Computer Sciences Commons^™

Hybrid Machine And Deep Learning-Based Cyberattack Detection And Classification In Smart Grid Networks, Adedayo Aribisala

Electronic Theses and Dissertations

Power grids have rapidly evolved into Smart grids and are heavily dependent on Supervisory Control and Data Acquisition (SCADA) systems for monitoring and control. However, this evolution increases the susceptibility of the remote (VMs, VPNs) and physical interfaces (sensors, PMUs LAN, WAN, sub-stations power lines, and smart meters) to sophisticated cyberattacks. The continuous supply of power is critical to power generation plants, power grids, industrial grids, and nuclear grids; the halt to global power could have a devastating effect on the economy's critical infrastructures and human life.

Machine Learning and Deep Learning-based cyberattack detection modeling have yielded promising results when …

A Machine Learning Approach For Reconnaissance Detection To Enhance Network Security, Rachel Bakaletz

Electronic Theses and Dissertations

Before cyber-crime can happen, attackers must research the targeted organization to collect vital information about the target and pave the way for the subsequent attack phases. This cyber-attack phase is called reconnaissance or enumeration. This malicious phase allows attackers to discover information about a target to be leveraged and used in an exploit. Information such as the version of the operating system and installed applications, open ports can be detected using various tools during the reconnaissance phase. By knowing such information cyber attackers can exploit vulnerabilities that are often unique to a specific version.

In this work, we develop an …

Machine Learning Approaches To Dribble Hand-Off Action Classification With Sportvu Nba Player Coordinate Data, Dembe Stephanos

Electronic Theses and Dissertations

Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification …

Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), David Schmidt

Electronic Theses and Dissertations

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its …

Predicting Intraday Financial Market Dynamics Using Takens' Vectors; Incorporating Causality Testing And Machine Learning Techniques, Abubakar-Sadiq Bouda Abdulai

Electronic Theses and Dissertations

Traditional approaches to predicting financial market dynamics tend to be linear and stationary, whereas financial time series data is increasingly nonlinear and non-stationary. Lately, advances in dynamical systems theory have enabled the extraction of complex dynamics from time series data. These developments include theory of time delay embedding and phase space reconstruction of dynamical systems from a scalar time series. In this thesis, a time delay embedding approach for predicting intraday stock or stock index movement is developed. The approach combines methods of nonlinear time series analysis with those of causality testing, theory of dynamical systems and machine learning (artificial …