includes receiving the cloud client requests and allocating computational and network resources in a way that …

An Energy-Efficient, Time-Constrained Scheduling Scheme In Local Mobile Cloud, Ting Shi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Mobile devices have limited resource, such as computation performance and battery life. Mobile cloud computing is gaining popularity as a solution to overcome these resource limitations by sending heavy computation to resourceful servers and receiving the results from these servers. Local mobile clouds comprised of nearby mobile devices are proposed as a better solution to support real-time applications. Since network bandwidth and computational resource is shared among all the mobile devices, a scheduling scheme is needed to ensure that multiple mobile devices can efficiently offload tasks to local mobile clouds, satisfying the tasks' time constraint while keeping low-energy consumption. Two …

A State-Of-The-Art Review Of Cloud Forensics, Sameera Almulla, Youssef Iraqi, Andrew Jones

Journal of Digital Forensics, Security and Law

Cloud computing and digital forensics are emerging fields of technology. Unlike traditional digital forensics where the target environment can be almost completely isolated, acquired and can be under the investigators control; in cloud environments, the distribution of computation and storage poses unique and complex challenges to the investigators. Recently, the term “cloud forensics” has an increasing presence in the field of digital forensics. In this state-of-the-art review, we included the most recent research efforts that used “cloud forensics” as a keyword and then classify the literature into three dimensions: (1) survey-based, (2) technology-based and (3) forensics-procedural-based. We discuss widely accepted …