Global ETD Search

41	GARBLED COMPUTATION: HIDING SOFTWARE, DATAAND COMPUTED VALUES Shoaib Amjad Khan (19199497) 27 July 2024 (has links) <p dir="ltr">This thesis presents an in depth study and evaluation of a class of secure multiparty protocols that enable execution of a confidential software program $\mathcal{P}$ owned by Alice, on confidential data $\mathcal{D}$ owned by Bob, without revealing anything about $\mathcal{P}$ or $\mathcal{D}$ in the process. Our initial adverserial model is an honest-but-curious adversary, which we later extend to a malicious adverarial setting. Depending on the requirements, our protocols can be set up such that the output $\mathcal{P(D)}$ may only be learned by Alice, Bob, both, or neither (in which case an agreed upon third party would learn it). Most of our protocols are run by only two online parties which can be Alice and Bob, or alternatively they could be two commodity cloud servers (in which case neither Alice nor Bob participate in the protocols' execution - they merely initialize the two cloud servers, then go offline). We implemented and evaluated some of these protocols as prototypes that we made available to the open source community via Github. We report our experimental findings that compare and contrast the viability of our various approaches and those that already exist. All our protocols achieve the said goals without revealing anything other than upper bounds on the sizes of program and data.</p><p><br></p> Data and information privacy Data security and protection Software and application security Garbled computing Confidential computing Cryptographic construction security protocols. Security Protocol Analysis
42	Internet of Things and Cybersecurity in a Smart Home Kiran Vokkarne (17367391) 10 November 2023 (has links) <p dir="ltr">With the ability to connect to networks and send and receive data, Internet of Things (IoT) devices involve associated security risks and threats, for a given environment. These threats are even more of a concern in a Smart Home network, where there is a lack of a dedicated security IT team, unlike a corporate environment. While efficient user interface(UI) and ease of use is at the front and center of IoT devices within Smart Home which enables its wider adoption, often security and privacy have been an afterthought and haven’t kept pace when needed. Therefore, a unsafe possibility exists where malicious actors could exploit vulnerable devices in a domestic home environment.</p><p dir="ltr">This thesis involves a detailed study of the cybersecurity for a Smart Home and also examines the various types of cyberthreats encountered, such as DDoS, Man-In-Middle, Ransomware, etc. that IoT devices face. Given, IoT devices are commonplace in most home automation scenarios, its crucially important to detect intrusions and unauthorized access. Privacy issues are also involved making this an even more pertinent topic. Towards this, various state of the art industry standard tools, such as Nmap, Nessus, Metasploit, etc. were used to gather data on a Smart Home environment to analyze their impacts to detect security vulnerabilities and risks to a Smart Home. Results from the research indicated various vulnerabilities, such as open ports, password vulnerabilities, SSL certificate anomalies and others that exist in many cases, and how precautions when taken in timely manner can help alleviate and bring down those risks.</p><p dir="ltr">Also, an IoT monitoring dashboard was developed based on open-source tools, which helps visualize threats and emphasize the importance of monitoring. The IoT dashboard showed how to raise alerts and alarms based on specific threat conditions or events. In addition, currently available cybersecurity regulations, standards, and guidelines were also examined that can help safeguard against threats to commonly used IoT devices in a Smart Home. It is hoped that the research carried out in this dissertation can help maintain safe and secure Smart Homes and provide direction for future work in the area of Smart Home Cybersecurity.</p> Data and information privacy Data security and protection Hardware security Software and application security Information security management Software architecture Internet of things (IoT) Data Internet of Things (IoT) devices cybersecurity standards Smart Home User Interfaces SMART HOME SECURITY SYSTEM monitoring adaptation
43	Investigation of Backdoor Attacks and Design of Effective Countermeasures in Federated Learning Agnideven Palanisamy Sundar (11190282) 03 September 2024 (has links) <p dir="ltr">Federated Learning (FL), a novel subclass of Artificial Intelligence, decentralizes the learning process by enabling participants to benefit from a comprehensive model trained on a broader dataset without direct sharing of private data. This approach integrates multiple local models into a global model, mitigating the need for large individual datasets. However, the decentralized nature of FL increases its vulnerability to adversarial attacks. These include backdoor attacks, which subtly alter classification in some categories, and byzantine attacks, aimed at degrading the overall model accuracy. Detecting and defending against such attacks is challenging, as adversaries can participate in the system, masquerading as benign contributors. This thesis provides an extensive analysis of the various security attacks, highlighting the distinct elements of each and the inherent vulnerabilities of FL that facilitate these attacks. The focus is primarily on backdoor attacks, which are stealthier and more difficult to detect compared to byzantine attacks. We explore defense strategies effective in identifying malicious participants or mitigating attack impacts on the global model. The primary aim of this research is to evaluate the effectiveness and limitations of existing server-level defenses and to develop innovative defense mechanisms under diverse threat models. This includes scenarios where the server collaborates with clients to thwart attacks, cases where the server remains passive but benign, and situations where no server is present, requiring clients to independently minimize and isolate attacks while enhancing main task performance. Throughout, we ensure that the interventions do not compromise the performance of both global and local models. The research predominantly utilizes 2D and 3D datasets to underscore the practical implications and effectiveness of proposed methodologies.</p> Computer vision Data security and protection Distributed systems and algorithms Adversarial machine learning Backdoor Poisoning Attacks Backdoor Detection and Defense Trust Networks Generative Adversarial Networks
