<p dir="ltr">Security and privacy are one of the top concerns when experts select for communication protocols. When a protocol is confirmed with problems, such as leaking users’ privacy, the protocol developers will upgrade it to an advanced version to cover those concerns in a short interval, or the protocol will be discarded or replaced by other secured ones. </p><p dir="ltr">There are always communication protocols failing to protect users’ privacy or exposing users’ accounts under attack. A malicious user or an attacker can utilize the vulnerabilities in the protocol to gain private information, or even take control of the users’ devices. Hence, it is important to expose those protocols and improve them to enhance the security properties. Some protocols protect users’ privacy but in a less efficient way. Due to the new cryptography technique or the modern hardware support, the protocols can be improved with less overhead and enhanced security protection. </p><p dir="ltr">In this dissertation, we focus on analyzing and improving security-enhanced communication protocols in three aspects: </p><p dir="ltr">(1) We systematically analyzed an existing and widely used communication protocol: Zigbee. We identified the vulnerabilities of the existing Zigbee protocols during the new device joining process and proposed a security-enhanced Zigbee protocol. The new protocol utilized public-key primitives with little extra overhead with capabilities to protect against the outsourced attackers. The new protocol is formally verified and implemented with a prototype. </p><p dir="ltr">(2) We explored one type of communication detection system: Keyword-based deep packet inspection. The system has several protocols, such as BlindBox, PrivDPI, PE-DPI, mbTLS, and so on. We analyzed those protocols and identified their vulnerabilities or inefficiencies. To address those issues, we proposed three enhanced protocols: MT-DPI, BH-DPI, and CE-DPI which work readily with AES-based encryption schemes deployed and well-supported by AES-NI. Specifically, MT-DPI utilized multiplicative triples to support multi-party computation. </p><p dir="ltr">(3) We developed a technique to support Distributed confidential computing with the use of a trusted execution environment. We found that the existing confidential computing cannot handle multiple-stakeholder scenarios well and did not give reasonable control over derived data after computation. We analyzed six real use cases and pointed out what is missing in the existing solutions. To bridge the gap, we developed a language SeDS policy that was built on top of the trusted execution environment. It works well for specific privacy needs during the collaboration and gives protection over the derived data. We examined the language in the use cases and showed the benefits of applying the new policies.</p>

<p dir="ltr">Deep Neural Networks (DNNs) are deployed in many applications and protecting the privacy of training data has become a major concern. Membership Inference Attacks (MIAs) occur when an unauthorized person is able to determine whether a piece of data is used in training the DNNs. This paper investigates using Trusted Execution Environments (TEEs) in modern processors to protect the privacy of training data. Running DNNs on TEE, however, encounters many challenges, including limited computing and storage resources as well as a lack of development frameworks. This paper proposes a new method to partition pre-trained DNNs so that parts of the DNNs can fit into TEE to protect data privacy. The existing software infrastructure for running DNNs on TEE requires a significant amount of human effort using C programs. However, most existing DNNs are implemented using Python. This paper presents a framework that can automate most parts of the process of porting Python-based DNNs to TEE. The proposed method is deployed in Arm TrustZone-A on Raspberry Pi 3B+ with OPTEE-OS and evaluated on popular image classification models - AlexNet, ResNet, and VGG. Experimental results show that our method can reduce the accuracy of gradient-based MIAs on AlexNet, VGG- 16, and ResNet-20 evaluated on the CIFAR-100 dataset by 17.9%, 11%, and 35.3%. On average, processing an image in the native execution environment takes 4.3 seconds, whereas in the Trusted Execution Environment (TEE), it takes about 10.1 seconds per image.<br><br></p>

<p dir="ltr">In this thesis, we aim at securing database applications in different ways. We have designed, implemented and experimentally evaluated two systems, AD-PROM and DCAFixer. AD-PROM has the goal to monitor database application while running to detect changes in applications’ behaviors at run time. DCAFixer, focus on securing database applications at the early development stages, i.e., coding and testing.</p>

