arXiv subjects

Renascence Tarafder Prapty

Publications and source records attributed to Renascence Tarafder Prapty.

6 recordsLinked to original sources

arXiv2026

DuoLungo: Usability Study of Duo 2FA

Multi-Factor Authentication (MFA) enhances login security by requiring multiple authentication factors. Its adoption has increased in response to more frequent and sophisticated attacks. Duo is widely used by organizations including Fortune 500 companies and major educational institutions, yet its usability has not been examined thoroughly or recently. Earlier studies focused on technical challenges during initial deployment but did not measure core usability metrics such as task completion time or System Usability Scale (SUS) scores. These results are also outdated, originating from a time when MFA was less familiar to typical users. We conducted a long-term, large-scale Duo usability study at the University of California Irvine during the 2024-2025 academic year, involving 2559 participants. Our analysis uses authentication log data and a survey of 57 randomly selected users. The average overhead of a Duo Push task is nearly 8 seconds, which participants described as short to moderate. Overhead varies with time of day, field of study, and education level. The rate of authentication failures due to incomplete Duo tasks is 4.35 percent, and 43.86 percent of survey respondents reported at least one Duo login failure. The Duo SUS score is 70, indicating good usability. Participants generally find Duo easy to use but somewhat annoying, while also reporting an increased sense of account security. They also described common issues and offered suggestions for improvement.

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arXiv2025

MADEA: A Malware Detection Architecture for IoT blending Network Monitoring and Device Attestation

Internet-of-Things (IoT) devices are vulnerable to malware and require new mitigation techniques due to their limited resources. To that end, previous research has used periodic Remote Attestation (RA) or Traffic Analysis (TA) to detect malware in IoT devices. However, RA is expensive, and TA only raises suspicion without confirming malware presence. To solve this, we design MADEA, the first system that blends RA and TA to offer a comprehensive approach to malware detection for the IoT ecosystem. TA builds profiles of expected packet traces during benign operations of each device and then uses them to detect malware from network traffic in real-time. RA confirms the presence or absence of malware on the device. MADEA achieves 100% true positive rate. It also outperforms other approaches with 160x faster detection time. Finally, without MADEA, effective periodic RA can consume at least ~14x the amount of energy that a device needs in one hour.

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arXiv2025

TOCTOU Resilient Attestation for IoT Networks (Full Version)

Internet-of-Things (IoT) devices are increasingly common in both consumer and industrial settings, often performing safety-critical functions. Although securing these devices is vital, manufacturers typically neglect security issues or address them as an afterthought. This is of particular importance in IoT networks, e.g., in the industrial automation settings. To this end, network attestation -- verifying the software state of all devices in a network -- is a promising mitigation approach. However, current network attestation schemes have certain shortcomings: (1) lengthy TOCTOU (Time-Of-Check-Time-Of-Use) vulnerability windows, (2) high latency and resource overhead, and (3) susceptibility to interference from compromised devices. To address these limitations, we construct TRAIN (TOCTOU-Resilient Attestation for IoT Networks), an efficient technique that minimizes TOCTOU windows, ensures constant-time per-device attestation, and maintains resilience even with multiple compromised devices. We demonstrate TRAIN's viability and evaluate its performance via a fully functional and publicly available prototype.

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arXiv2024

Using Retriever Augmented Large Language Models for Attack Graph Generation

As the complexity of modern systems increases, so does the importance of assessing their security posture through effective vulnerability management and threat modeling techniques. One powerful tool in the arsenal of cybersecurity professionals is the attack graph, a representation of all potential attack paths within a system that an adversary might exploit to achieve a certain objective. Traditional methods of generating attack graphs involve expert knowledge, manual curation, and computational algorithms that might not cover the entire threat landscape due to the ever-evolving nature of vulnerabilities and exploits. This paper explores the approach of leveraging large language models (LLMs), such as ChatGPT, to automate the generation of attack graphs by intelligently chaining Common Vulnerabilities and Exposures (CVEs) based on their preconditions and effects. It also shows how to utilize LLMs to create attack graphs from threat reports.

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arXiv2024

KESIC: Kerberos Extensions for Smart, IoT and CPS Devices

Secure and efficient multi-user access mechanisms are increasingly important for the growing number of Internet of Things (IoT) devices being used today. Kerberos is a well-known and time-tried security authentication and access control system for distributed systems wherein many users securely access various distributed services. Traditionally, these services are software applications or devices, such as printers. However, Kerberos is not directly suitable for IoT devices due to its relatively heavy-weight protocols and the resource-constrained nature of the devices. This paper presents KESIC, a system that enables efficient and secure multi-user access for IoT devices. KESIC aims to facilitate mutual authentication of IoT devices and users via Kerberos without modifying the latter's protocols. To facilitate that, KESIC includes a special Kerberized service, called IoT Server, that manages access to IoT devices. KESIC presents two protocols for secure and comprehensive multi-user access system for two types of IoT devices: general and severely power constrained. In terms of performance, KESIC onsumes $\approx~47$ times less memory, and incurs $\approx~135$ times lower run-time overhead than Kerberos.

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arXiv2023

Dazed & Confused: A Large-Scale Real-World User Study of reCAPTCHAv2

Since about 2003, captchas have been widely used as a barrier against bots, while simultaneously annoying great multitudes of users worldwide. As their use grew, techniques to defeat or bypass captchas kept improving, while captchas themselves evolved in terms of sophistication and diversity, becoming increasingly difficult to solve for both bots and humans. Given this long-standing and still-ongoing arms race, it is important to investigate usability, solving performance, and user perceptions of modern captchas. In this work, we do so via a large-scale (over 3, 600 distinct users) 13-month real-world user study and post-study survey. The study, conducted at a large public university, was based on a live account creation and password recovery service with currently prevalent captcha type: reCAPTCHAv2. Results show that, with more attempts, users improve in solving checkbox challenges. For website developers and user study designers, results indicate that the website context directly influences (with statistically significant differences) solving time between password recovery and account creation. We consider the impact of participants' major and education level, showing that certain majors exhibit better performance, while, in general, education level has a direct impact on solving time. Unsurprisingly, we discover that participants find image challenges to be annoying, while checkbox challenges are perceived as easy. We also show that, rated via System Usability Scale (SUS), image tasks are viewed as "OK", while checkbox tasks are viewed as "good". We explore the cost and security of reCAPTCHAv2 and conclude that it has an immense cost and no security. Overall, we believe that this study's results prompt a natural conclusion: reCAPTCHAv2 and similar reCAPTCHA technology should be deprecated.

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