The Internet of Things is expanding, and with the increasing numbers of connected devices,exploitation of those devices also becomes more common. Since IoT devices and IoT networksare used in many crucial areas in modern societies, ranging from everything between securityand militrary applications to healthcare monitoring and production efficiency, the need to securethese devices is of great importance for researchers and businesses. This project explores howan intrusion detection system called DETONAR can be used on border router logs, instead of itsoriginal use of sniffer devices. Using DETONAR in this way allows us to detect many differentattacks, without contributing to the additional cost of deploying sniffer devices and the additionalrisk of the sniffer devices themselves becoming the target of attack

The objective of this thesis is to evaluate if it is possible to run RSA authentication in a specified scenario. A Raspberry Pi with a limited CPU is used to simulate a low-performance device. A series of tests on this device shows that it is not possible to run RSA authentication in the provided scenario.A survey conducted on IT-professionals shows that there is a strong belief that this is possible. The results shows that there is a disparity between the tested RSA performance and the perception in the industry. However since ambiguity exists in the scenario it is hard to draw conclusions about the results. / Syftet med den här uppsatsen är att utvärdera om det är möjligt att använda RSA autentisering i ett specificerat scenario. En Raspberry Pi med begränsad CPU används för att simulera en enhet med låg prestanda. Ett antal tester med den här enheten visar att det inte är möjligt att använda RSA autentisering i det givna scenariot. En enkät utgiven till anställda inom IT-branschen visar att det finns en stark uppfattning om att detta är möjligt. Resultatet visar att det finns en avvikelse mellan den testade RSA prestandan och uppfattningen inom branschen. Det existerar dock otydligheter i scenariot vilket gör det svårt att dra slutsatser om resultatet.

I takt med att samhället i stort digitaliseras i hisnande fart kommer även fler och fler lösningar på hur teknik kan hjälpa elever och lärare i den svenska skolan. Internet of Things är på uppgång och nya tekniker för ansiktsigenkänning och databehandling öppnar upp för olika automatiseringsmöjligheter. Syftet med denna rapport är att undersöka användarnas upplevelse av ansiktsigenkänning för närvarohantering i skolan. Lärarnas administrativa börda växer och mycket lektionstid går åt till just närvarotagning, därför hoppas man kunna effektivisera lektionerna och skolan i stort med hjälp av ny teknik. Denna kvalitativa undersökning som görs i samarbete med RISE Research Institutes of Sweden undersöker upplevelsen av ansiktsigenkänningsteknik för närvarotagning och hantering i skolan ur elevernas perspektiv. En klass i en gymnasieskola i Sverige provade under en vecka en prototyp för närvarotagning och fick sedan utvärdera denna i fokusgruppsintervjuer. Resultaten pekar på en positiv inställning till tekniken generellt och att den potentiellt kan ha positiv inverkan på studiemiljön och undervisning. Det visar sig att design och interaktion spelar en viktig roll kring synen på tekniken, och denna studie ämnar ge en helhetsbild av hur elever på ett tekniskt gymnasium upplever tekniken. Undersökningen visar på designutmaningar och öppnar upp för vidare studier i exakt hur ansiktsigenkänningsteknik kan och bör användas i skolmiljö ur ett användarperspektiv. Här ges en grundläggande bild av hur elever upplever ansiktsigenkänningsteknik i skolan och undersökningen bidrar till att stärka arbetet mot en digitaliserad skola. / As society as a whole is digitized in breathtaking speed, more and more solutions to how technology can help students and teachers in the Swedish school system emerge. The Internet of Things is on the rise and new techniques for facial recognition and data processing open up for various automation possibilities. The purpose of this report is to examine the users' experience of facial recognition for attendance management in school. The teachers' administrative burden is growing and a lot of time is devoted to attendance, so one hopes to make the classes, and the school as a whole, more efficient with the help of new technology. This qualitative study, conducted in collaboration with RISE Research Institutes of Sweden, examines the experience of facial recognition techniques for attendance and management in school from the pupils' perspective. One class in a high school in Sweden tested a prototype for attendance during one week and then evaluated it in focus group interviews. The results point to a positive attitude towards the technology in general and that it can potentially have a positive impact on the study environment and teaching. It turns out that design and interaction play an important role in the view of the technology, and this study aims to give an overall picture of how students in a Swedish high school experience the technology. The study shows design challenges and opens up for further studies in exactly how facial recognition technology can and should be used in a school environment from a user perspective. Here, a basic picture is given of how students experience facial recognition technology in school and the study helps to strengthen the work towards a digitized school.

This report investigates reliability within the Internet of Things with a focus on the messaging protocol MQTT. Within MQTT we explore the options for ensuring reliability, mainly Quality of Service. We attempt to compare a few different implementations of the MQTT protocol over different Quality of Service levels. This comparison occurs through experiments that measure the communication size in bytes, and the time to perform, a simple publish-type communication. We find that there are some differences that seem likely to be impactful enough that some implementations are preferable, depending on the application and resources available. This report also covers some basic theory about IoT systems and their parts.

This thesis focuses on the design and implementation of a new, inexpensive, and less complex system for a Do-It-Yourself (DIY) Internet of Things (IoT) platform. The hardware aspects focus on a new chip called the ESP8266 which contains both microcontroller and WiFi connectivity capabilities in an extremely affordable package. The system uses the Arduino IDE to program the ESP8266, which is known to be an extremely user-friendly environment. All other software is both free and easy to use. Past methods of creating IoT projects involved either expensive hardware, often ranging from $50-$100 per node, or complicated programming requiring a full computer, or a constant connection to an immobile power source. This method costs as little as $2.50, can last for months or even years off of batteries, can be smaller than a quarter, and only requires a few lines of code to get data moving, making this platform much more attractive for ubiquitous use.