<p dir="ltr">The explosive growth of computer networks over the past few decades has significantly enhanced communication capabilities. However, this expansion has also attracted malicious attackers seeking to compromise and disable these networks for personal gain. Network Intrusion Detection Systems (NIDS) were developed to detect threats and alert users to potential attacks. As the types and methods of attacks have grown exponentially, NIDS have struggled to keep pace. A paradigm shift occurred when NIDS began using Machine Learning (ML) to differentiate between anomalous and normal traffic, alleviating the challenge of tracking and defending against new attacks. However, the adoption of ML-based anomaly detection in NIDS has unraveled a new avenue of exploitation due to the inherent inadequacy of machine learning models - their susceptibility to adversarial attacks.</p><p dir="ltr">In this work, we explore the application of adversarial attacks from the image domain to bypass Network Intrusion Detection Systems (NIDS). We evaluate both white-box and black-box adversarial attacks against nine popular ML-based NIDS models. Specifically, we investigate Projected Gradient Descent (PGD) attacks on two ML models, transfer attacks using adversarial examples generated by the PGD attack, the score-based Zeroth Order Optimization attack, and two boundary-based attacks, namely the Boundary and HopSkipJump attacks. Through comprehensive experiments using the NSL-KDD dataset, we find that logistic regression and multilayer perceptron models are highly vulnerable to all studied attacks, whereas decision trees, random forests, and XGBoost are moderately vulnerable to transfer attacks or PGD-assisted transfer attacks with approximately 60 to 70% attack success rate (ASR), but highly susceptible to targeted HopSkipJump or Boundary attacks with close to a 100% ASR. Moreover, SVM-linear is highly vulnerable to both transfer attacks and targeted HopSkipJump or Boundary attacks achieving around 100% ASR, whereas SVM-rbf is highly vulnerable to transfer attacks with a 77% ASR but only moderately to targeted HopSkipJump or Boundary attacks with a 52% ASR. Finally, both KNN and Label Spreading models exhibit robustness against transfer-based attacks with less than 30% ASR but are highly vulnerable to targeted HopSkipJump or Boundary attacks with a 100% ASR with a large perturbation. Our findings may provide insights for designing future NIDS that are robust against potential adversarial attacks.</p>

<p dir="ltr">The pervasive integration of mobile devices in today’s modern world, e.g., smartphones, IoT, and mixed-reality devices, has transformed various domains, enhancing user experiences, yet raising concerns about data security and privacy. Despite the implementation of various measures, such as permissions, to protect user privacy-sensitive data, vulnerabilities persist. These vulnerabilities pose significant threats to user privacy, including the risk of side-channel attacks targeting low-permission sensors. Additionally, the introduction of new permissions, such as the App Tracking Transparency framework in iOS, seeks to enhance user transparency and control over data sharing practices. However, these framework designs are accompanied by ambiguous developer guidelines, rendering them susceptible to deceptive patterns. These patterns can influence user perceptions and decisions, undermining the intended purpose of these permissions. Moreover, the emergence of new mobile technologies, e.g., mixed-reality devices, presents novel challenges in ensuring secure data sharing among multiple users in collaborative environments, while preserving usability.</p><p dir="ltr">In this dissertation, I focus on developing user-centered methods for enhancing the security and privacy of mobile system, navigating through the complexities of unsolicited data access strategies and exploring innovative approaches to secure device authentication and data sharing methodologies.</p><p dir="ltr">To achieve this, first, I introduce my work on the iStelan system, a three-stage side-channel attack. This method exploits the low-permission magnetometer sensor in smartphones to infer user sensitive touch data and application usage patterns. Through an extensive user study, I demonstrate the resilience of iStelan across different scenarios, surpassing the constraints and limitations of prior research efforts.</p><p dir="ltr">Second, I present my analysis and study on the App Tracking Transparency permission in iOS. Specifically, my work focuses on analyzing and detecting the dark patterns employed by app developers in the permission alerts to obtain user consent. I demonstrate my findings on the dark patterns observed in permission alerts on a large-scale of apps collected from Apple’s store, using both static and dynamic analysis methods. Additionally, I discuss the application of a between-subject user study to evaluate users’ perceptions and understanding when exposed to different alert patterns.</p><p dir="ltr">Lastly, I introduce StareToPair, a group pairing system that leverages multi-modal sensing technologies in mixed-reality devices to enable secure data sharing in collaborative settings. StareToPair employs a sophisticated threat model capable of addressing various real-world scenarios, all while ensuring high levels of scalability and usability.</p><p dir="ltr">Through rigorous investigation, theoretical analysis and user studies, my research endeavors enhance the field of security and privacy for mobile systems. The insights gained from these studies offer valuable guidance for future developments in mobile systems, ultimately contributing to the design of user-centered secure and privacy-aware mobile ecosystems.</p>

