<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>

Reliability and sensitive information protection are critical aspects of integrated circuits. A novel technique using near-field evanescent wave coupling from two subwavelength gratings (SWGs), with the input laser source delivered through an optical fiber is presented for tamper evidence of electronic components. The first grating of the pair of coupled subwavelength gratings (CSWGs) was milled directly on the output facet of the silica fiber using focused ion beam (FIB) etching. The second grating was patterned using e-beam lithography and etched into a glass substrate using reactive ion etching (RIE). The slightest intrusion attempt would separate the CSWGs and eliminate near-field coupling between the gratings. Tampering, therefore, would become evident. Computer simulations guided the design for optimal operation of the security solution. The physical dimensions of the SWGs, i.e. period and thickness, were optimized, for a 650 nm illuminating wavelength. The optimal dimensions resulted in a 560 nm grating period for the first grating etched in the silica optical fiber and 420 nm for the second grating etched in borosilicate glass. The incident light beam had a half-width at half-maximum (HWHM) of at least 7 µm to allow discernible higher transmission orders, and a HWHM of 28 µm for minimum noise. The minimum number of individual grating lines present on the optical fiber facet was identified as 15 lines. Grating rotation due to the cylindrical geometry of the fiber resulted in a rotation of the far-field pattern, corresponding to the rotation angle of moiré fringes. With the goal of later adding authentication to tamper evidence, the concept of CSWGs signature was also modeled by introducing random and planned variations in the glass grating. The fiber was placed on a stage supported by a nanomanipulator, which permitted three-dimensional displacement while maintaining the fiber tip normal to the surface of the glass substrate. A 650 nm diode laser was fixed to a translation mount that transmitted the light source through the optical fiber, and the output intensity was measured using a silicon photodiode. The evanescent wave coupling output results for the CSWGs were measured and compared to the simulation results.

<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>

<p> The growing trend of encrypted network traffic is changing the cybersecurity threat scene. Most critical infrastructures and organizations enhance service delivery by embracing digital platforms and applications that use encryption to ensure that data and Information are moved across networks in an encrypted form to improve security. While this protects data confidentiality, hackers are also taking advantage of encrypted network traffic to hide malicious software known as malware that will easily bypass the conventional detection mechanisms on the system because the traffic is not transparent for the monitoring mechanism on the system to analyze. Cybercriminals leverage encryption using cryptographic protocols such as SSL/TLS to launch malicious attacks. This hidden threat exists because of the SSL encryption of benign traffic. Hence, there is a need for visibility in encrypted traffic. This research was conducted to detect malware in encrypted network traffic without decryption. The existing solution involves bulk decryption, analysis, and re-encryption. However, this method is prone to privacy issues, is not cost-efficient, and is time-consuming, creating huge overhead on the network. In addition, limited research exists on detecting malware in encrypted traffic without decryption. There is a need to strike a balance between security and privacy by building an intelligent framework that can detect malicious activity in encrypted network traffic without decrypting the traffic prior to inspection. With the payload still encrypted, the study focuses on extracting metadata from flow features to train the machine-learning model. It further deployed this set of features as input to an autoencoder, leveraging the construction error of the autoencoder for anomaly detection. </p>

<p>In today’s world, cloud machines store an ever-increasing amount of sensitive user data, but it remains challenging to guarantee the security of our data. This is because a cloud machine’s system software—critical components like the operating system and hypervisor that can access and thus leak user data—is subject to attacks by numerous other tenants and cloud administrators. Trusted execution environments (TEEs) like Intel SGX promise to alter this landscape by leveraging a trusted CPU to create execution contexts (or enclaves) where data cannot be directly accessed by system software. Unfortunately, the protection provided by TEEs cannot guarantee complete data security. In particular, our data remains unprotected if a third-party service (e.g., Yelp) running inside an enclave is adversarial. Moreover, data can be indirectly leaked from the enclave using traditional memory side-channels.</p> <p><br></p> <p>This dissertation takes a significant stride towards strong user data protection in cloud machines using TEEs by defeating the critical threats of adversarial cloud services and memory side-channels. To defeat these threats, we systematically explore both software and hardware designs. In general, we designed software solutions to avoid costly hardware changes and present faster hardware alternatives.</p> <p><br></p> <p>We designed 4 solutions for this dissertation. Our Chancel system prevents data leaks from adversarial services by restricting data access capabilities through robust and efficient compiler-enforced software sandboxing. Moreover, our Obliviate and Obfuscuro systems leverage strong cryptographic randomization and prevent information leakage through memory side-channels. We also propose minimal CPU extensions to Intel SGX called Reparo that directly close the threat of memory side-channels efficiently. Importantly, each designed solution provides principled protection by addressing the underlying root-cause of a problem, instead of enabling partial mitigation.</p> <p><br></p> <p>Finally, in addition to the stride made by our work, future research thrust is required to make TEEs ubiquitous for cloud usage. We propose several such research directions to pursue the essential goal of strong user data protection in cloud machines.</p>

<p dir="ltr">Protecting personal information is growing increasingly important to the general public, to the point that major tech companies now advertise the privacy features of their products. Despite this, it remains challenging to implement applications that do not leak private information either directly or indirectly, through timing behavior, memory access patterns, or control flow side channels. Existing security and cryptographic techniques such as secure multiparty computation (MPC) provide solutions to privacy-preserving computation, but they can be difficult to use for non-experts and even experts.</p><p dir="ltr">This dissertation develops the design, theory and implementation of various language-based techniques that help programmers write privacy-critical applications under a strong threat model. The proposed languages support private structured data, such as trees, that may hide their structural information and complex policies that go beyond whether a particular field of a record is private. More crucially, the approaches described in this dissertation decouple privacy and programmatic concerns, allowing programmers to implement privacy-preserving applications modularly, i.e., to independently develop application logic and independently update and audit privacy policies. Secure-by-construction applications are derived automatically by combining a standard program with a separately specified security policy.</p><p><br></p>

