Relevant bibliographies by topics / Machine Learning in Security / Dissertations / Theses
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Dissertations / Theses on the topic 'Machine Learning in Security'

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Published: 7 June 2025
Last updated: 2 August 2025

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1

Stomeo, Carlo. "Applying Machine Learning to Cyber Security." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17303/.

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Intrusion Detection Systems (IDS) nowadays are a very important part of a system. In the last years many methods have been proposed to implement this kind of security measure against cyber attacks, including Machine Learning and Data Mining based. In this work we discuss in details the family of anomaly based IDSs, which are able to detect never seen attacks, paying particular attention to adherence to the FAIR principles. This principles include the Accessibility and the Reusability of software. Moreover, as the purpose of this work is the assessment of what is going on in the state of the ar
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2

Jan, Steve T. K. "Robustifying Machine Learning based Security Applications." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99862.

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In recent years, machine learning (ML) has been explored and employed in many fields. However, there are growing concerns about the robustness of machine learning models. These concerns are further amplified in security-critical applications — attackers can manipulate the inputs (i.e., adversarial examples) to cause machine learning models to make a mistake, and it's very challenging to obtain a large amount of attackers' data. These make applying machine learning in security-critical applications difficult. In this dissertation, we present several approaches to robustifying three machine lea
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3

DEMETRIO, LUCA. "Formalizing evasion attacks against machine learning security detectors." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1035018.

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Recent work has shown that adversarial examples can bypass machine learning-based threat detectors relying on static analysis by applying minimal perturbations. To preserve malicious functionality, previous attacks either apply trivial manipulations (e.g. padding), potentially limiting their effectiveness, or require running computationally-demanding validation steps to discard adversarial variants that do not correctly execute in sandbox environments. While machine learning systems for detecting SQL injections have been proposed in the literature, no attacks have been tested against the pro
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4

Shrivastwa, Ritu Ranjan. "Enhancements in Embedded Systems Security using Machine Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT051.

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La liste des appareils connectés (ou IoT) s’allonge avec le temps, de même que leur vulnérabilité face aux attaques ciblées provenant du réseau ou de l’accès physique, communément appelées attaques Cyber Physique (CPS). Alors que les capteurs visant à détecter les attaques, et les techniques d’obscurcissement existent pour contrecarrer et améliorer la sécurité, il est possible de contourner ces contre-mesures avec des équipements et des méthodologies d’attaque sophistiqués, comme le montre la littérature récente. De plus, la conception des systèmes intégrés est soumise aux contraintes de compl
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5

Tian, Ke. "Learning-based Cyber Security Analysis and Binary Customization for Security." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85013.

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This thesis presents machine-learning based malware detection and post-detection rewriting techniques for mobile and web security problems. In mobile malware detection, we focus on detecting repackaged mobile malware. We design and demonstrate an Android repackaged malware detection technique based on code heterogeneity analysis. In post-detection rewriting, we aim at enhancing app security with bytecode rewriting. We describe how flow- and sink-based risk prioritization improves the rewriting scalability. We build an interface prototype with natural language processing, in order to customize
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Pozdniakov, K. "A machine learning approach for smart computer security audit." Thesis, City, University of London, 2017. http://openaccess.city.ac.uk/19971/.

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This thesis presents a novel application of machine learning technology to automate network security audit and penetration testing processes in particular. A model-free reinforcement learning approach is presented. It is characterized by the absence of the environmental model. The model is derived autonomously by the audit system while acting in the tested computer network. The penetration testing process is specified as a Markov decision process (MDP) without definition of reward and transition functions for every state/action pair. The presented approach includes application of traditional a
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7

Grosse, Kathrin [Verfasser]. "Why is Machine Learning Security so hard? / Kathrin Grosse." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1237268818/34.

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8

Sorio, Enrico. "Machine Learning Techniques for Document Processing and Web Security." Doctoral thesis, Università degli studi di Trieste, 2013. http://hdl.handle.net/10077/8533.