<p>The prevalence of offline password attacks, resulting from attackers breaching authentication servers and stealing cryptographic password hashes, poses a significant threat. Users' tendency to select weak passwords and reuse passwords across multiple accounts, coupled with computation advancement,  further exacerbate the danger.</p> <p><br></p> <p>This dissertation addresses this issue by proposing password authentication mechanisms that aim to minimize the number of compromised passwords in the event of offline attacks, while ensuring that the server's workload remains manageable. Specifically, we present three mechanisms: (1) DAHash: This mechanism adjusts password hashing costs based on the strength of the underlying password. Through appropriate tuning of hashing cost parameters, the DAHash mechanism effectively reduces the fraction of passwords that can be cracked by an offline password cracker. (2) Password Strength Signaling: We explore the application of Bayesian Persuasion to password authentication. The key idea is to have the authentication server store a noisy signal about the strength of each user password for an offline attacker to find. We demonstrate that by appropriately tuning the noise distribution for the signal, a rational attacker will crack fewer passwords. (3) Cost-Asymmetric Memory Hard Password Hashing: We extend the concept of password peppering to modern Memory Hard password hashing algorithms. We identify limitations in naive extensions and introduce the concept of cost-even breakpoints as a solution. This approach allows us to overcome these limitations and achieve cost-asymmetry, wherein the expected cost of validating a correct password is significantly smaller than the cost of rejecting an incorrect password.</p> <p><br></p> <p>When analyzing the behavior of a rational attacker it is important to understand the attacker’s guessing curve i.e., the percentage of passwords that the attacker could crack within a guessing budget B. Dell’Amico and Filippone introduced a Monte Carlo algorithm to estimate the guessing number of a password as well as an estimate for the guessing curve. While the estimated guessing number is accurate in expectation the variance can be large and the method does not guarantee that the estimates are accurate with high probability. Thus, we introduce Confident Monte Carlo as a tool to provide confidence intervals for guessing number estimates and upper/lower bound the attacker’s guessing curves.</p> <p><br></p> <p>Moreover, we extend our focus beyond classical attackers to include quantum attackers. We present a decision-theoretic framework that models the rational behavior of attackers equipped with quantum computers. The objective is to quantify the capabilities of a rational quantum attacker and the potential damage they could inflict, assuming optimal decision-making. Our framework can potentially contribute to the development of effective countermeasures against a wide range of quantum pre-image attacks in the future.</p>

