Social media today is an important source of information about various events happening around the world. Among various social networking platforms, microtext based ones such as Twitter are of special interest as they are also a rich source of real-time events. In this thesis, our goal is to study the effectiveness of using Twitter as a social sensor for obtaining real-time information on road traffic conditions. Specifically, we focus on: i) identifying tweets that contain traffic event related information, ii) classify such tweets into six main groups of accident, fire, road construction, police activities, weather and others, iii) extract fine-grained location information about the traffic incident by analyzing tweet text. Our experimental results show that using Twitter as a social sensor for obtaining rich information about traffic events is indeed a promising approach. We show that we can correctly detect traffic related tweets with an accuracy of 81%. Moreover, the accuracy of correctly classifying traffic related tweets into one of the six categories is 97%. Lastly, our experimental results show that using only geo-tags of tweets is not sufficient for fine-grained localization of traffic incidents due to two reasons: i) a vast majority of traffic related tweets do not contain geo-tags, and ii) the location mentioned in the tweet text and the geo-tag of a tweet do not always agree. Such observations prove that fine-grained localization of traffic incidents from tweet must also include analysis of the tweet text using Natural Language Processing techniques.

Diese Dissertation soll einen Beitrag zur Weiterentwicklung der luftgestützten Verkehrslageerfassung leisten. Als Plattform dafür dient ein flugzeuggetragenes Kamerasystem, welches mit einem Inertialsystem gekoppelt ist. Vorgestellt werden hauptsächlich bildverarbeitende Algorithmen, welche an die Bildaufnahme anschließend bis hin zur Ermittlung der verkehrstechnischen Kenngrößen zum Einsatz kommen. Nach kurzer Skizzierung der verwendeten Hardware wird die Kalibrierung der Kameraeinbauwinkel durch Testflüge erläutert und auf ihre Genauigkeit hin untersucht. Es wird gezeigt, dass die Orientierungsdaten nicht die vom Hersteller angegebene Genauigkeit erreichen, was jedoch für die Verkehrslageerfassung nur von geringer Bedeutung ist. Anschließend an die Bildaufbereitung, welche die Orthobildgenerierung sowie die Eingrenzung der verkehrsaktiven Flächen beinhaltet, wird zur Ermittlung der Fahrzeugdichte ein zweistufiger Fahrzeugerkennungsalgorithmus entwickelt, welcher zunächst auf Kantenfilterbasis möglichst schnell Hypothesen erstellt. Diese werden in einer zweiten Phase durch eine Support Vector Machine überprüft, wobei ein Großteil der Fehlhypothesen verworfen wird. Die Erkennung erreicht bei guten Voraussetzungen Vollständigkeiten bis zu 90 Prozent bei sehr geringem Anteil von Fehldetektionen. Anschließend wird ein auf Singulärwertzerlegung basierender Tracking-Algorithmus verwendet, um Fahrzeughypothesen in benachbarten Bildern zu assoziieren und die mittleren Geschwindigkeiten zu ermitteln. Die erhaltenen Geschwindigkeiten unterscheiden sich um weniger als zehn km/h von den manuell erhobenen. Abschließend wird eine alternative Orientierungsmethode vorgestellt, welche auf Basis von GPS-Positionen und Bildinformationen automatisch die Fluglage ermittelt. Dies geschieht durch die Extraktion und das Matching von Straßensegmenten sowie zusätzliche Passpunktverfolgung. Die Ergebnisse weisen Genauigkeiten von etwa 0,1 bis 0,2 Grad auf. / This dissertation should make a contribution to the further development of airborne traffic detection. The used hardware is an airborne camera system combined with an inertial measurement unit for orientation determination. Mainly computer vision algorithms are presented, which are applied afterwards the image acquisition up to the determination of the most important traffic data. After a short presentation of the used hardware the calibration of the camera''s alignment angles during test flights is explained and its accuracy is analyzed. It is shown that the orientation data doesn''t reach the specified accuracy, which is fortunately less important for traffic detection. After the image preparation, which contains the ortho image generation as well as the clipping of traffic areas, a two-stage vehicle detection algorithm is implemented, which at first rapidly creates hypotheses based on edge filters. In the second stage those hypotheses are verified by a Support Vector Machine which rejects most of the False Posititves. At good conditions the detection reaches completeness rates of up to 90 percent with a low contingent of FP detections. Subsequently a tracking algorithm based on singular value decomposition is applied to associate vehicle hypotheses in adjacent images and determine the average speed. The achieved velocities differ less than ten kph from the manually obtained data. Concluding an orientation method is presented, that automatically determines the airplane''s attitude based on GPS and image information. This is realized by extraction and matching of street segments and additional tracking of ground control points. The results have accuracies of around 0.1 to 0.2 degrees.

