Collaboration has become a key feature of modern software, allowing teams to work together effectively in real-time while in different locations. In order for a user to communicate their intention to several distributed peers, computing devices must exchange high-frequency updates with transient metadata like mouse position, text range highlights, and temporary comments. Current peer-to-peer awareness solutions have high time and space complexity due to the ever-expanding logs that each client must maintain in order to ensure robust collaboration in eventually consistent environments. This paper proposes an awareness Conflict-Free Replicated Data Type (CRDT) library that provides the tooling to support an eventually consistent, decentralized, and robust multi-user collaborative environment. Our library is tuned for rapid iterative updates that communicate fine-grained user actions across a network of collaborators. Our approach holds memory constant for subsequent writes to an existing key on a shared resource and completely prunes stale data from shared documents. These features allow us to keep the CRDT's memory footprint small, making it a feasible solution for memory constrained applications. Results show that our CRDT implementation is comparable to or exceeds the performance of similar data structures in high-frequency read/write scenarios.

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Traditional Machine Learning (ML) methods usually rely on a central server to per-form ML tasks. However, these methods have problems like security risks, datastorage issues, and high computational demands. Federated Learning (FL), on theother hand, spreads out the ML process. It trains models on local devices and thencombines them centrally. While FL improves computing and customization, it stillfaces the same challenges as centralized ML in security and data storage. This thesis introduces a new approach combining Federated Learning and Decen-tralized Machine Learning (DML), which operates on an Ethereum Virtual Machine(EVM) compatible blockchain. The blockchain’s security and decentralized naturehelp improve transparency, trust, scalability, and efficiency. The main contributionsof this thesis include:1. Redesigning a semi-centralized system with enhanced privacy and the multi-KRUM algorithm, following the work of Shayan et al..2. Developing a new decentralized framework that supports both standard anddeep-learning FL, using the InterPlanetary File System (IPFS) and EthereumVirtual Machine (EVM)-compatible Smart Contracts.3. Assessing how well the system defends against common data poisoning attacks,using a version of Multi-KRUM that’s better at detecting outliers.4. Applying privacy methods to securely combine data from different sources.

Polar Spectrum Coding is a novel speech coding algorithm for narrowband voice communications. A polar Fourier transform of the signal is computed, and the magnitude and phase of the speech spectrum is encoded for transmission. The correlation between frames of speech signals is exploited to minimize the transmission rate required for intelligible speech. At the receiver, the encoded words are decoded and the spectrum reconstructed. An inverse Fourier transform is performed, and the result is the reconstructed speech waveform. Polar Spectrum Coding theory is explained. The sensitivities of various parameters on performance are explored, and performance in the presence of channel noise is measured. Directions for future research in the realm of Polar Spectrum Coding is suggested.

This report explains the development of a telephone line multiplexer to be used with a computer based personal paging system. The large geographical area coverage made possible by computer processing results in many calls having to cross telephone tariff boundaries. Since system users cannot be expected to pay long distance charges, dedicated lines which cross tariff boundaries must be leased. Multiplexing applied to those leased lines reduces the cost of data transmission, and is, therefore, justified. The design of the multiplexer proceeds from a specification set which is derived from user response requirements. Fundamentally, the response consists in advising the user, within a reasonable time, that a "page" has been accepted by the system. The specification set is then partitioned into functional blocks which are modeled using flow charts and state diagrams. Logic design follows directly from the models. Production of the multiplexer is followed by field installation. The savings resulting from the multiplexing are considerable.

Today, all modern computing systems are undoubtedly vulnerable to numerous types of attacks that could be targeted toward any layer of the system from dedicated hardware to highly abstracted software. Unfortunately, many devices and systems naturally contain inadequately protected components or software modules that un- dermine their security as a whole. Additionally, security is heavily variable system to system, and has a huge dependence on adequate implementation and ongoing support from device and software manufacturers. To address these various security issues in a very general way, TrustGuard, a containment security system utilizing an external device called the Sentry that would verify the activity of the host machine and control all incoming/outgoing communication accordingly, was created. To do this, Trust- Guard uses cryptographic memory protection schemes, a small trusted hardware and software base, and recomputation and checking of application behavior running on the host machine at an instruction-by-instruction granularity before allowing exter- nal communication to occur. Currently, however, the TrustGuard system only allows for one 8-byte chunk to be sent or received externally at one time, limiting overall throughput, and heavily polluting the main system caches in the case of large data transfers. To combat this limitation, This thesis proposes a system to allow for ef- ficient communication of large batches of data at once. In particular, it does so by using a small dedicated cache and efficient tree traversal techniques to asynchronously verify large chunks of program memory in stream-like fashion. This thesis primarily serves to provide a design, proof-of-concept, and collection of important information that will help future students implement such a system.