<p dir="ltr">VANET is formed by vehicles, road units, infrastructure components, and various con- nected objects.It aims mainly to ensure public safety and traffic control. New emerging applications include value-added and user-oriented services. While this technological ad- vancement promises ubiquitous deployment of the VANET, security and privacy challenges must be addressed. Thence, vehicle authentication is a vital process to detect malicious users and prevent them from harming legitimate communications. Hover, the authentication pro- cess uses sensitive information to check the vehicle’s identity. Sharing this information will harm vehicle privacy. In this thesis, we aim to deal with this issues:</p><ul><li>How can we ensure vehicle authentication and avoid sensitive and identity information leaks simultaneously?</li><li>When nodes are asked to provide identity proof, how can we ensure that the shared information is only used by an authorized entity?</li><li>Can we define an effective scheme to distinguish between legitimate and malicious network nodes?This dissertation aims to address the preservation of vehicle private information used within the authentication mechanism in VANET communications.The VANET characteristics are thoroughly presented and analyzed. Security require- ments and challenges are identified. Additionally, we review the proposed authentication techniques and the most well-known security attacks while focusing on the privacy preser- vation need and its challenges.To fulfill, the privacy preservation requirements, we proposed a new solution called Active Bundle AUthentication Solution based on SDN for Vehicular Networks (ABAUS). We intro- duce the Software Defined Networks (SDN) as an authentication infrastructure to guarantee the authenticity of each participant. Furthermore, we enhance the preservation of sensitive data by the use of an active data Bundle (ADB) as a self-protecting security mechanism. It ensures data protection throughout the whole data life cycle. ABAUS defines a dedicated registration protocol to verify and validate the different members of the network.</li></ul><p dir="ltr">first solution focused on legitimate vehicle identification and sensitive data pro- tection. A second scheme is designed to recognize and eliminate malicious users called BEhaviour-based REPutation scheme for privacy preservation in VANET using blockchain technology (BEREP). Dedicated public blockchains are used by a central trust authority to register vehicles and store their behavior evaluation and a trust scoring system allows nodes to evaluate the behavior of their communicators and detect malicious infiltrated users.</p><p dir="ltr">By enhancing sensitive data preservation during the authentication process and detect- ing malicious attempts, our proposed work helps to tackle serious challenges in VANET communications.</p>

<p dir="ltr">In the modern, internet-connected world, online actions have a big impact. Organizational information system security is a complex issue, with both external attacks and internal vulnerabilities posing serious risks. Although there is ample evidence linking job discontent and stress in the context of insider threat prediction, the stress caused by a perceived lack of social support is mostly unstudied. This research seeks to address this gap by assessing how aggressive behaviors outside the workplace and the absence of offline social support can predict insider threat behaviors within organizations. Given the prevalence of insider threats, a comprehensive investigation into their motivations and actions is imperative. Understanding these dynamics can provide organizations with crucial insights to effectively manage this persistent risk. The widespread nature of insider threats calls for a thorough study into their roots, motives, and behaviors. By comprehensively analyzing these factors, companies can gain valuable insights into insider threats' dynamics and develop effective risk management strategies.</p><p dir="ltr">The study conducted a survey with 206 participants recruited through Amazon Mechanical Turk (MTurk), analyzing data using SPSS. The survey consisted of several questionnaires, including demographic information, insider threat traits, cyberaggressive behaviors, online and offline social support. The correlational analysis revealed significant variables related to insider threat characteristics. The results of the study suggested that Cyberbullying and Deception were significant predictors of Hacking and Identity Theft. Additionally, individuals displaying traits of Unwanted Contact and Online Harassment outside the workplace were more likely to exhibit insider threat behaviors within an organization. Notably, the lack of online social support was not found to be indicative of insider threats. However, the absence of offline social support was associated with an increased probability of individuals engaging in cybercrimes within organizational settings.</p><p dir="ltr">The findings suggest that organizations and information security policymakers should implement strategies to mitigate insider threats effectively. To manage insider threats, organizations should focus on behavioral cues, implement positive interventions and utilize technical monitoring to track online actions of insiders. Understanding the psychological, behavioral, and technical aspects of insider threats is crucial for early detection and prevention. Policymakers at companies should not only focus on traditional background checks related to criminal history but also consider psychological and behavioral factors to prevent insider threats effectively. By integrating these insights into policies and practices, companies can enhance their ability to mitigate potential insider threats effectively.</p><p dir="ltr">The present study augments the existing literature on insider threats and cyber aggression by examining the influence of stressors on employee behavior. Building upon prior research, this investigation delves into the nuanced impact of both offline and online social support systems on stress levels experienced by employees. It explores how the absence of adequate offline and online social support can exacerbate stress levels, consequently increasing the likelihood of insider threats and cyber aggression. In conclusion, the findings of this research contribute significantly to our understanding of the pivotal role of offline social support in mitigating workplace stress. Moreover, it underscores the importance of understanding individual online presence and background verification processes in evaluating potential risks within the workplace.</p>