44	Measuring Data Protection: A Causal Artificial Intelligence Modeling Approach Robert R Morton II (20374230) 05 December 2024 (has links) <p dir="ltr">The research delves into the intricate challenge of quantifying data protection, a concept that has evolved from ancient ethical codes to the complex landscape of modern cybersecurity. The research underscores the pressing need for a scientific approach to cybersecurity, emphasizing the importance of measurable security properties and a robust theoretical foundation. It highlights the historical evolution of confidentiality, tracing its roots from ancient civilizations to the contemporary digital era, where the proliferation of technology has amplified both the important ortance and complexity of safeguarding sensitive information. The research identifies key challenges in measuring data protection, including the dynamic nature of threats, the gap between theoretical models and real-world implementations, and the difficulty of accurately modeling risks. It also explores societal challenges related to data protection, such as data breaches, surveillance, social media privacy erosion, and the lack of adequate regulations and enforcement.</p><p dir="ltr">The core of the research lies in developing a causal model that examines the interplay of security controls, vulnerabilities,and threats, providing a deeper understanding of the factors influencing data exposure. The model is built upon a comprehensive literature review, synthesizing key findings and establishing a taxonomy of security protections. The research outlines a structured approach to building and utilizing causality models, incorporating essential elements such as identifying key variables, visualizing causal relationships using Directed (A)cyclic Graphs (DAGs), and determining appropriate research methodologies. The model is rigorously validated through various techniques, including assessing model fit, examining confounding factors. The research also explores a general set of experiments for both interventions and counterfactual studies.</p><p dir="ltr">The research concludes by highlighting potential future research directions, particularly emphasizing the need for standardized data protection metrics and the development of adaptive security systems. It underscores the importance of consistent measurements that enable organizations to compare their security performance effectively and adapt to the evolving threat landscape. The development of adaptive security systems, capable of dynamically modifying defense mechanisms in response to new threats, is also identified as a crucial research avenue. The research's contribution lies in providing a systematic approach to studying data protection, from problem identification to model development, validation, and future directions, ultimately aiming to enhance the protection of sensitive information.</p> Autonomous agents and multiagent systems Data and information privacy Data security and protection data protection artificial intelligence causality modeling secrecy privacy deception trust risk modeling risk management adaptive agents supply-chain close-access remote physical lawful access
45	An Image-based ML Approach for Wi-Fi Intrusion Detection System and Education Modules for Security and Privacy in ML Rayed Suhail Ahmad (18476697) 02 May 2024 (has links) <p dir="ltr">The research work presented in this thesis focuses on two highly important topics in the modern age. The first topic of research is the development of various image-based Network Intrusion Detection Systems (NIDSs) and performing a comprehensive analysis of their performance. Wi-Fi networks have become ubiquitous in enterprise and home networks which creates opportunities for attackers to target the networks. These attackers exploit various vulnerabilities in Wi-Fi networks to gain unauthorized access to a network or extract data from end users' devices. The deployment of an NIDS helps detect these attacks before they can cause any significant damages to the network's functionalities or security. Within the scope of our research, we provide a comparative analysis of various deep learning (DL)-based NIDSs that utilize various imaging techniques to detect anomalous traffic in a Wi-Fi network. The second topic in this thesis is the development of learning modules for security and privacy in Machine Learning (ML). The increasing integration of ML in various domains raises concerns about its security and privacy. In order to effectively address such concerns, students learning about the basics of ML need to be made aware of the steps that are taken to develop robust and secure ML-based systems. As part of this, we introduce a set of hands-on learning modules designed to educate students on the importance of security and privacy in ML. The modules provide a theoretical learning experience through presentations and practical experience using Python Notebooks. The modules are developed in a manner that allows students to easily absorb the concepts regarding privacy and security of ML models and implement it in real-life scenarios. The efficacy of this process will be obtained from the results of the surveys conducted before and after providing the learning modules. Positive results from the survey will demonstrate the learning modules were effective in imparting knowledge to the students and the need to incorporate security and privacy concepts in introductory ML courses.</p> Data and information privacy Data security and protection System and network security Adversarial machine learning Deep learning adversarial machine learning (AdvML) Adversarial Machine Learning Privacy Preserving Machine Learning PPML AML Cyber Security AWID AWID3 Cybersecurity Education Intrusion Detection Wi-Fi Networks Deep Learning Wireless Intrusion Detection Security and Privacy
46	AspectKE: Security aspects with program analysis for distributed systems Fan, Yang, Masuhara, Hidehiko, Aotani, Tomoyuki, Nielson, Flemming, Nielson, Hanne Riis* January 2010 (has links) Enforcing security policies to distributed systems is difficult, in particular, when a system contains untrusted components. We designed AspectKE, a distributed AOP language based on a tuple space, to tackle this issue. In AspectKE, aspects can enforce access control policies that depend on future behavior of running processes. One of the key language features is the predicates and functions that extract results of static program analysis, which are useful for defining security aspects that have to know about future behavior of a program. AspectKE* also provides a novel variable binding mechanism for pointcuts, so that pointcuts can uniformly specify join points based on both static and dynamic information about the program. Our implementation strategy performs fundamental static analysis at load-time, so as to retain runtime overheads minimal. We implemented a compiler for AspectKE, and demonstrate usefulness of AspectKE through a security aspect for a distributed chat system. aspect oriented programming program analysis security policies distributed systems tuple spaces Data processing Computer science Language Constructs and Features (E.2) Abstract data types Classes and objects (NEW) Concurrent programming structures Constraints (NEW) Control structures Coroutines Data types and structures Dynamic storage management Frameworks (NEW) Inheritance (NEW) Input/output Modules, packages Patterns (NEW) Polymorphism (NEW) Procedures, functions, and subroutines Recursion Security and Protection (K.6.5) Access controls Authentication Cryptographic controls Information flow controls Security kernels Verification Algebraic approaches to semantics Denotational semantics Operational semantics Partial evaluation (NEW) Process models (NEW) Program analysis (NEW)

Page generated in 0.0962 seconds