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2011/2012<br>The task of extracting structured information from documents that are unstructured or whose structure is unknown is of uttermost importance in many application domains, e.g., office automation, knowledge management, machine-to-machine interactions. In practice, this information extraction task can be automated only to a very limited extent or subject to strong assumptions and constraints on the execution environment. In this thesis work I will present several novel application of machine learning techniques aimed at extending the scope and opportunities for automation of inform
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9

Davis, Jonathan J. "Machine learning and feature engineering for computer network security." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106914/1/Jonathan_Davis_Thesis.pdf.

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This thesis studies the application of machine learning to the field of Cyber security. Machine learning algorithms promise to enhance Cyber security by identifying malicious activity based only on provided examples. However, a major difficulty is the unsuitability of raw Cyber security data as input. In an attempt to address this problem, this thesis presents a framework for automatically constructing relevant features suitable for machine learning directly from network traffic. We then test the effectiveness of the framework by applying it to three Cyber security problems: HTTP tunnel detect
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10

Lounici, Sofiane. "Watermarking machine learning models." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS282.pdf.

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La protection de la propriété intellectuelle des modèles d’apprentissage automatique apparaît de plus en plus nécessaire, au vu des investissements et de leur impact sur la société. Dans cette thèse, nous proposons d’étudier le tatouage de modèles d’apprentissage automatique. Nous fournissons un état de l’art sur les techniques de tatouage actuelles, puis nous le complétons en considérant le tatouage de modèles au-delà des tâches de classification d’images. Nous définissons ensuite les attaques de contrefaçon contre le tatouage pour les plateformes d’hébergement de modèles, et nous présentons
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11

Hu, Ji. "A virtual machine architecture for IT-security laboratories." Phd thesis, [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=980935652.

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12

Liu, Ruidong. "Power system stability scanning and security assessment using machine learning." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/19584.

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Future grids planning requires a major departure from conventional power system planning, where only a handful of the most critical scenarios is analyzed. To account for a wide range of possible future evolutions, scenario analysis has been proposed in many industries. As opposed to the conventional power system planning, where the aim is to find an optimal transmission and/or generation expansion plan for an existing grid, the aim in future grids scenario analysis is to analyze possible evolution pathways to inform power system planning and policy making. Therefore, future grids’ planning may
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13

Šrndić, Nedim [Verfasser]. "Machine Learning and Security of Non-Executable Files / Nedim Šrndić." München : Verlag Dr. Hut, 2017. http://d-nb.info/1149580364/34.

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14

MARCELLI, ANDREA. "Machine Learning and other Computational-Intelligence Techniques for Security Applications." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2751497.

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15

Mayo, Quentin R. "Detection of Generalizable Clone Security Coding Bugs Using Graphs and Learning Algorithms." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404548/.

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This research methodology isolates coding properties and identifies the probability of security vulnerabilities using machine learning and historical data. Several approaches characterize the effectiveness of detecting security-related bugs that manifest as vulnerabilities, but none utilize vulnerability patch information. The main contribution of this research is a framework to analyze LLVM Intermediate Representation Code and merging core source code representations using source code properties. This research is beneficial because it allows source programs to be transformed into a graphical
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16

Pundir, Nitin K. Pundir. "Design of a Hardware Security PUF Immune to Machine Learning Attacks." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513009797455883.

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17

Mutai, K. (Kenneth). "Internet of Things security with machine learning techniques:a systematic literature review." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906212619.

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Abstract. The Internet of Things (IoT) technologies are beneficial for both private and businesses. The growth of the technology and its rapid introduction to target fast-growing markets faces security challenges. Machine learning techniques have been recently used in research studies as a solution in securing IoT devices. These machine learning techniques have been implemented successfully in other fields. The objective of this thesis is to identify and analyze existing scientific literature published recently regarding the use of machine learning techniques in securing IoT devices. In thi
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Duan, Ren. "Machine Learning in Defensive IT Security: Early Detection of Novel Threats." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266122.