<p dir="ltr">Our research revolves around the evolving landscape of Open-Source Software (OSS) supply chains, emphasizing their critical role in contemporary software development while investigating the escalating security concerns associated with their integration. As OSS continues to shape the software ecosystem, our research acknowledges the paradigm shift in the software supply chain, highlighting its complexity and the associated security challenges. Focusing on Debian packages, we employ advanced network science methods to comprehensively assess the structural dynamics and vulnerabilities within the OSS supply chain. The study is motivated by the imperative to understand, model, and mitigate security risks from interconnected software components.</p><p dir="ltr">Our research questions delve into 1) identifying high-risk packages 2) comparing risk profiles between source and build stages and 3) predicting future vulnerabilities. Data collection involves collecting source code repositories, build-info information, and vulnerability data of Debian packages. Leveraging a multifaceted methodology, we perform the following things: graph construction, subsampling, metrics creation, explorative data analysis, and statistical investigations on the Debian package network. This statistical approach integrates the Wilcoxon test, Chi-Square test, and advanced network dynamics modeling with machine learning, to explore evolving trends and correlations between different stages of the OSS supply chain.</p><p dir="ltr">Our goals include providing actionable insights for industry practitioners, policymakers, and developers to enhance risk management in the OSS supply chain. The expected outcomes encompass an enriched understanding of vulnerability propagation, the identification of high-risk packages, and the comparison of network-based risk metrics against traditional software engineering measures. Ultimately, our research contributes to the ongoing discourse on securing open-source ecosystems, offering practical strategies for risk mitigation and fostering a safer and more resilient OSS supply chain.</p>

<p>Dockerizing web servers has gained significant popularity due to its lightweight containerization approach, enabling rapid and efficient deployment of web services. However, the security of web server containers remains a critical concern. This study proposes a novel approach to enhance the security of Docker-based web servers using bpftrace to trace Nginx and Apache containers under attack, identifying abnormal syscalls, connections, shared library calls, and file accesses from normal ones. The gathered metrics are used to generate tailored AppArmor profiles for improved mandatory access control policies and enhanced container security. BPFtrace is a high-level tracing language allowing for real-time analysis of system events. This research introduces an innovative method for generating AppArmor profiles by utilizing BPFtrace to monitor system alerts, creating customized security policies tailored to the specific needs of Docker-based web servers. Once the profiles are generated, the web server container is redeployed with enhanced security measures in place. This approach increases security by providing granular control and adaptability to address potential threats. The evaluation of the proposed method is conducted using CVE’s found in the open source literature affecting nginx and apache web servers that correspond to the classification system that was created. The Apache and Nginx containers was attacked with Metasploit, and benchmark tests including ltrace evaluation in accordance with existing literature were conducted. The results demonstrate the effectiveness of the proposed approach in mitigating security risks and strengthening the overall security posture of Docker-based web servers. This is achieved by limiting memcpy and memset shared library calls identified using bpftrace and applying rlimits in 9 AppArmor to limit their rate to normal levels (as gauged during testing) and deny other harmful file accesses and syscalls. The study’s findings contribute to the growing body of knowledge on container security and offer valuable insights for practitioners aiming to develop more secure web server deployments using Docker. </p>