arXiv subjects

Raheem Beyah

Publications and source records attributed to Raheem Beyah.

22 records · Page 2Linked to original sources

arXiv2019

De-Health: All Your Online Health Information Are Belong to Us

In this paper, we study the privacy of online health data. We present a novel online health data De-Anonymization (DA) framework, named De-Health. De-Health consists of two phases: Top-K DA, which identifies a candidate set for each anonymized user, and refined DA, which de-anonymizes an anonymized user to a user in its candidate set. By employing both candidate selection and DA verification schemes, De-Health significantly reduces the DA space by several orders of magnitude while achieving promising DA accuracy. Leveraging two real world online health datasets WebMD (89,393 users, 506K posts) and HealthBoards (388,398 users, 4.7M posts), we validate the efficacy of De-Health. Further, when the training data are insufficient, De-Health can still successfully de-anonymize a large portion of anonymized users. We develop the first analytical framework on the soundness and effectiveness of online health data DA. By analyzing the impact of various data features on the anonymity, we derive the conditions and probabilities for successfully de-anonymizing one user or a group of users in exact DA and Top-K DA. Our analysis is meaningful to both researchers and policy makers in facilitating the development of more effective anonymization techniques and proper privacy polices. We present a linkage attack framework which can link online health/medical information to real world people. Through a proof-of-concept attack, we link 347 out of 2805 WebMD users to real world people, and find the full names, medical/health information, birthdates, phone numbers, and other sensitive information for most of the re-identified users. This clearly illustrates the fragility of the notion of privacy of those who use online health forums.

cs.CR↗

arXiv2019

On Evaluating the Effectiveness of the HoneyBot: A Case Study

In recent years, cyber-physical system (CPS) security as applied to robotic systems has become a popular research area. Mainly because robotics systems have traditionally emphasized the completion of a specific objective and lack security oriented design. Our previous work, HoneyBot \cite{celine}, presented the concept and prototype of the first software hybrid interaction honeypot specifically designed for networked robotic systems. The intuition behind HoneyBot was that it would be a remotely accessible robotic system that could simulate unsafe actions and physically perform safe actions to fool attackers. Unassuming attackers would think they were connected to an ordinary robotic system, believing their exploits were being successfully executed. All the while, the HoneyBot is logging all communications and exploits sent to be used for attacker attribution and threat model creation. In this paper, we present findings from the result of a user study performed to evaluate the effectiveness of the HoneyBot framework and architecture as it applies to real robotic systems. The user study consisted of 40 participants, was conducted over the course of several weeks, and drew from a wide range of participants aged between 18-60 with varying level of technical expertise. From the study we found that research subjects could not tell the difference between the simulated sensor values and the real sensor values coming from the HoneyBot, meaning the HoneyBot convincingly spoofed communications.

cs.CR↗

arXiv2019

Adversarial CAPTCHAs

Following the principle of to set one's own spear against one's own shield, we study how to design adversarial CAPTCHAs in this paper. We first identify the similarity and difference between adversarial CAPTCHA generation and existing hot adversarial example (image) generation research. Then, we propose a framework for text-based and image-based adversarial CAPTCHA generation on top of state-of-the-art adversarial image generation techniques. Finally, we design and implement an adversarial CAPTCHA generation and evaluation system, named aCAPTCHA, which integrates 10 image preprocessing techniques, 9 CAPTCHA attacks, 4 baseline adversarial CAPTCHA generation methods, and 8 new adversarial CAPTCHA generation methods. To examine the performance of aCAPTCHA, extensive security and usability evaluations are conducted. The results demonstrate that the generated adversarial CAPTCHAs can significantly improve the security of normal CAPTCHAs while maintaining similar usability. To facilitate the CAPTCHA security research, we also open source the aCAPTCHA system, including the source code, trained models, datasets, and the usability evaluation interfaces.

cs.CR↗

arXiv2018

Blindsight: Blinding EM Side-Channel Leakage using Built-In Fully Integrated Inductive Voltage Regulator

Modern high-performance as well as power-constrained System-on-Chips (SoC) are increasingly using hardware accelerated encryption engines to secure computation, memory access, and communication operations. The electromagnetic (EM) emission from a chip leaks information of the underlying logical operations and can be collected using low-cost non-invasive measurements. EM based side-channel attacks (EMSCA) have emerged as a major threat to security of encryption engines in a SoC. This paper presents the concept of Blindsight where a high-frequency inductive voltage regulator (IVR) integrated on the same chip with an encryption engine is used to increase resistance against EMSCA. High-frequency (~100MHz) IVRs are present in modern microprocessors to improve energy-efficiency. We show that an IVR with a randomized control loop (R-IVR) can reduce EMSCA as the integrated inductance acts as a strong EM emitter and blinds an adversary from EM emission of the encryption engine. The EM measurements are performed on a test-chip containing two architectures of a 128-bit Advanced Encryption Standard (AES) engine powered by a high-frequency R-IVR and under two attack scenarios, one, where an adversary gains complete physical access of the target device and the other, where the adversary is only in proximity of the device. In both attack modes, an adversary can observe information leakage in Test Vector Leakage Assessment (TVLA) test in a baseline IVR (B-IVR, without control loop randomization). However, we show that EM emission from the R-IVR blinds the attacker and significantly reduces SCA vulnerability of the AES engine. A range of practical side-channel analysis including TVLA, Correlation Electromagnetic Analysis (CEMA), and a template based CEMA shows that R-IVR can reduce information leakage and prevent key extraction even against a skilled adversary.

cs.CR↗