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The rapid development of technology leads to a rise in cybercrime, hence cybersecurityis of unprecedented significance, especially for businesses. Defensiveand forensic IT security is a rather niche field in IT security but it issurely going to grow. It focuses on preventing attacks by good design standardsand the education of persons. The typical reaction time of a computerattack currently lies in the order of hours, due to the reason that this field stillrelies on intensive manual work of skilled experts. In this thesis, we combineddefensive IT security with the most flourishing field in the
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19

GUIDOTTI, DARIO. "Verification and Repair of Machine Learning Models." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1082694.

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In these last few years, machine learning (ML) has gained incredible traction in the Artificial Intelligence community, and ML models have found successful applications in many different domains across computer science. However, it is hard to provide any formal guarantee on the behavior of ML models, and therefore their reliability is still in doubt, especially concerning their deployment in safety and security-critical applications. Verification and repair emerged as promising solutions to address some of these problems. In this dissertation, we present our contributions to these two lines
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Singh, Maneesha. "A machine learning approach for image enhancement and segmentation for aviation security." Thesis, University of Exeter, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410826.

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21

Kumar, Sharad Kumar. "Analysis of Machine Learning Modeling Attacks on Ring Oscillator based Hardware Security." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1541759752027838.

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22

Cheng, Aidan. "Using Machine Learning to Detect Malicious URLs." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1567.

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There is a need for better predictive model that reduces the number of malicious URLs being sent through emails. This system should learn from existing metadata about URLs. The ideal solution for this problem would be able to learn from its predictions. For example, if it predicts a URL to be malicious, and that URL is deemed safe by the sandboxing environment, the predictor should refine its model to account for this data. The problem, then, is to construct a model with these characteristics that can make these predictions for the vast number of URLs being processed. Given that the current sy
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23

Moniruzzaman, Md. "Imbalanced data classification and its application in cyber security." Thesis, Federation University Australia, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176349.

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Cyber security, also known as information technology security or simply as information security, aims to protect government organizations, companies and individuals by defending their computers, servers, electronic systems, networks, and data from malicious attacks. With the advancement of client-side on the fly web content generation techniques, it becomes easier for attackers to modify the content of a website dynamically and gain access to valuable information. The impact of cybercrime to the global economy is now more than ever, and it is growing day by day. Among various types of cybercri
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Šrndić, Nedim [Verfasser], and Andreas [Akademischer Betreuer] Zell. "Machine Learning and Security of Non-Executable Files / Nedim Šrndić ; Betreuer: Andreas Zell." Tübingen : Universitätsbibliothek Tübingen, 2018. http://d-nb.info/1168148596/34.

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Shaikh, Farooq Israr Ahmed. "Security Framework for the Internet of Things Leveraging Network Telescopes and Machine Learning." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7935.

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The recent advancements in computing and sensor technologies, coupled with improvements in embedded system design methodologies, have resulted in the novel paradigm called the Internet of Things (IoT). IoT is essentially a network of small embedded devices enabled with sensing capabilities that can interact with multiple entities to relay information about their environments. This sensing information can also be stored in the cloud for further analysis, thereby reducing storage requirements on the devices themselves. The above factors, coupled with the ever increasing needs of modern society t
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APRUZZESE, GIOVANNI. "Security Analytics and Machine Learning for Cyber Detection: Problematiche Moderne e Soluzioni Innovative." Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2020. http://hdl.handle.net/11380/1200588.

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La rilevazione efficace dei cyber-attacchi avanzati è un problema complesso che presenta numerose problematiche e sfide. Gli attaccanti più esperti migliorano continuamente i propri strumenti e, attraverso l'attuazione di strategie originali, sono in grado di eludere la rilevazione degli approcci tradizionali basati su regole statiche. Di conseguenza, molte data-breach richiedono mesi prima di essere identificate, provocando ingenti danni alle organizzazioni moderne. Gli operatori umani da soli non sono in grado di gestire il continuo aumento della complessità, varietà e velocità delle minacce
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DEMONTIS, AMBRA. "Securing Machine Learning against Adversarial Attacks." Doctoral thesis, Università degli Studi di Cagliari, 2018. http://hdl.handle.net/11584/255948.