<p dir="ltr">Brute force guessing attacks continue to pose a significant threat to user passwords. To protect user passwords against brute force attacks, many organizations impose restrictions aimed at forcing users to select stronger passwords. Organizations may also adopt stronger hashing functions in an effort to deter offline brute force guessing attacks. However, these defenses induce trade-offs between security, usability, and the resources an organization is willing to investigate to protect passwords. In order to make informed password policy decisions, it is crucial to understand the distribution over user passwords and how policy updates will impact this password distribution and/or the strategy of a brute force attacker.</p><p dir="ltr">This first part of this thesis focuses on developing rigorous statistical tools to analyze user password distributions and the behavior of brute force password attackers. In particular, we first develop several rigorous statistical techniques to upper and lower bound the guessing curve of an optimal attacker who knows the user password distribution and can order guesses accordingly. We apply these techniques to analyze eight password datasets and two PIN datasets. Our empirical analysis demonstrates that our statistical techniques can be used to evaluate password composition policies, compare the strength of different password distributions, quantify the impact of applying PIN blocklists, and help tune hash cost parameters. A real world attacker may not have perfect knowledge of the password distribution. Prior work introduced an efficient Monte Carlo technique to estimate the guessing number of a password under a particular password cracking model, i.e., the number of guesses an attacker would check before this particular password. This tool can also be used to generate password guessing curves, but there is no absolute guarantee that the guessing number and the resulting guessing curves are accurate. Thus, we propose a tool called Confident Monte Carlo that uses rigorous statistical techniques to upper and lower bound the guessing number of a particular password as well as the attacker's entire guessing curve. Our empirical analysis also demonstrate that this tool can be used to help inform password policy decisions, e.g., identifying and warning users with weaker passwords, or tuning hash cost parameters.</p><p dir="ltr">The second part of this thesis focuses on developing stronger password hashing algorithms to protect user passwords against offline brute force attacks. In particular, we establish that the memory hard function Scrypt, which has been widely deployed as password hash function, is maximally bandwidth hard. We also present new techniques to construct and analyze depth robust graph with improved concrete parameters. Depth robust graph play an essential rule in the design and analysis of memory hard functions.</p>

<p dir="ltr">Distributed cryptographic protocols typically assume a bounded number of malicious parties (who behave arbitrarily) in the system---and in turn, a lower bound on the number of <i>honest</i> parties (who follow and only follow a protocol faithfully/honestly without performing unspecified computations)---for their respective security guarantees to hold. However, when deploying these protocols in practice, the nature of computing parties does not necessarily align nicely with the protocols' assumptions. Specifically, there may be only a few honest/compliant parties, or none exists. Instead, non-malicious parties may be <i>semi-honest</i> (who follow the protocol specifications but are curious to learn as much information as possible from semi-honest parties' transcripts) or <i>rational</i> (who take actions that maximize their utilities instead of actions benefiting the protocol the most, e.g., performing extra computations or not following protocols). In such cases, the security guarantees of such protocols may deviate greatly in real life from what is theoretically promised, leaving a huge gap between theory and practice. </p><p dir="ltr">In this thesis, I bridge such a gap by enhancing the fault tolerance of various distributed cryptographic primitives by <i>relaxing the assumption on the existence of honest parties</i>.</p><p dir="ltr">First, in the context of <b>secure multi-party computations</b>, without honest parties, my goal is to induce honest (i.e., not compromising correctness) and non-curious (i.e., not harming privacy) behaviors from rational participants via game theoretic and cryptographic techniques. In particular, I first demonstrate how to ensure protocol correctness and deter collusion among parties to recover secrets---which also breaks privacy---in multiserver private information retrieval with a singleton access structure. Then for primitives with more general (non-singleton) access structures, I introduce a distinct treatment through the lens of verifiable secret sharing. The two solutions are designed with a public bulletin board, commitment schemes, digital signature schemes, zkSNARKs (zero-knowledge succinct non-interactive arguments of knowledge), and distinct incentive structures tailored for varying access structures underlying the schemes.</p><p dir="ltr">Second, in <b>permissionless blockchain systems</b>, for protocols without privacy guarantees like computation outsourcing and consensus, my goal is to incentivize rational parties to behave correctly. This means to act according to the protocol specifications or as implied by the security requirements of the primitive, e.g., fairly distribute rewards to participants based on contributions in proof-of-work (PoW) blockchains. Specifically, I present a defense against an undercutting attack in PoW blockchains from a game theory perspective and propose a decentralized computation outsourcing protocol built on permissionless blockchain systems based on multi-unit auctions.</p>

<p> As cyber threats become highly damaging and complex, a new cybersecurity compliance certification model has been developed by the Department of Defense (DoD) to secure its Defense Industrial Base (DIB), and communication with its private partners. These partners or contractors are obligated by the Defense Federal Acquisition Regulations (DFARS) to be compliant with the latest standards in computer and data security. The Cybersecurity Maturity Model Certification (CMMC), and it is built upon existing DFARS 252.204-7012 and the NIST SP 800-171 controls. As of 2020, the DoD has incorporated DFARS and the National Institute of Standards and Technology (NIST) recommended security practices into what is now the CMMC. This thesis examines the most commonly identified security control deficiencies faced, the attacks mitigated by addressing these deficiencies, and suggested remediations, to 127 DoD contractors in order to bring them into compliance with the CMMC guidelines. By working with a compliance service provider, an analysis is done on how companies are undergoing and implementing important changes in their processes, to protect crucial information from ever-growing and looming cyber threats. </p>