Tor is a Privacy-Enhancing Technology based on onion routing which lets its users browse the web anonymously. Even though the traffic is encrypted in multiple layers, traffic analysis can still be used to gather information from meta-data such as time, size, and direction of the traffic. A Website Fingerprinting (WF) attack is characterized by monitoring traffic locally to the user in order to predict the destination website based on the observed patterns. TrafficSliver is a proposed defence against WF attacks which splits the traffic on multiple paths in the Tor network. This way, a local attacker is assumed to only be able to observe a subset of all the user's total traffic. The initial evaluation of TrafficSliver against Deep Fingerprinting (DF), the state-of-the-art WF attack, showed promising results for the defence, reducing the accuracy of DF from over 98% down to less than 7% without adding artificial delays or dummy traffic. In this thesis, we further evaluate TrafficSliver against DF beyond what was done in the original work by De la Cadena et al. by using a richer data representation and finding out whether it is possible to utilize simulated training data to improve the accuracy of the attack. By introducing directional time as a richer data representation and increasing the size of the training dataset using a simulator, the accuracy of DF was improved against TrafficSliver on three different datasets. Against the original dataset provided by the authors of TrafficSliver, the accuracy was initially 7.1% and then improved to 49.9%. The results were confirmed by using two additional datasets with TrafficSliver, where the accuracy was improved from 5.4% to 44.9% and from 9.8% to 37.7%. / Tor är ett personlig-integritetsverktyg baserat på onion routing som låter sina användare anonymnt besöka hemsidor på internet. Även om trafiken är enkrypterad i flera lager, kan trafikanalys användas för att utvinna information från metadata som exempelvis: tid, storlek och riktning av trafik. En Website Fingerprinting (WF)-attack karaktäriseras av att övervaka trafik nära användaren för att sedan avgöra vilken hemsida som besökts utifrån mönster. TrafficSliver är ett föreslaget försvar mot WF-attacker genom att dela upp trafiken på flera vägar genom nätverket. Detta gör att en attackerare antas endast kunna se en delmängd av användarens totala trafik. Den första utvärderingen av TrafficSliver mot Deep Fingerprinting (DF), spjutspetsen inom WF-attacker, visade lovande resultat för försvaret genom att reducera träffsäkerheten av DF från över 98% till mindre än 7% utan att lägga till artificiella fördröjningar eller falsk trafik. I denna uppsats strävar vi att fortsätta utvärderingen av TrafficSliver mot DF utöver vad som redan har gjorts av De la Cadena et al. med en rikare datarepresentation och en undersökning huruvida det går att använda simulerad data för att träna attacker mot försvaret. Genom att introducera riktad tid och öka mängden data för att träna attacken, ökades träffsäkerheten av DF mot TrafficSliver på tre distinkta dataset. Mot det dataset som samlades in av TrafficSliver var träffsäkerheten inledelsevis 7.1% och sedan förbättrad med hjälp av riktad tid och större mängder av simulerad träningsdata till 49.9%. Dessa resultat bekräftades även för två ytterligare dataset med TrafficSliver, där träffsäkerheten blev förbättrad från 5.4% till 44.9% och från 9.8% till 37.7%.

<p>Recently, wireless sensor network (WSN) has become a promising technologywith a wide range of applications such as supply chain monitoringand environment surveillance. It is typically composed of multiple tiny devicesequipped with limited sensing, computing and wireless communicationcapabilities. Design of such networks presents several technique challengeswhile dealing with various requirements and diverse constraints. Performanceanalysis techniques are required to provide insight on design parametersand system behaviors.</p><p>Based on network calculus, we present a deterministic analysis methodfor evaluating the worst-case delay and buffer cost of sensor networks. Tothis end, three general traffic flow operators are proposed and their delayand buffer bounds are derived. These operators can be used in combinationto model any complex traffic flowing scenarios. Furthermore, the methodintegrates a variable duty cycle to allow the sensor nodes to operate at lowrates thus saving power. In an attempt to balance traffic load and improveresource utilization and performance, traffic splitting mechanisms areintroduced for mesh sensor networks. Based on network calculus, the delayand buffer bounds are derived in non-splitting and splitting scenarios.In addition, analysis of traffic splitting mechanisms are extended to sensornetworks with general topologies. To provide reliable data delivery in sensornetworks, retransmission has been adopted as one of the most popularschemes. We propose an analytical method to evaluate the maximum datatransmission delay and energy consumption of two types of retransmissionschemes: hop-by-hop retransmission and end-to-end retransmission.</p><p>We perform a case study of using sensor networks for a fresh food trackingsystem. Several experiments are carried out in the Omnet++ simulationenvironment. In order to validate the tightness of the two bounds obtainedby the analysis method, the simulation results and analytical results arecompared in the chain and mesh scenarios with various input traffic loads.From the results, we show that the analytic bounds are correct and tight.Therefore, network calculus is useful and accurate for performance analysisof wireless sensor network.</p> / Ipack VINN Excellence Center

Recently, wireless sensor network (WSN) has become a promising technologywith a wide range of applications such as supply chain monitoringand environment surveillance. It is typically composed of multiple tiny devicesequipped with limited sensing, computing and wireless communicationcapabilities. Design of such networks presents several technique challengeswhile dealing with various requirements and diverse constraints. Performanceanalysis techniques are required to provide insight on design parametersand system behaviors. Based on network calculus, we present a deterministic analysis methodfor evaluating the worst-case delay and buffer cost of sensor networks. Tothis end, three general traffic flow operators are proposed and their delayand buffer bounds are derived. These operators can be used in combinationto model any complex traffic flowing scenarios. Furthermore, the methodintegrates a variable duty cycle to allow the sensor nodes to operate at lowrates thus saving power. In an attempt to balance traffic load and improveresource utilization and performance, traffic splitting mechanisms areintroduced for mesh sensor networks. Based on network calculus, the delayand buffer bounds are derived in non-splitting and splitting scenarios.In addition, analysis of traffic splitting mechanisms are extended to sensornetworks with general topologies. To provide reliable data delivery in sensornetworks, retransmission has been adopted as one of the most popularschemes. We propose an analytical method to evaluate the maximum datatransmission delay and energy consumption of two types of retransmissionschemes: hop-by-hop retransmission and end-to-end retransmission. We perform a case study of using sensor networks for a fresh food trackingsystem. Several experiments are carried out in the Omnet++ simulationenvironment. In order to validate the tightness of the two bounds obtainedby the analysis method, the simulation results and analytical results arecompared in the chain and mesh scenarios with various input traffic loads.From the results, we show that the analytic bounds are correct and tight.Therefore, network calculus is useful and accurate for performance analysisof wireless sensor network. / Ipack VINN Excellence Center