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Machine learning techniques are nowadays widely used in different application domains, ranging from computer vision to computer security, despite it has been shown that they are vulnerable to well-crafted attacks performed by skilled attackers. These include evasion attacks aimed to mislead detection at test time, and poisoning attacks in which malicious samples are injected into the training data to compromise the learning procedure. Different defenses have been proposed so far. However, the majority of them is computationally expensive, and it is not clear under which attack conditions they
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Hu, Tianrui. "Detecting Bots using Stream-based System with Data Synthesis." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98595.

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Machine learning has shown great success in building security applications including bot detection. However, many machine learning models are difficult to deploy since model training requires the continuous supply of representative labeled data, which are expensive and time-consuming to obtain in practice. In this thesis, we build a bot detection system with a data synthesis method to explore detecting bots with limited data to address this problem. We collected the network traffic from 3 online services in three different months within a year (23 million network requests). We develop a novel
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Ferdowsi, Khosrowshahi Aidin. "Distributed Machine Learning for Autonomous and Secure Cyber-physical Systems." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99466.

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Autonomous cyber-physical systems (CPSs) such as autonomous connected vehicles (ACVs), unmanned aerial vehicles (UAVs), critical infrastructure (CI), and the Internet of Things (IoT) will be essential to the functioning of our modern economies and societies. Therefore, maintaining the autonomy of CPSs as well as their stability, robustness, and security (SRS) in face of exogenous and disruptive events is a critical challenge. In particular, it is crucial for CPSs to be able to not only operate optimally in the vicinity of a normal state but to also be robust and secure so as to withstand poten
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Tout, Hicham Refaat. "Measuring the Impact of email Headers on the Predictive Accuracy of Machine Learning Techniques." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/325.

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The majority of documented phishing attacks have been carried by email, yet few studies have measured the impact of email headers on the predictive accuracy of machine learning techniques in detecting email phishing attacks. Research has shown that the inclusion of a limited subset of email headers as features in training machine learning algorithms to detect phishing attack did increase the predictive accuracy of these learning algorithms. The same research also recommended further investigation of the impact of including an expanded set of email headers on the predictive accuracy of machine
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Delvecchio, Matthew David. "Enhancing Communications Aware Evasion Attacks on RFML Spectrum Sensing Systems." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99792.

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Recent innovations in machine learning have paved the way for new capabilities in the field of radio frequency (RF) communications. Machine learning techniques such as reinforcement learning and deep neural networks (DNN) can be leveraged to improve upon traditional wireless communications methods so that they no longer require expertly-defined features. Simultaneously, cybersecurity and electronic warfare are growing areas of focus and concern in an increasingly technology-driven world. Privacy and confidentiality of communication links are both more important and more difficult than ever in
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Chaitou, Hassan. "Optimization of security risk for learning on heterogeneous quality data." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT030.

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Les systèmes de détection d'intrusion (IDS) sont des composants essentiels dans l'infrastructure de sécurité des réseaux. Pour faire face aux problèmes de scalabilité des IDS utilisant des règles de détection artisanales, l'apprentissage automatique est utilisé pour concevoir des IDS formés sur des ensembles de données. Cependant, ils sont de plus en plus mis au défi par des méta-attaques, appelées attaques d'évasion adverses, qui modifient les attaques existantes pour améliorer leurs capacités d'évasion. Par exemple, ces approches utilisent les réseaux antagonistes génératifs (GAN) pour autom
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Likarish, Peter F. "Early detection of malicious web content with applied machine learning." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/4871.

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This thesis explores the use of applied machine learning techniques to augment traditional methods of identifying and preventing web-based attacks. Several factors complicate the identification of web-based attacks. The first is the scale of the web. The amount of data on the web and the heterogeneous nature of this data complicate efforts to distinguish between benign sites and attack sites. Second, an attacker may duplicate their attack at multiple, unexpected locations (multiple URLs spread across different domains) wit
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MORICHETTA, ANDREA. "Machine Learning and Big Data Approaches for Automatic Internet Monitoring." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2779392.