With rapid developments in communication technologies and awareness of security and privacy risks online, Security and Privacy Enhancing Networks (SPENs) have become increasingly popular. Especially during the COVID-19 pandemic, workplaces encouraged employees to take additional security measures, such as VPNs. In this work, we conduct a comprehensive study on website fingerprinting attacks. A comprehensive system model and threat model based on two types of SPENs (Virtual Private Networks and Tor Networks) are presented. Moreover, we demonstrate a website fingerprinting attack by ethically collecting website fetch data and analyzing the collected data using five different machine learning classification models including k nearest neighbors, decision tree, ada boost, and random forest. We find that SPENs are still vulnerable to website fingerprinting attacks which enable attackers to violate users’ behavioral privacy. However, it is not easy to get accurate results, especially over a large number of websites. Furthermore, we discuss a series of recommendations for SPENs to increase behavioral privacy for their customers. Finally, we cover a variety of directions that future work could take.

WiFi networks are susceptible to statistical traffic analysis attacks. Despite encryption, the metadata of encrypted traffic, such as packet inter-arrival time and size, remains visible. This visibility allows potential eavesdroppers to infer private information in the Internet of Things (IoT) environment. For example, it allows for the identification of sleep monitors and the inference of whether a user is awake or asleep. WiFi eavesdropping theoretically enables the identification of IoT devices without the need to join the victim's network. This attack scenario is more realistic and much harder to defend against, thus posing a real threat to user privacy. However, researchers have not thoroughly investigated this type of attack due to the noisy nature of wireless channels and the relatively low accuracy of WiFi sniffers. Furthermore, many countermeasures proposed in the literature are inefficient in addressing side-channel leakage in WiFi networks. They often burden internet traffic with high data overhead and disrupt the user experience by introducing deliberate delays in packet transmission. This dissertation investigates privacy leakage resulting from WiFi eavesdropping and proposes efficient defensive techniques. We begin by assessing the practical feasibility of IoT device identification in WiFi networks. We demonstrate how an eavesdropper can fingerprint IoT devices by passively monitoring the wireless channel without joining the network. After exploring this privacy attack, we introduce a traffic spoofing-based defense within the WiFi channel to protect against such threats. Additionally, we propose a more data-efficient obfuscation technique to counter traffic analytics based on packet size without adding unnecessary noise to the traffic.

In the rapidly advancing field of autonomous vehicles, ensuring the security and reliability of self-driving systems is crucial. Autonomous vehicle systems, such as cooperative adaptive cruise control (CACC), must undergo significant research and testing before their integration into commercial intelligent transportation systems. CACC considers multiple vehicles in close proximity as a single entity, or platoon, with each vehicle equipped with a controller that uses sensor-based measurements and vehicle-to-vehicle (V2V) communication to control inter-vehicle spacing. While this system offers numerous potential benefits for traffic safety and efficiency, it is also susceptible to False Data Injection (FDI) attacks, which can cause the system to behave in potentially life-threatening ways. Testing these scenarios in the real world is infeasible due to expense, safety concerns, and the use of theoretical technologies. This study presents an implementation of a vehicle platoon in a simulated environment where the vehicles' controllers were tuned to maintain desired inter-vehicle spacing. Various FDI signals were then implemented to demonstrate the feasibility of malicious attacks, including a novel parameterized sinusoidal FDI signal. Furthermore, acknowledging the necessity for future anomaly detection schemes and noise filtration, a theoretical optimal attack—generated using a model of the sinusoidal FDI attack and identification of optimal FDI values—was also evaluated.

The role of the South African National Defence Force (SANDF) has changed considerably in the last decade. The emphasis has moved from protecting the country's borders to peacekeeping duties in Central Africa and even further North. Communications between the peacekeeping missions and the military bases back in South Africa is vital to ensure the success of these missions. Currently use is made of satellite as well as High Frequency (HF) communications. There are drawbacks associated with these technologies (high cost and low data rates/interference respectively). Successful long distance ionospheric propagation in the low Very High Frequency (VHF) range will complement the existing infrastructure and enhance the success rate of these missions. This thesis presents a feasibility study to determine under what ionospheric conditions such low VHF communications will be possible. The International Reference Ionosphere (IRI) was used to generate ionospheric data for the reflection point(s) of the signal. The peak height of the ionospheric F2 layer (hmF2) was used to calculate the required antenna elevation angle. Once the elevation angle is known it is possible to calculate the required F2 layer critical frequency (foF2). The required foF2 value was calculated by assuming a Maximum Useable Frequency (MUF) of 20% higher than the planned operational frequency. It was determined that single hop propagation is possible during the daytime if the smoothed sunspot number (SSN) exceeds 15. The most challenging requirement for successful single hop propagation is the need of an antenna height of 23 m. For rapid deployment and semi-mobile operations within a jungle environment it may prove to be a formidable obstacle.