The ever-increasing demand for private and sensitive data transmission over wireless networks has made security a crucial concern in the current and future large-scale, dynamic, and heterogeneous wireless communication systems. To address this challenge, wireless researchers have tried hard to continuously analyze the jamming threats and come up with improved countermeausres. In this research, we have analyzed the jamming-vulnerabilities of the leading multi-carrier communication systems, Orthogonal Frequency Division Multiplexing (OFDM) and Single-Carrier Frequency Division Multiple Access (SC-FDMA). In order to lay the necessary theoretical groundwork, first we derived the analytical BER expressions for BPSK/QPSK and analytical upper and lower bounds for 16-QAM for OFDMA and SC-FDMA using Pilot Symbol Assisted Channel Estimation (PSACE) techniques in Rayleigh slow-fading channel that takes into account channel estimation error as well as pilot-jamming effect. From there we advanced to propose more novel attacks on the Cyclic Prefix (CP) of SC-FDMA. The associated countermeasures developed prove to be very effective to restore the system. We are first to consider the effect of frequency-selectivity and fading correlation of channel on the achievable rates of the legitimate system under pilot-spoofing attack. With respect to jamming mitigation techniques, our approaches are more focused on Anti-Jamming (AJ) techniques rather than Low Probability of Intercept (LPI) methods. The Channel State Information (CSI) of the two transceivers and the CSI between the jammer and the target play critical roles in ensuring the effectiveness of jamming and nulling attacks. Although current literature is rich with different channel estimation techniques between two legitimate transceivers, it does not have much to offer in the area of channel estimation from jammer's perspective. In this dissertation, we have proposed novel, computationally simple, deterministic, and optimal blind channel estimation techniques for PSK-OFDM as well as QAM-OFDM that estimate the jammer channel to the target precisely in high Signal-to-Noise (SNR) environment from a single OFDM symbol and thus perform well in mobile radio channel. We have also presented the feasibility analysis of estimating transceiver channel from jammer's perspective at the transmitter as well as receiver side of the underlying OFDM system. / Ph. D.

Thesis deals with testing of selected vulnerabilities from the IEC 61850 standard and following design of mitigation measures for selected vulnerabilities. Author simulated vulnerabilities of the GOOSE protocol, NTP attack and attack ona MMS client. Those attacks were GOOSE stNum, GOOSE semantic, GOOSE test bit,GOOSE replay, GOOSE flood, NTP spoofing and MMS password capture. Attacks on protocols GOOSE and MMS were successful, attack on NTP was only partially successful since the device confirmed receiving spoofed time, however it did not change it’s inner clock. Author then designed possible mitigation measures. Tool for automatic testing of selected vulnerabilities, parser for the GOOSE protocol and lightweight multiplatform parser for configuration files were created as well.The outcome of this thesis allows the implementation of lager scale tool for penetration testing of industrial networks as well as it allows implementation of discussed mitigation measures.

Cílem konverze hlasu (voice conversion, VC) je převést hlas zdrojového řečníka na hlas cílového řečníka. Technika je populární je u vtipných internetových videí, ale má také řadu seriózních využití, jako je dabování audiovizuálního materiálu a anonymizace hlasu (například pro ochranu svědků). Vzhledem k tomu, že může sloužit pro spoofing systémů identifikace hlasu, je také důležitým nástrojem pro vývoj detektorů spoofingu a protiopatření.    Modely VC byly dříve trénovány převážně na paralelních (tj. dva řečníci čtou stejný text) a na vysoce kvalitních audio materiálech. Cílem této práce bylo prozkoumat vývoj VC na neparalelních datech a na signálech nízké kvality, zejména z veřejně dostupné databáze VoxCeleb. Práce vychází z moderní architektury AutoVC definované Qianem et al. Je založena na neurálních autoenkodérech, jejichž cílem je oddělit informace o obsahu a řečníkovi do samostatných nízkodimenzionýálních vektorových reprezentací (embeddingů). Cílová řeč se potom získá nahrazením embeddingu zdrojového řečníka embeddingem cílového řečníka. Qianova architektura byla vylepšena pro zpracování audio nízké kvality experimentováním s různými embeddingy řečníků (d-vektory vs. x-vektory), zavedením klasifikátoru řečníka z obsahových embeddingů v adversariálním schématu trénování neuronových sítí a laděním velikosti obsahového embeddingu tak, že jsme definovali informační bottle-neck v příslušné neuronové síti. Definovali jsme také další adversariální architekturu, která porovnává původní obsahové embeddingy s embeddingy získanými ze zkonvertované řeči. Výsledky experimentů prokazují, že neparalelní VC na nekvalitních datech je skutečně možná. Výsledná audia nebyla tak kvalitní případě hi fi vstupů, ale výsledky ověření řečníků po spoofingu výsledným systémem jasně ukázaly posun hlasových charakteristik směrem k cílovým řečníkům.

Remote data collection systems are widely used. One of the area is also data collection in energetics, where the energy consumption can be collected daily and presented to users on-line. The advantage of the remote data collection is possibility of frequent readings without a physical presence at the electrometers. The data transmission over the Internet can be subject of various attacks, which is the disadvantage. The understanding of attack method is the most important thing. The protection against the hackers is not complicated, but requires lot of attention. This master's thesis is focused on testing security of the communication unit LAN of remote data acquisition against attacks from the Internet. The next aim of this thesis is to describe algorithm of particular attack, needed recourses for their realization and method of their measurement and evaluation. Communication unit and component composition for attacks simulation is described in the first part of this thesis. The next part is focused on scanning for hosts and ports. The main part of this thesis is focused on the denial of service attacks and man in the middle attacks. In the end of my thesis is described selection of cryptographic system for remote data acquisition and is showed possibility of authentication mirroring. Problems of physical security are described too. The result of this thesis is script implementing all attacks, which are described.

