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Articles 1 - 8 of 8
Full-Text Articles in Cybersecurity
Using Siamese Neural Networks To Effectively Detect Trojans In Fpgas When Trojans Manipulate Encryption Operations At The Bitstream Level, Kylie Arnett
Graduate Theses and Dissertations (2019 - present)
This research investigates security vulnerabilities in Field-Programmable Gate Arrays (FPGAs) at the bitstream level, focusing on hardware trojans (HTs) that manipulate encryption operations. This study addresses two critical questions: (1) The feasibility of exploiting FPGA bitstreams to selectively bypass encryption operations when a predefined input pattern is observed (all ones), thereby exposing sensitive data, and (2) the efficacy of Siamese Neural Networks (SNNs) in detecting such trojans with high accuracy. FPGAs are vulnerable to malicious modifications during manufacturing or deployment, posing risks to data integrity and system functionality. In this work, a trojan is inserted into a Xilinx series-7 FPGA …
Polyglot File Detection For Forensic Investigations, Chase Stevens
Polyglot File Detection For Forensic Investigations, Chase Stevens
Graduate Theses and Dissertations (2019 - present)
As technology has become far more ubiquitous over the years, so too has the amount of digital evidence that can and needs to be collected and processed. Forensic tools are developed in response to the growing need, but are limited to what they are programmed to do. One such forensic tool is Autopsy, one of the most widely used open-source tools. Autopsy uses known file-type signatures (e.g., headers, trailers) to identify and recover files from forensic images. Polyglot files introduce a unique problem to both these forensic tools and investigators alike. In the context of the research conducted, polyglot files …
Detecting Sophisticated Cyberattacks On Public Water Infrastructure, Ayrton Purdy
Detecting Sophisticated Cyberattacks On Public Water Infrastructure, Ayrton Purdy
Graduate Theses and Dissertations (2019 - present)
In recent years there has been an increasing number of cyberattacks on public water generation and distribution systems. Advanced persistent attackers could usurp sensors and control systems to contaminate public drinking water. In order to conceal their malicious activity, they can manipulate sensor data flows to give the appearance of normal activity. The compromised sensors would report normal chemical levels even though unsafe water is entering the distribution system. In response, this research proposes a multi-sensor, cross-comparison approach to anomaly detection. The proposed approach is designed to detect sophisticated cyberattacks which are not easily detectable using traditional cyber tools. The …
Developing A Framework For Microchip Design Recovery, Eric Diep
Developing A Framework For Microchip Design Recovery, Eric Diep
Graduate Theses and Dissertations (2019 - present)
Due to the increase in diverse chip production over the past decade, reverse engineering has become a difficult and daunting task. This research develops a methodology for microchip design recovery, seeking to validate and reproduce prior approaches to physical reverse engineering using low-cost tools and techniques. We used mechanical hardware abrasion tools and techniques to delayer and capture silicon integrated chip (IC) layout. We focused on the Mifare Classic EVl microchip, commonly implemented in public transit/transportation cards, to extract information for design recovery. The research explores limitations and advantages of mechanical abrasion and optical microscopy in context to modem chip …
Nonlinear Phase Space Analysis For Anomaly Detection In Ros 2 Communications: Detecting Man-In-The-Middle Attacks In Simulated Environments, William L. Locklier
Nonlinear Phase Space Analysis For Anomaly Detection In Ros 2 Communications: Detecting Man-In-The-Middle Attacks In Simulated Environments, William L. Locklier
Graduate Theses and Dissertations (2019 - present)
Robot Operating System 2 (ROS 2) marks a significant advancement over its predecessor through the transition from a centralized to a decentralized architecture, integrating the Data Distribution Service (DDS) to support real-time, scalable communications. Despite these improvements, inherent vulnerabilities in the ROS 2 communication stack continue to leave these systems exposed to sophisticated network-based attacks. This study leveraged nonlinear phase space analysis (NLPSA) as an intrusion detection system (IDS) to detect man-in-the-middle (MitM) attack anomalies in ROS 2 traffic. Grounded in Takens’ embedding theorem, NLPSA reconstructs the phase space of communication features and compares the resulting structure against a baseline …
Application Of Graph Neural Networks On Phase Space Graphs For Cybersecurity, Parker H. Cole
Application Of Graph Neural Networks On Phase Space Graphs For Cybersecurity, Parker H. Cole
Graduate Theses and Dissertations (2019 - present)
Non-linear phase space analysis may be used to represent time-series data as graph data with transitions between states in the time domain. By studying these transitions, we can predict anomalies within the system. Previous research has demonstrated success in learning from phase graphs for malware and seizure detection. These solutions either require extracting global features or converting the graph into an image for convolutional neural networks (CNNs), which adds a layer of complexity and limits the size and potential expressiveness of a graph. To sidestep current limitations, this study proposed Graph Neural Networks (GNNs) for analyzing phase graphs. GNNs do …
Out-Of-Band Anomaly Detection For Real Time Operating Systems, Jeffrey K. Holifield
Out-Of-Band Anomaly Detection For Real Time Operating Systems, Jeffrey K. Holifield
Graduate Theses and Dissertations (2019 - present)
Real Time Operating Systems (RTOS) are increasing present throughout the industrial, business, defense, and healthcare spaces. These lightweight and efficient operating systems are designed to run on embedded, resource constrained devices, often within cyber-physical systems (CPS). A defining characteristic ofRTOSs is that they are deterministic. Tasks are scheduled to run on fixed timelines within guaranteed execution windows. In Industry 4.0 applications for example, sensors must receive and process inputs within a fixed schedule to ensure products are properly manufactured. This requires guaranteed service at fixed time periods. To accomplish this, RTOSs must conform to worst case execution times (WCETs) as …
Hybrid Deep Learning-Based Model For Eclipse Attack Detection On Ethereum Network, Dhanasak Bhumichai
Hybrid Deep Learning-Based Model For Eclipse Attack Detection On Ethereum Network, Dhanasak Bhumichai
Graduate Theses and Dissertations (2019 - present)
An eclipse attack is a significant cyber threat targeting the network layer of blockchain platforms. Detecting eclipse attacks is challenging for several reasons. First, there are no available datasets for training and testing models. Second, comprehensive studies identifying features to detect eclipse attacks are lacking. Additionally, the amount of eclipse network traffic is much smaller than that of normal network traffic, which leads to imbalanced samples. Moreover, the characteristics of eclipse network traffic closely resemble those of normal traffic, causing overlapping samples, which makes it challenging for traditional classifiers to learn how to identify eclipse attacks. To address these challenges, …