Tor network forensics and hidden service deanonymization

The cybernetics revolution of the last years improved a lot our lives, having an immediate access to services and a huge amount of information over the Internet. Nowadays the user is increasingly asked to insert his sensitive information on the Internet, leaving its traces everywhere. But there are some categories of people that cannot risk to reveal their identities on the Internet. Even if born to protect U.S. intelligence communications online, nowadays Tor is the most famous low-latency network, that guarantees both anonymity and privacy of its users. The aim of this thesis project is to well understand how the Tor protocol works, not only studying its theory, but also implementing those concepts in practice, having a particular attention for security topics. In order to run a Tor private network, that emulates the real one, a virtual testing environment has been configured. This behavior allows to conduct experiments without putting at risk anonymity and privacy of real users. We used a Tor patch, that stores TLS and circuit keys, to be given as inputs to a Tor dissector for Wireshark, in order to obtain decrypted and decoded traffic. Observing clear traffic allowed us to well check the protocol outline and to have a proof of the format of each cell. Besides, these tools allowed to identify a traffic pattern, used to conduct a traffic correlation attack to passively deanonymize hidden service clients. The attacker, controlling two nodes of the Tor network, is able to link a request for a given hidden server to the client who did it, deanonymizing him. The robustness of the traffic pattern and the statistics, such as the true positive rate, and the false positive rate, of the attack are object of a potential future work.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.

Graph-based User Scheduling for MIMO LEO-based Satellite Communication Systems

The study of the user scheduling problem in a Low Earth Orbit (LEO) Multi-User MIMO system is the objective of this thesis. With the application of cutting-edge digital beamforming algorithms, a LEO satellite with an antenna array and a large number of antenna elements can provide service to many user terminals (UTs) in full frequency reuse (FFR) schemes. Since the number of UTs on-ground are many more than the transmit antennas on the satellite, user scheduling is necessary. Scheduling can be accomplished by grouping users into different clusters: users within the same cluster are multiplexed and served together via Space Division Multiple Access (SDMA), i.e., digital beamforming or Multi-User MIMO techniques; the different clusters of users are then served on different time slots via Time Division Multiple Access (TDMA). The design of an optimal user grouping strategy is known to be an NP-complete problem which can be solved only through exhaustive search. In this thesis, we provide a graph-based user scheduling and feed space beamforming architecture for the downlink with the aim of reducing user inter-beam interference. The main idea is based on clustering users whose pairwise great-circle distance is as large as possible. First, we create a graph where the users represent the vertices, whereas an edge in the graph between 2 users exists if their great-circle distance is above a certain threshold. In the second step, we develop a low complex greedy user clustering technique and we iteratively search for the maximum clique in the graph, i.e., the largest fully connected subgraph in the graph. Finally, by using the 3 aforementioned power normalization techniques, a Minimum Mean Square Error (MMSE) beamforming matrix is deployed on a cluster basis. The suggested scheduling system is compared with a position-based scheduler, which generates a beam lattice on the ground and randomly selects one user per beam to form a cluster.

99% - Dall’1% dell'idea al 99% della fase Pre-Seed: ciò che serve sapere (e forse non tutti dicono) su questo percorso, visto dalla prospettiva di un Service Designer

Oggigiorno, quando una persona “ha un’idea” e vuole concretizzarla si rivolge agli incubatori: la diffusa metodologia Lean Startup è determinante nel far comprendere agli aspiranti founders la portata della propria “idea” abbattendo i rischi. Tuttavia, ci sono due problemi: accedere ai percorsi di incubazione non è scontato e, una volta concluso il percorso, si hanno diverse conoscenze per passare dalla validazione alla fase Seed, ma non sufficienti per un’efficace Execution. Partendo dalla personale esperienza del progetto Climby, si cercherà di dare un’overview sugli aspetti più importanti da considerare, evidenziando ciò che si potrebbe sottovalutare. Il fine è dunque supportare chi “ha un’idea” nell’intraprendere il percorso di incubazione in modo più consapevole e illustrare i limiti dei percorsi stessi, suggerendo approfondimenti di immediata applicazione ed utilità nelle diverse fasi citate, come integrazioni tratte dal Service Design. Pertanto, sarà posta particolare attenzione a ciò che è stato appreso in prima persona, considerando errori e aspetti positivi del percorso di un’“idea” nata nel 2019 e trasformatasi drasticamente durante l’incubazione, fino a ciò che accade dopo il Pitch Day. In tal modo, si intende anche esplicitare alcuni aspetti importanti, talvolta trascurati, ma rilevanti per le scelte da compiere prima ancora di iniziare un percorso. Il fine, quindi, è quello di produrre un contenuto complementare agli autorevoli scritti che trattano di Lean Startup, invece di proporne trattazione alternativa. Infine, conducendo queste riflessioni, si proporrà anche un confronto più specifico tra le metodologie e gli strumenti di Lean Startup e Service Design e - anticipando le conclusioni - suggerendo che sarebbe interessante approfondire l’integrazione di questi due ambiti, di cui emerge ancora una forte mancanza di reciproca conoscenza e che, invece, potrebbero rappresentare reciprocamente una risorsa se considerati senza visioni di parte.