RF Fingerprinting Unmanned Aerial Vehicles

As unmanned aerial vehicles (UAVs) continue to become more readily available, their use in civil, military, and commercial applications is growing significantly. From aerial surveillance to search-and-rescue to package delivery the use cases of UAVs are accelerating. This accelerating popularity gives rise to numerous attack possibilities for example impersonation attacks in drone-based delivery, in a UAV swarm, etc. In order to ensure drone security, in this project we propose an authentication system based on RF fingerprinting. Specifically, we extract and use the device-specific hardware impairments embedded in the transmitted RF signal to separate the identity of each UAV. To achieve this goal, AlexNet with the data augmentation technique was employed

Cybersecurity of Digital Service Chains

This open access book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios. We expect the book to be interesting for the broad group of researchers, engineers, and professionals daily experiencing the inadequacy of outdated cybersecurity models for modern computing environments and cyber-physical systems

Design and Evaluation of Compression, Classification and Localization Schemes for Various IoT Applications

Nowadays we are surrounded by a huge number of objects able to communicate, read information such as temperature, light or humidity, and infer new information through ex- changing data. These kinds of objects are not limited to high-tech devices, such as desktop PC, laptop, new generation mobile phone, i.e. smart phone, and others with high capabilities, but also include commonly used object, such as ID cards, driver license, clocks, etc. that can made smart by allowing them to communicate. Thus, the analog world of just a few years ago is becoming the a digital world of the Inter- net of Things (IoT), where the information from a single object can be retrieved from the Internet. The IoT paradigm opens several architectural challenges, including self-organization, self-managing, self-deployment of the smart objects, as well as the problem of how to minimize the usage of the limited resources of each device. The concept of IoT covers a lot of communication paradigms such as WiFi, Radio Frequency Identification (RFID), and Wireless Sensor Network (WSN). Each paradigm can be thought of as an IoT island where each device can communicate directly with other devices. The thesis is divided in sections in order to cover each problem mentioned above. The first step is to understand the possibility to infer new knowledge from the deployed device in a scenario. For this reason, the research is focused on the web semantic, web 3.0, to assign a semantic meaning to each thing inside the architecture. The sole semantic concept is unusable to infer new information from the data gathered; in fact, it is necessary to organize the data through a hierarchical form defined by an Ontology. Through the exploitation of the Ontology, it is possible to apply semantic engine reasoners to infer new knowledge about the network. The second step of the dissertation deals with the minimization of the usage of every node in a WSN. The main purpose of each node is to collect environmental data and to exchange hem with other nodes. To minimize battery consumption, it is necessary to limit the radio usage. Therefore, we implemented Razor, a new lightweight algorithm which is expected to improve data compression and classification by leveraging on the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. Data compression is performed studying the well-know Vector Quantization (VQ) theory in order to create the codebooks necessary for signal compression. At the same time, it is requested to give a semantic meaning to un- known signals. In this way, the codebook feature is able not only to compress the signals, but also to classify unknown signals. Razor is compared with both state-of-the-art compression and signal classification techniques for WSN . The third part of the thesis covers the concept of smart object applied to Robotic research. A critical issue is how a robot can localize and retrieve smart objects in a real scenario without any prior knowledge. In order to achieve the objectives, it is possible to exploit the smart object concept and localize them through RSSI measurements. After the localization phase, the robot can exploit its own camera to retrieve the objects. Several filtering algorithms are developed in order to mitigate the multi–path issue due to the wireless communication channel and to achieve a better distance estimation through the RSSI measurement. The last part of the dissertation deals with the design and the development of a Cognitive Network (CN) testbed using off the shelf devices. The device type is chosen considering the cost, usability, configurability, mobility and possibility to modify the Operating System (OS) source code. Thus, the best choice is to select some devices based on Linux kernel as Android OS. The feature to modify the Operating System is required to extract the TCP/IP protocol stack parameters for the CN paradigm. It is necessary to monitor the network status in real-time and to modify the critical parameters in order to improve some performance, such as bandwidth consumption, number of hops to exchange the data, and throughput