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35

Wei, Longfei. "Game-Theoretic and Machine-Learning Techniques for Cyber-Physical Security and Resilience in Smart Grid." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3850.

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The smart grid is the next-generation electrical infrastructure utilizing Information and Communication Technologies (ICTs), whose architecture is evolving from a utility-centric structure to a distributed Cyber-Physical System (CPS) integrated with a large-scale of renewable energy resources. However, meeting reliability objectives in the smart grid becomes increasingly challenging owing to the high penetration of renewable resources and changing weather conditions. Moreover, the cyber-physical attack targeted at the smart grid has become a major threat because millions of electronic devices
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Kulkarni, Keyur. "Android Malware Detection through Permission and App Component Analysis using Machine Learning Algorithms." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525454213460236.

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37

Flowers, Bryse Austin. "Adversarial RFML: Evading Deep Learning Enabled Signal Classification." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91987.

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Deep learning has become an ubiquitous part of research in all fields, including wireless communications. Researchers have shown the ability to leverage deep neural networks (DNNs) that operate on raw in-phase and quadrature samples, termed Radio Frequency Machine Learning (RFML), to synthesize new waveforms, control radio resources, as well as detect and classify signals. While there are numerous advantages to RFML, this thesis answers the question "is it secure?" DNNs have been shown, in other applications such as Computer Vision (CV), to be vulnerable to what are known as adversarial evasio
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Fereidooni, Hossein. "Security Issues of Mobile and Smart Wearable Devices." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3426666.

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Mobile and smart devices (ranging from popular smartphones and tablets to wearable fitness trackers equipped with sensing, computing and networking capabilities) have proliferated lately and redefined the way users carry out their day-to-day activities. These devices bring immense benefits to society and boast improved quality of life for users. As mobile and smart technologies become increasingly ubiquitous, the security of these devices becomes more urgent, and users should take precautions to keep their personal information secure. Privacy has also been called into question as so many of mo
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Stefanova, Zheni Svetoslavova. "Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7367.

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Given the continuing advancement of networking applications and our increased dependence upon software-based systems, there is a pressing need to develop improved security techniques for defending modern information technology (IT) systems from malicious cyber-attacks. Indeed, anyone can be impacted by such activities, including individuals, corporations, and governments. Furthermore, the sustained expansion of the network user base and its associated set of applications is also introducing additional vulnerabilities which can lead to criminal breaches and loss of critical data. As a result, t
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Otoum, Safa. "Machine Learning-driven Intrusion Detection Techniques in Critical Infrastructures Monitored by Sensor Networks." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39090.

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In most of critical infrastructures, Wireless Sensor Networks (WSNs) are deployed due to their low-cost, flexibility and efficiency as well as their wide usage in several infrastructures. Regardless of these advantages, WSNs introduce various security vulnerabilities such as different types of attacks and intruders due to the open nature of sensor nodes and unreliable wireless links. Therefore, the implementation of an efficient Intrusion Detection System (IDS) that achieves an acceptable security level is a stimulating issue that gained vital importance. In this thesis, we investigate the
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Mahmood, Waqas, and Muhammad Faheem Akhtar. "Validation of Machine Learning and Visualization based Static Code Analysis Technique." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4347.

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Software security has always been an afterthought in software development which results into insecure software. Companies rely on penetration testing for detecting security vulnerabilities in their software. However, incorporating security at early stage of development reduces cost and overhead. Static code analysis can be applied at implementation phase of software development life cycle. Applying machine learning and visualization for static code analysis is a novel idea. Technique can learn patterns by normalized compression distance NCD and classify source code into correct or faulty usage
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Shanyour, Basim. "Testing and Security Considerations in Presence of Process Variations." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1804.

