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The thesis seeks to improve the accuracy of indoor wireless local area network (WLAN) location detection. The main task of the project is the design and analysis of a solution, which utilizes the packets which are already emitted by WLAN devices. The system consists of a signal receiver and signal processing. The positioning system does not transmit, thus the detection is completely passive. The result of measurements on received frames is used to calculate the WLAN transmitter's position. It does not require any transmissions, thus the detection is completely passive. The result of the measurements can be used to calculate the WLAN transmitter’s position. Location systems are more and more viewed as a necessary element of a WLAN system. Positioning accuracy is the most important issue in location system, especial in the indoor WLAN location detection. Indoor location systems are affected by indoor environment both due to multi-path and man-made effects. To resist these problems, we introduce a way to detect the arrival of the first instance of the signal by detecting the signal header. In our experiment, we timestamp the arrival of an IEEE 802.11b header. In our prototype the logic necessary to do this is implemented in an FPGA, specifically that of the Ettus Research USRP. The additional logic is quite small and might easily be added to the receiver in an access point, thus reducing the deployment cost of a location system in a real network. The proposed solution was experimentally verified. From our experiments, the detection works without requiring any changes to the hardware or software of the mobile device. By exploiting existing IEEE 802.11b transmissions the cost and difficulty of deployment is simplified due to the wide usage of IEEE 802.11b in mobile devices. Additionally, the preamble has good correlation properties making it is easy to detect the arrival of a IEEE 802.11frame. Our implementation is based upon open source hardware and software making it possible to implement this solution. A relatively low cost FPGA can be used as the correlation and timestamp circuit is rather simple (in terms of numbers of gates), making this solution feasible for commercial implementation. The method, implementation, testing, and analysis are presented in detail in the thesis. / Avhandlingen syftar till att förbättra noggrannheten i inomhus trådlösa lokala nätverk (WLAN) placering upptäckt. Huvuduppgiften för projektet är design och analys av en lösning som utnyttjar paketen som redan avges av WLAN-enheter. Systemet består av en signal mottagare och signalbehandling. Det kräver inga transmissioner, alltså upptäckt är helt passiv. Resultatet av mätningarna kan användas för att beräkna WLAN-sändarens läge. Målet är att förbättra noggrannheten i inomhus plats uppskattning. Läge system alltmer ses som en nödvändig del av WLAN system. Positioneringsnoggrannheten ses som den viktigaste frågan i läge system, speciellt för inomhusbruk WLAN baserade location. Läge system påverkas mer av inomhusmiljöer än utemiljön, eftersom det finns mer multi-path fading och konstgjorda effekter. Att minska dessa problem, vi införa ett sätt att känna av signalen ankomst genom att förbättra upptäckten av ankomsten av IEEE 802.11-huvudet. Detta kan bidra till att besegra multipath effekt och enkla metoden skulle kunna minska kostnaderna för placering i framtiden kopplingspunkter. Den föreslagna lösningen har verifierats experimentellt. Från vårt experiment fungerar upptäckt utan att kräva några ändringar i hårdvara eller mjukvara för den mobila enheten. Genom att utnyttja befintliga IEEE 802.11b sändningar kostnaden och svårigheten att utbyggnaden är förenklad på grund av den breda användningen av IEEE 802.11b i mobila enheter. Dessutom "preamble" har god korrelation egenskaper som gör det lätt att upptäcka ankomsten av en IEEE 802.11-ramen. Vår genomfört bygger på öppen källkod maskin-och programvara som gör det möjligt att genomföra denna lösning. En relativt låg kostnad FPGA kan användas som korrelation och tidstämpel kretsen är ganska enkel (i termer av antalet logikelement), vilket gör denna lösning vara möjlig för kommersiell tillämpning. Metoden, implementation, testning och analys presenteras i detalj i avhandlingen.

Phishing is a social engineering attack to deceive and persuade people to divulge private information like usernames and passwords, account details (including bank account details), and social security numbers. Phishers typically utilize e-mail, chat, text messages, or social media. Despite the presence of automatic anti-phishing filters, phishing messages reach online users' inboxes. Understanding the influence of phishing techniques and individual differences on susceptibility and detection accuracy is an important step toward creating comprehensive behavioral and organizational anti-phishing awareness programs. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to explore the nature of phishing threats that including identifying attack intentions, and psychological and design techniques of phishing attacks. Essay 2 seeks to understand the relative influence of attack techniques and individual phishing experiential traits on people's phishing susceptibility. Essay 3 seeks to understand an individual's cognitive and affective differences that differentiate between an individual's phishing detection accuracy.