This document deals with design, implementation and testing of software system for detecting p2p (peer-to-peer) networks based on combination of BPF prefiltering and POSIX regular expressions packet payload matching with known p2p protocol communications. The proposed detection system includes a database with some rules of most effuse p2p protocols in format resembling to definitions for L7-filter classifier. The application is implemented in C, runs in userspace and is targeted to all POSIX compatible platforms. The combination of detector with user attached QoS controlling is complete solution for traffic reduction of common p2p protocols.

Dagens trafikstyrning sker främst med hjälp av nedgrävda induktionsslingor som detekterar fordon som passerar över dem. Dessa detektorer går relativt ofta sönder och kräver utgrävningar av vägbanan vid reparation. Dessa reparationer tar tid, skapar hinder i trafikflödet och leder till ytterligare samhällskostnader. Detta arbete undersöker möjligheten att byta ut induktionsslingorna mot kameror som monteras direkt på trafikljusen. En kamerabaserad lösning skulle kunna underlätta reparationsarbetet och på så sätt vara ett mer ekonomiskt alternativ till induktionsslingor. För att detta ska fungera krävs det att den kamerabaserade lösningen kan prestera på en likvärdig nivå till lösningen med induktionsslingor. Detta arbete bygger upp en modell av en trafikkorsning för att besvara denna fråga. Trafikkorsningen byggs upp i Unreal Engine 5 och baserar sig på en verklig korsning i Linköping, Sverige. I modellen byggs korsningens induktionsslingor upp med liknande logik som den som finns i den verkliga korsningen. Det byggs även en kamerabaserad detektionslösning där kamerorna monteras på ett trovärdigt sätt. För att mäta prestandan av de olika lösningarna används måttet fördröjningstid, vilket är den extra tid som ett fordon spenderar i korsningen på grund av trafikljusen. Arbetet fann att den simulerade induktionsslingorna presterade bättre än den kamerabaserade detektionsmetoden. Skillnaden är tillräckligt liten för att inte helt utesluta en kamerabaserad lösning som ett alternativ till induktionsslingor. Det skulle däremot krävas vidare forskning för att täcka fler scenarion och få en bättre förståelse för den kamerabaserade lösningens fulla potential. / The traffic control of today is primarily controlled using buried induction loops that detect vehicles passing over them. These detectors break relatively often and require excavation of the roadway to repair. These reparations take time, generate obstructions in the flow of traffic and lead to further societal costs. This paper explores the possibility of using cameras directly mounted to the traffic lights of an intersection as a substitute for induction loops. The camera-based solution is required to perform on a similar level to the solution based on induction loop for this to be plausible. This paper constructs a model of an intersection and uses that model to evaluate if this is possible or not. The intersection is constructed in Unreal Engine 5 and is based on a real-world intersection from Linköping, Sweden. The model includes a representation of the induction loops present in the real-world intersection. It also includes an implementation of a camera-based vehicle detection solution where the cameras are mounted in a realistic way throughout the simulated intersection. The measurement Fördröjningstid is used to measure the performance of the different solutions. Delay time is the added time a vehicle that passes through the intersection experiences because of the traffic lights. The paper finds that the simulated induction loops outperform the camerabased solution. The difference is small enough to not completely rule out a camera-based solution as an alternative to induction loops. Further research would be required to cover more scenarios and to get a better understanding of the full potential of a camera-based solution.

V dnešnom svete zrýchľujúcej sa sieťovej prevádzky je potrebné držať krok v jej monitorovaní . Dostatočný prehľad o dianí v sieti dokáže zabrániť rozličným útokom na ciele nachádzajúce sa v nej . S tým nám pomáhajú systémy IDS, ktoré upozorňujú na udalosti nájdené v analyzovanej prevádzke . Pre túto prácu bol vybraný systém Suricata . Cieľom práce je vyladiť nastavenia systému Suricata s rozhraním AF_PACKET pre optimálnu výkonnosť a následne navrhnúť a implementovať optimalizáciu Suricaty . Výsledky z meraní AF_PACKET majú slúžiť ako základ pre porovnanie s navrhnutým vylepšením . Navrhovaná optimalizácia implementuje nové rozhranie založené na projekte Data Plane Development Kit ( DPDK ). DPDK je schopné akcelerovať príjem paketov a preto sa predpokladá , že zvýši výkon Suricaty . Zhodnotenie výsledkov a porovnanie rozhraní AF_PACKET a DPDK je možné nájsť na konci diplomovej práce .