Computer Engineering Commons^™

Toward Building An Intelligent And Secure Network: An Internet Traffic Forecasting Perspective, Sajal Saha

Electronic Thesis and Dissertation Repository

Internet traffic forecast is a crucial component for the proactive management of self-organizing networks (SON) to ensure better Quality of Service (QoS) and Quality of Experience (QoE). Given the volatile and random nature of traffic data, this forecasting influences strategic development and investment decisions in the Internet Service Provider (ISP) industry. Modern machine learning algorithms have shown potential in dealing with complex Internet traffic prediction tasks, yet challenges persist. This thesis systematically explores these issues over five empirical studies conducted in the past three years, focusing on four key research questions: How do outlier data samples impact prediction accuracy for …

Classification Of Ddos Attack With Machine Learning Architectures And Exploratory Analysis, Amreen Anbar

Electronic Thesis and Dissertation Repository

The ever-increasing frequency of occurrence and sophistication of DDoS attacks pose a serious threat to network security. Accurate classification of DDoS attacks with efficiency is crucial in order to develop effective defense mechanisms. In this thesis, we propose a novel approach for DDoS classification using the CatBoost algorithm, on CICDDoS2019, a benchmark dataset containing 12 variations of DDoS attacks and legitimate traffic using real-world traffic traces. With a developed ensemble feature selection method and feature engineering, our model proves to be a good fit for DDoS attack type prediction. Our experimental results demonstrate that our proposed approach achieves high classification …

Transforming Large-Scale Virtualized Networks: Advancements In Latency Reduction, Availability Enhancement, And Security Fortification, Ibrahim Tamim

Electronic Thesis and Dissertation Repository

In today’s digital age, the increasing demand for networks, driven by the proliferation of connected devices, data-intensive applications, and transformative technologies, necessitates robust and efficient network infrastructure. This thesis addresses the challenges posed by virtualization in 5G networking and focuses on enhancing next-generation Radio Access Networks (RANs), particularly Open-RAN (O-RAN). The objective is to transform virtualized networks into highly reliable, secure, and latency-aware systems. To achieve this, the thesis proposes novel strategies for virtual function placement, traffic steering, and virtual function security within O-RAN. These solutions utilize optimization techniques such as binary integer programming, mixed integer binary programming, column generation, …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Experimenting An Edge-Cloud Computing Model On The Gpulab Fed4fire Testbed, Vikas Tomer, Sachin Sharma

Conference papers

There are various open testbeds available for testing algorithms and prototypes, including the Fed4Fire testbeds. This demo paper illustrates how the GPULAB Fed4Fire testbed can be used to test an edge-cloud model that employs an ensemble machine learning algorithm for detecting attacks on the Internet of Things (IoT). We compare experimentation times and other performance metrics of our model based on different characteristics of the testbed, such as GPU model, CPU speed, and memory. Our goal is to demonstrate how an edge-computing model can be run on the GPULab testbed. Results indicate that this use case can be deployed seamlessly …

Total Sky Imager Project, Ryan D. Maier, Benjamin Jack Forest, Kyle X. Mcgrath

Mechanical Engineering

Solar farms like the Gold Tree Solar Farm at Cal Poly San Luis Obispo have difficulty delivering a consistent level of power output. Cloudy days can trigger a significant drop in the utility of a farm’s solar panels, and an unexpected loss of power from the farm could potentially unbalance the electrical grid. Being able to predict these power output drops in advance could provide valuable time to prepare a grid and keep it stable. Furthermore, with modern data analysis methods such as machine learning, these predictions are becoming more and more accurate – given a sufficient data set. The …

A Quantitative Comparison Of Algorithmic And Machine Learning Network Flow Throughput Prediction, Cayden Wagner

All Theses

Applications ranging from video meetings, live streaming, video games, autonomous vehicle operations, and algorithmic trading heavily rely on low latency communication to operate optimally. A solution to fully support this growing demand for low latency is called dual-queue active queue management (AQM). Dual-queue AQM's functionality is reduced without network traffic throughput prediction.

Perhaps due to the current popularity of machine learning, there is a trend to adopt machine learning models over traditional algorithmic throughput prediction approaches without empirical support. This study tested the effectiveness of machine learning as compared to time series forecasting algorithms in predicting per-flow network traffic throughput …

Machine Learning For Unmanned Aerial System (Uas) Networking, Jian Wang

Doctoral Dissertations and Master's Theses

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex …

Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez

Electronic Thesis and Dissertation Repository

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …

Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley

Graduate Theses and Dissertations

Identifying freight patterns in transit is a common need among commercial and municipal entities. For example, the allocation of resources among Departments of Transportation is often predicated on an understanding of freight patterns along major highways. There exist multiple sensor systems to detect and count vehicles at areas of interest. Many of these sensors are limited in their ability to detect more specific features of vehicles in traffic or are unable to perform well in adverse weather conditions. Despite this limitation, to date there is little comparative analysis among Laser Imaging and Detection and Ranging (LIDAR) sensors for freight detection …

A Literature Survey And Bibliometric Analysis Of Application Of Artificial Intelligence Techniques On Wireless Mesh Networks, Smita R. Mahajan Mrs., Harikrishnan R Dr., Ketan Kotecha Dr.