In today’s world the internet and social media are omnipresent and information is accessible to everyone. This shifted the advantage from those who have access to information to those who do so first. Identifying new events as they emerge is of substantial value to financial institutions who consider realtime information in their decision making processes, as well as for journalists that report about breaking news and governmental agencies that collect information and respond to emergencies. First Story Detection is the task of identifying those documents in a stream of documents that talk about new events first. This seemingly simple task is non-trivial as the computational effort increases with every processed document. Standard approaches to solve First Story Detection determine a document’s novelty by comparing it to previously seen documents. This results in the highest reported accuracy but even the currently fastest system only scales to 10% of the Twitter stream. In this thesis, we propose a new algorithm family, called memory-based methods, able to scale to the full Twitter stream on a single core. Our memory-based method computes a document’s novelty up to two orders of magnitude faster than state-of-the-art systems without sacrificing accuracy. This thesis additional provides original work on the impact of processing unbounded data streams on detection accuracy. Our experiments reveal for the first time that the novelty scores of state-of-the-art comparison based and memory-based methods decay over time. We show how to counteract the discovered novelty decay and increase detection accuracy. Additionally, we show that memory-based methods are applicable beyond First Story Detection by building the first real time rumour detection system on social media streams.

This work first implements a prototype of the proof-of-location scheme Vouch in order for an evaluation to be conducted in a real-world environment. With simulations of the scheme showing promising results, the next step would be an evaluation of the schemes performance in the real-world. This report introduces the scheme and similar works in relation to implementation and evaluation. Method of implementation is presented followed by an evaluation. The evaluation focuses on detection accuracy of the scheme by investigating impacts of the inevitably arising staleness. Contributors of staleness are identified and their impacts on overall detection accuracy of Vouch are measured. With the prototype successfully implemented, measurements showed a trend in improving detection accuracy with higher proof update frequencies, reaching as high as 9̃5% in a high velocity environment. The results shows that the Vouch scheme not only gives promising results in simulation, but also in the real-world.

Until recent years, terahertz (THz) waves were an undiscovered, or most importantly, an unexploited area of electromagnetic spectrum. This was due to difficulties in generation and detection of THz waves. Recent advances in hardware technology have started to open up the field to new applications such as THz imaging. This non-destructive and non-contact imaging technique can penetrate through diverse materials such that internal structures, in some cases invisible to other imaging modalities, can be visualized. Today, there are variety of techniques available to generate and detect THz waves in both pulsed and continuous fashion in two different geometries; transition, and reflection modes. In this thesis continuous wave THz imaging was employed for higher spatial resolution. However, with any new technology comes its challenges; automated processing of THz images can be quite cumbersome. Low contrast and the presence of a widely unknown type of noise make the analysis of these images difficult. In this work, there is an attempt to detect defects in composite material via segmentation by using a Terahertz imaging system. According to our knowledge, this is the first time that this type of materials are being tested under Terahertz cameras to detect manufacturing defects in aerospace industry. In addition, segmentation accuracy of THz images have been investigated by using a phantom. Beyond the defect detection for composite materials, this can establish some general knowledge about Terahertz imaging, its capabilities and limitations. To be able to segment the THz images successfully, pre-processing techniques are inevitable. In this thesis, a variety of different image processing techniques, self-developed or available from literature, have been employed for image enhancement. These methods range from filtering to contrast adjustment to fusion of phase and amplitude images by using fuzzy set theory, to just name a few. The result of pre-procssing and segmentation methods demonstrates promising outcome for future work in this field.

Until recent years, terahertz (THz) waves were an undiscovered, or most importantly, an unexploited area of electromagnetic spectrum. This was due to difficulties in generation and detection of THz waves. Recent advances in hardware technology have started to open up the field to new applications such as THz imaging. This non-destructive and non-contact imaging technique can penetrate through diverse materials such that internal structures, in some cases invisible to other imaging modalities, can be visualized. Today, there are variety of techniques available to generate and detect THz waves in both pulsed and continuous fashion in two different geometries; transition, and reflection modes. In this thesis continuous wave THz imaging was employed for higher spatial resolution. However, with any new technology comes its challenges; automated processing of THz images can be quite cumbersome. Low contrast and the presence of a widely unknown type of noise make the analysis of these images difficult. In this work, there is an attempt to detect defects in composite material via segmentation by using a Terahertz imaging system. According to our knowledge, this is the first time that this type of materials are being tested under Terahertz cameras to detect manufacturing defects in aerospace industry. In addition, segmentation accuracy of THz images have been investigated by using a phantom. Beyond the defect detection for composite materials, this can establish some general knowledge about Terahertz imaging, its capabilities and limitations. To be able to segment the THz images successfully, pre-processing techniques are inevitable. In this thesis, a variety of different image processing techniques, self-developed or available from literature, have been employed for image enhancement. These methods range from filtering to contrast adjustment to fusion of phase and amplitude images by using fuzzy set theory, to just name a few. The result of pre-procssing and segmentation methods demonstrates promising outcome for future work in this field.