Tato práce se zabývá rozšířením snímače otisků prstů Touchless Biometric Systems 3D-Enroll o jednotku detekce živosti prstu na základě žil. Bylo navrhnuto a zkonstruováno hardwarové řešení s využitím infračervených diod. Navržené softwarové řešení pracuje ve dvou různých režimech: detekce živosti na základě texturních příznaků a verifikace uživatelů na základě porovnávání žilních vzorů. Datový soubor obsahující přes 1100 snímků jak živých prstů tak jejich falsifikátů vznikl jako součást této práce a výkonnost obou zmíněných režimů byla vyhodnocena na tomto datovém souboru. Na závěr byly navrhnuty materiály vhodné k výrobě falsifikátů otisků prstů umožňující oklamání detekce živosti pomocí žilních vzorů.

This MSc Thesis focuses on principles, practical performability and security against four attacks used in contemporary local-area networks: CAM Table Overflow capable of capturing traffic in switched networks, ARP Man-in-the-Middle, whose target is to redirect or modify traffic and against two variants of VLAN Hopping attack allowing a hacker to send and capture data from VLANs he has no access to.

<p>In this work, machine learning (ML) modeling is proposed for the detection and classification of global positioning system (GPS) spoofing in unmanned aerial vehicles (UAVs). Three testing scenarios are implemented in an outdoor yet controlled setup to investigate static and dynamic attacks. In these scenarios, authentic sets of GPS signal features are collected, followed by other sets obtained while the UAV is under spoofing attacks launched with a software-defined radio (SDR) transceiver module. All sets are standardized, analyzed for correlation, and reduced according to feature importance prior to their exploitation in training, validating, and testing different multiclass ML classifiers. Two schemes for the dataset are proposed, location-dependent and location-independent datasets. The location-dependent dataset keeps the location specific features which are latitude, longitude, and altitude. On the other hand, the location-independent dataset excludes these features. The resulting performance evaluation of these classifiers shows a detection rate (DR), misdetection rate (MDR), and false alarm rate (FAR) better than 92%, 13%, and 4%, respectively, together with a sub-millisecond detection time. Hence, the proposed modeling facilitates accurate real-time GPS spoofing detection and classification for UAV applications.</p> <p><br></p> <p>Then, a three-class ML model is implemented on a UAV with a Raspberry Pi processor for classifying the two GPS spoofing attacks (i.e., static, dynamic) in real-time. First, several models are developed and tested utilizing the prepared dataset. Models evaluation is carried out using the DR, F-score, FAR, and MDR, which all showed an acceptable performance. Then, the optimum model is loaded to the onboard processor and tested for real-time detection and classification. Location-dependent applications, such as fixed-route public transportation, are expected to benefit from the methodology presented herein as the longitude, latitude, and altitude features are characterized in the implemented model.</p>

Machine Learning (ML) is the current trend that most industries opt for to improve their business and operations. ML has also been adopted in the financial markets, where well-funded financial institutions employ the latest ML algorithms to gain an advantage on the market. The darker side of ML is the potential emergence of complex algorithmic trading schemes that are abusive and manipulative. Because of this, it is inevitable that ML will be applied to financial market surveillance in order to detect these abusive and manipulative trading strategies. Ideally, an accurate ML detection model would be developed with data from many financial institutions or trading venues. However, such ML models require vast quantities of data, which poses a problem in market surveillance where data is sensitive or limited. Data sharing between companies or countries is typically accompanied by legal and privacy concerns. By training ML models on distributed datasets, Federated Learning (FL) overcomes these issues by eliminating the need to centralise sensitive data. This thesis aimed to address these ML related issues in market surveillance by implementing and evaluating a FL model. FL enables a group of independent data-holding clients with the same intention to build a shared ML model collaboratively without compromising private data. In this work, a ML model is initially deployed in a centralised data setting and trained to detect the manipulative trading scheme known as spoofing. The LSTM-Autoencoder was the model chosen method for this task. The same model is also implemented in a federated setting but with decentralised data, using the FL framework FEDn. Another FL framework, Flower, is also employed to evaluate the performance of FEDn. Experiments were conducted comparing the FL models to the conventional centralised learning model, as well as comparing the two frameworks to each other. The results showed that under certain circumstances, the FL models performed better than the centralised model in detecting spoofing. FEDn was equivalent to Flower in terms of detection performance. In addition, the results indicated that Flower was marginally faster than FEDn. It is assumed that variations in the experimental setup and stochasticity account for the performance disparity.

Digital Selective Calling (DSC) is a vital maritime communications and safety system, enabling ships in distress to alert nearby vessels and coast guard stations of their emergency. While DSC is suitable for calling, its technical format is substandard from a cybersecurity perspective. Specifically, this work aims to demonstrate that Very High Frequency (VHF) DSC distress calls can be spoofed using Software Defined Radio (SDR). A VHF DSC distress call encoder and VHF DSC SDR signal constructor were developed. The forged distress call was transmitted using various techniques to two different DSC decoder programs, as well as to the maritime VHF transceiver ICOM IC-M510. It was shown that all of the targeted DSC decoders were susceptible to spoofing. This thesis concludes that VHF DSC distress calls can be spoofed using SDR, and infers that the DSC system as a whole has inherent security vulnerabilities that need to be addressed to assure the safety of future seafaring.

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