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Process variations is one of the most challenging phenomena in deep submicron. Delay fault testing becomes more complicated because gate delays are not fixed but instead, they are statistical quantities due to the variations in the transistor characteristics. On the other hand, testing for hardware Trojan is also challenging in the presence of process variations because it can easily mask the impact of the inserted Trojan. This work consists of two parts. In the first part, an approach to detect ultra-low-power no-payload Trojans by analyzing IDDT waveforms at each gate in the presence of proc
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Burch, Zachary Campbell. "Credential Theft Powered Unauthorized Login Detection through Spatial Augmentation." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85583.

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Credential theft is a network intrusion vector that subverts traditional defenses of a campus network, with a malicious login being the act of an attacker using those stolen credentials to access the target network. Historically, this approach is simple for an attacker to conduct and hard for a defender to detect. Alternative mitigation strategies require an in depth view of the network hosts, an untenable proposition in a campus network. We introduce a method of spatial augmentation of login events, creating a user and source IP trajectory for each event. These location mappings, built using
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Klock, Robert. "Quality of SQL Code Security on StackOverflow and Methods of Prevention." Oberlin College Honors Theses / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1625831198110328.

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Nowroozi, Ehsan. "Machine Learning Techniques for Image Forensics in Adversarial Setting." Doctoral thesis, Università di Siena, 2020. http://hdl.handle.net/11365/1096177.

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The use of machine-learning for multimedia forensics is gaining more and more consensus, especially due to the amazing possibilities offered by modern machine learning techniques. By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceed those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques. However, the inherent vulnerability and fragility of machine learning architectures pose new serious security threats, hindering the use of these tools in security-oriented applications, and, among them, multi
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Sharma, Sagar. "Towards Data and Model Confidentiality in Outsourced Machine Learning." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1567529092809275.

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47

Ziemann, Ingvar. "Applications of Information Inequalities to Linear Systems : Adaptive Control and Security." Licentiate thesis, KTH, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291203.

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This thesis considers the application of information inequalities, Cramér-Rao type bounds, based on Fisher information, to linear systems. These tools are used to study the trade-offs between learning and performance in two application areas: adaptive control and control systems security. In the first part of the thesis, we study stochastic adaptive control of linear quadratic regulators (LQR). Here, information inequalities are used to derive instance-dependent  regret lower bounds. First, we consider a simplified version of LQR, a memoryless reference tracking model, and show how regret can
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48

Spolaor, Riccardo. "Security and Privacy Threats on Mobile Devices through Side-Channels Analysis." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3426796.

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In recent years, mobile devices (such as smartphones and tablets) have become essential tools in everyday life for billions of people all around the world. Users continuously carry such devices with them and use them for daily communication activities and social network interactions. Hence, such devices contain a huge amount of private and sensitive information. For this reason, mobile devices become popular targets of attacks. In most attack settings, the adversary aims to take local or remote control of a device to access user sensitive information. However, such violations are n
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49

Inci, Mehmet Sinan. "Micro-architectural Threats to Modern Computing Systems." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/528.

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With the abundance of cheap computing power and high-speed internet, cloud and mobile computing replaced traditional computers. As computing models evolved, newer CPUs were fitted with additional cores and larger caches to accommodate run multiple processes concurrently. In direct relation to these changes, shared hardware resources emerged and became a source of side-channel leakage. Although side-channel attacks have been known for a long time, these changes made them practical on shared hardware systems. In addition to side-channels, concurrent execution also opened the door to practical qu
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50

Labonne, Maxime. "Anomaly-based network intrusion detection using machine learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAS011.

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Ces dernières années, le piratage est devenu une industrie à part entière, augmentant le nombre et la diversité des cyberattaques. Les menaces qui pèsent sur les réseaux informatiques vont des logiciels malveillants aux attaques par déni de service, en passant par le phishing et l'ingénierie sociale. Un plan de cybersécurité efficace ne peut plus reposer uniquement sur des antivirus et des pare-feux pour contrer ces menaces : il doit inclure plusieurs niveaux de défense. Les systèmes de détection d'intrusion (IDS) réseaux sont un moyen complémentaire de renforcer la sécurité, avec la possibili
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