Library Philosophy and Practice (e-journal)

Recent years have seen a surge in the use of technology for executing transactions in both online and offline modes. Various industries like banking, e-commerce, and private organizations use networks for the exchange of confidential information and resources. Network security is thus of utmost importance, with the expectation of effective and efficient analysis of the network traffic. Wireless Mesh Networks are effective in communicating information over a vast span with minimal costs. A network is evaluated based on its security, accessibility, and extent of interoperability. Artificial Intelligence techniques like machine learning and deep learning have found widespread use to solve …

Identification Of Users Via Ssh Timing Attack, Thomas J. Flucke

Master's Theses

Secure Shell, a tool to securely access and run programs on a remote machine, is an important tool for both system administrators and developers alike. The technology landscape is becoming increasingly distributed and reliant on tools such as Secure Shell to protect information as a user works on a system remotely. While Secure Shell accounts for the abuses the security of older tools such as telnet overlook, it still has fundamental vulnerabilities which leak information about both the user and their activities through timing attacks. The OpenSSH client, the implementation included in all Linux, Mac, and Windows computers, sends each …

Crude Oil Prices Forecasting: Time Series Vs. Svr Models, Xin James He

Journal of International Technology and Information Management

This research explores the weekly crude oil price data from U.S. Energy Information Administration over the time period 2009 - 2017 to test the forecasting accuracy by comparing time series models such as simple exponential smoothing (SES), moving average (MA), and autoregressive integrated moving average (ARIMA) against machine learning support vector regression (SVR) models. The main purpose of this research is to determine which model provides the best forecasting results for crude oil prices in light of the importance of crude oil price forecasting and its implications to the economy. While SVR is often considered the best forecasting model in …

Evaluating Load Adjusted Learning Strategies For Client Service Levels Prediction From Cloud-Hosted Video Servers, Ruairí De Fréin, Obinna Izima, Mark Davis

Conference papers

Network managers that succeed in improving the accuracy of client video service level predictions, where the video is deployed in a cloud infrastructure, will have the ability to deliver responsive, SLA-compliant service to their customers. Meeting up-time guarantees, achieving rapid first-call resolution, and minimizing time-to-recovery af- ter video service outages will maintain customer loyalty.

To date, regression-based models have been applied to generate these predictions for client machines using the kernel metrics of a server clus- ter. The effect of time-varying loads on cloud-hosted video servers, which arise due to dynamic user requests have not been leveraged to improve prediction …

Real-Time Intrusion Detection Using Multidimensional Sequence-To-Sequence Machine Learning And Adaptive Stream Processing, Gobinath Loganathan

Electronic Thesis and Dissertation Repository

A network intrusion is any unauthorized activity on a computer network. There are host-based and network-based Intrusion Detection Systems (IDS's), of which there are each signature-based and anomaly-based detection methods. An anomalous network behavior can be defined as an intentional violation of the expected sequence of packets. In a real-time network-based IDS, incoming packets are treated as a stream of data. A stream processor takes any stream of data or events and extracts interesting patterns on the fly. This representation allows applying statistical anomaly detection using sequence prediction algorithms as well as using a stream processor to perform signature-based intrusion …

Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal

The Summer Undergraduate Research Fellowship (SURF) Symposium

In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples …

Operating System Identification By Ipv6 Communication Using Machine Learning Ensembles, Adrian Ordorica

Graduate Theses and Dissertations

Operating system (OS) identification tools, sometimes called fingerprinting tools, are essential for the reconnaissance phase of penetration testing. While OS identification is traditionally performed by passive or active tools that use fingerprint databases, very little work has focused on using machine learning techniques. Moreover, significantly more work has focused on IPv4 than IPv6. We introduce a collaborative neural network ensemble that uses a unique voting system and a random forest ensemble to deliver accurate predictions. This approach uses IPv6 features as well as packet metadata features for OS identification. Our experiment shows that our approach is valid and we achieve …

Mobile Computing: Challenges And Opportunities For Autonomy And Feedback, Ole J. Mengshoel, Bob Iannucci, Abe Ishihara

Ole J Mengshoel