A side-channel attack (SCA) recovers secret data from a device by exploiting unintended physical leakages such as power consumption. In a profiled SCA, we assume an adversary has control over a target and copy device. Using the copy device the adversary learns a profile of the device. With the profile, the adversary exploits the measurements from a target device and recovers the secret key. As SCAs have shown to be a realistic attack vector, countermeasures have been invented to harden these kinds of attacks. In the last few years, deep learning has been applied in a wide variety of domains. For example, convolutional neural networks have shown to be effective for object recognition in images and recurrent neural networks for text generation. In the side-channel analysis domain, deep learning has shown to be successful. Up until recently, no deep learning layer existed that was specifically designed for SCAs. In this work, we analyze this layer, called the spread layer, and demonstrate the flaws of this layer. We improve the flaws and show the spread layer does not enhance the performance of SCAs. Additionally, we show there is no need to develop a deep learning layer specifically for SCAs on unprotected implementations. For implementations where countermeasures are present, literature demonstrated that convolutional neural networks are the most successful. However, for both the masking and random delay countermeasure, little is known about the influence of the kernel size and depth of the network. In this work, we illustrate that increasing the kernel size and depth of the network both increase the attack efficiency for the random delay countermeasure. For the masking countermeasure, we demonstrate that higher kernel sizes and shallow networks perform the best. Additionally, in this work, we consider a portability setting where the probe position has been changed in between the measurements of the profiling and attack measurements. Here, we show that the probe position causes a typical deep learning SCA to be ineffective. We introduce a normalization method such that the attack becomes effective, and show this method enables the attack to perform as expected.

Since its inception in 1995, JavaScript usage has grown far beyond its initial domain of interactive websites. As the size of applications developed in the language grows, so does the desire for static analysis such as typechecking to provide safety and reliability. Many developments have been made in recent years on increasing the precision of analysis of JavaScript. This work introduces a concrete and a type interpreter for LambdaJS implemented within the Sturdy framework. The goal of this work is to evaluate the feasibility of implementing a shared interpreter using the Sturdy framework, evaluate the correctness of the concrete interpreter, and evaluate the feasibility of proving the typechecker sound. The resulting interpreters are tested experimentally with two test suites. The experimental evaluation gives confidence in the correctness of the concrete and abstract interpreter. A small part of the abstract interpreter is proven sound to evaluate the difficultyofcreatingacompletesoundnessproof.Thesuccessful implementation of the interpreters shows that implementing a shared arrow-based interpreter within the Sturdy framework is feasible for languages with complex semantics, that the utilities available reduce the effort required to do so, and that proving the abstract interpreter sound is simplified by using the library.

Software-Defined Networking (SDN) is the emerging paradigm that breaks vertical integration in networks, separating the network’s control logic from the underlying network devices such as routers and switches.
The decoupling of this data plane and control plane, there is need for a new communication channel which is used for the communication between the SDN controller and the network devices.
This channel is the so-called control channel and a popular protocol used over this channel is OpenFlow.
In this work we focus on the security of SDN while focusing on the control channel and the OpenFlow protocol. In example, we show several impersonation attacks and achieve denial-of-service by misusing the ARP protocol to generate a lot of OpenFlow traffic.
We also discuss how we can protect SDN against such attacks in order to improve SDN security.
This work has been performed at the IT security company Fox IT.

Internet of Things (IoT) applications nowadays have a wide impact on people’s daily life while the size of IoT has been increasing rapidly. Millions of devices huge amount of data and different kinds of new protocols can bring many security issues.
LoRaWAN is a MAC layer protocol for long-range low-power communication dedicated to the IoT. It can be used to transmit messages between IoT end devices and gateways. However, since the development of LoRaWAN is still at an early stage, the security level of the protocol is not well developed, and the need for analyzing and developing the security level of LoRaWAN is necessary and urgent.
This research summarizes the secure features of LoRaWAN in the aspects of activation methods, key management, cryptography, counter management and message acknowledgement.
Then, vulnerabilities of LoRaWAN are found and analyzed. 4 Attacks
based on these vulnerabilities are designed and described via an attack tree method. These attacks are (1) replay attack, (2) eavesdropping, (3) bit flipping and (4) ACK spoofing. As a poof-of-concept, the attacks are implemented and executed in a LoRaWAN environment. Afterwards, mitigation and secure solutions against attacks are given to protect the security of LoRaWAN networks.
The result of this research can be used in developing the security level of LoRaWAN protocol and setting the standard criteria for evaluating security of LoRaWAN devices.

Nowadays, 19% of the global energy consumption is used for lighting. For this reason, saving energy in lighting is vital. A simple way to save energy is to “simply” turn the lights off, or reduce the amount of light used when nobody is around. This thesis proposes a new method for luminaires to detect the presence of humans and objects which only uses a photodiode and a fraction of the light a luminaire normally emits, namely Dark Sensing.

Dark sensing works by sending out short flashes of light. These short flashes use little energy and are barely visible to the user. These flashes get reflected by the environment and received by a photodiode placed next to the light. By extracting a key feature of the received flash, we obtain a metric representing the surrounding area. If an object enters the observed area, the reflections of light will change. These changes will be noticed by the system, which triggers a detection resulting in the light being turned on.

A prototype was created which shows the potential of the newly developed method. The prototype was tested in two different environments and achieved detects between 73% and 90% of bypassing pedestrians, depending on the accepted false positive ratio (0 to 0.05).