Internet of Things (IoT) devices have been widely adopted in many different applications in recent years, such as smart home applications. An adversary can capture the network traffic of IoT devices and analyze it to reveal user activities even if the traffic is encrypted. Therefore, traffic privacy is a major concern, especially in smart home applications. Traffic shaping can be used to obfuscate the traffic so that no meaningful predictions can be drawn through traffic analysis. Current traffic shaping methods have many tunable variables that are difficult to optimize to balance bandwidth overheads and latencies. In this thesis, we study current traffic shaping algorithms in terms of computational requirements, bandwidth overhead, latency, and privacy protection based on captured traffic data from a mimic smart home network. A new traffic shaping method - Dynamic Traffic Padding is proposed to balance bandwidth overheads and delays according to the type of devices and desired privacy. We use previous device traffic to adjust the padding rate to reduce the bandwidth overhead. Based on the mimic smart home application data, we verify our proposed method can preserve privacy while minimizing bandwidth overheads and latencies.

The increase in popularity for wearable technologies (see: Apple Watch and Microsoft Band) has opened the door for an Internet of Things solution to healthcare. One of the most prevalent healthcare problems today is the poor survival rate of out-of hospital sudden cardiac arrests (9.5% on 360,000 cases in the USA in 2013). It has been proven that heart rate derived features can give an early indicator of sudden cardiac arrest, and that providing an early warning has the potential to save many lives. Many of these new wearable devices are capable of providing this warning through their heart rate sensors. This thesis paper introduces a prospective dataset of physical activity heart rates collected via Microsoft Band. This dataset is indicative of the heart rates that would be observed in the proposed Internet of Things solution. This dataset is combined with public heart rate datasets to provide a dataset larger than many of the ones used in related works and more indicative of out-of-hospital heart rates. This paper introduces the use of LogitBoost as a classifier for sudden cardiac arrest prediction. Using this technique, a five minute warning of sudden cardiac arrest is provided with 96.36% accuracy and F-score of 0.9375. These results are better than existing solutions that only include in-hospital data.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24232号 / 工博第5060号 / 新制||工||1790(附属図書館) / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 篠原 真毅, 教授 小嶋 浩嗣, 教授 山本 衛 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The Internet of Things (IoT) is an important and expanding technology used for a large variety of applications to monitor and automate processes. The aim of this thesis is to present a way to find and assign locally unique IDs to access points (APs) in enormous wireless IoT systems where mobile tags are traversing the network and communicating with multiple APs simultaneously. This is done in order to improve the robustness of the system and increase the battery time of the tags. The resulting algorithm is based on transforming the problem into a graph coloring problem and solving it using approximate methods. Two metaheuristics: Simulated annealing and tabu search were implemented and compared for this purpose. Both of these showed similar results and neither was clearly superior to the other. Furthermore, the presented algorithm can also exclude nodes from the coloring based on the results in order to ensure a proper solution that also satisfies a robustness criterion. A metric was also created in order for a user to intuitively evaluate the quality of a given solution. The algorithm was tested and evaluated on a system of 222 APs for which it produced good results.

Internet of Things (IoT) has weaknesses due to the vulnerabilities in the wireless medium and massively interconnected nodes that form an extensive attack surface for adversaries. It is essential to ensure security including IoT networks and applications. The thesis focus on three streams in IoT scenario, including fake task attack detection in Mobile Crowdsensing (MCS), blockchain technique-integrated system security and privacy protection in MCS, and network intrusion detection in IoT. In this thesis, to begin, in order to detect fake tasks in MCS with promising performance, a detailed analysis is provided by modeling a deep belief network (DBN) when the available sensory data is scarce for analysis. With oversampling to cope with the class imbalance challenge, a Principal Component Analysis (PCA) module is implemented prior to the DBN and weights of various features of sensing tasks are analyzed under varying inputs. Additionally, an ensemble learning-based solution is proposed for MCS platforms to mitigate illegitimate tasks. Meanwhile, a k-means-based classification is integrated with the proposed ensemble method to extract region-specific features as input to the machine learning-based fake task detection. A novel approach that is based on horizontal Federated Learning (FL) is proposed to identify fake tasks that contain a number of independent detection devices and an aggregation entity. Moreover, the submitted tasks are collected and managed conventionally by a centralized MCS platform. A centralized MCS platform is not safe enough to protect and prevent tampering sensing tasks since it confronts the single point of failure which reduces the effectiveness and robustness of MCS system. In order to address the centralized issue and identify fake tasks, a blockchain-based decentralized MCS is designed. Integration of blockchain into MCS enables a decentralized framework. The distributed nature of a blockchain chain prevents sensing tasks from being tampered. The blockchain network uses a Practical Byzantine Fault Tolerance (PBFT) consensus that can tolerate 1/3 faulty nodes, making the implemented MCS system robust and sturdy. Lastly, Machine Learning (ML)-based frameworks are widely investigated to identity attacks in IoT networks, namely Network Intrusion Detection System (NIDS). ML models perform divergent detection performance in each class, so it is challenging to select one ML model applicable to all classes prediction. With this in mind, an innovative ensemble learning framework is proposed, two ensemble learning approaches, including All Predict Wisest Decides (APWD) and Predictor Of the Lowest Cost (POLC), are proposed based on the training of numerous ML models. According to the individual model outcomes, a wise model performing the best detection performance (e.g., F1 score) or contributing the lowest cost is determined. Moreover, an innovated ML-based framework is introduced, combining NIDS and host-based intrusion detection system (HIDS). The presented framework eliminates NIDS restrictions via observing the entire traffic information in host resources (e.g., logs, files, folders).