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1. CACTI: Captcha Avoidance via Client-side TEE Integration

   Yoshimichi Nakatsuka, Ercan Ozturk (January 2021, 30th USENIX}Security Symposium (USENIX Security 21))

   null (Ed.)

   Preventing abuse of web services by bots is an increasingly important problem, as abusive activities grow in both volume and variety. CAPTCHAs are the most common way for thwarting bot activities. However, they are often ineffective against bots and frustrating for humans. In addition, some recent CAPTCHA techniques diminish user privacy. Meanwhile, client-side Trusted Execution Environments (TEEs) are becoming increasingly widespread (notably, ARM TrustZone and Intel SGX), allowing establishment of trust in a small part (trust anchor or TCB) of client-side hardware. This prompts the question: can a TEE help reduce (or remove entirely) user burden of solving CAPTCHAs? In this paper, we design CACTI: CAPTCHA Avoidance via Client-side TEE Integration. Using client-side TEEs, CACTI allows legitimate clients to generate unforgeable rate-proofs demonstrating how frequently they have performed specific actions. These rate-proofs can be sent to web servers in lieu of solving CAPTCHAs. CACTI provides strong client privacy guarantees, since the information is only sent to the visited website and authenticated using a group signature scheme. Our evaluations show that overall latency of generating and verifying a CACTI rate-proof is less than 0.25 sec, while CACTI's bandwidth overhead is over 98% lower than that of current CAPTCHA systems. 

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2. Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence

   https://doi.org/10.1145/3505226  

   Zhang, Ning; Ebrahimi, Mohammadreza; Li, Weifeng; Chen, Hsinchun (June 2022, ACM Transactions on Management Information Systems)

   Automated monitoring of dark web (DW) platforms on a large scale is the first step toward developing proactive Cyber Threat Intelligence (CTI). While there are efficient methods for collecting data from the surface web, large-scale dark web data collection is often hindered by anti-crawling measures. In particular, text-based CAPTCHA serves as the most prevalent and prohibiting type of these measures in the dark web. Text-based CAPTCHA identifies and blocks automated crawlers by forcing the user to enter a combination of hard-to-recognize alphanumeric characters. In the dark web, CAPTCHA images are meticulously designed with additional background noise and variable character length to prevent automated CAPTCHA breaking. Existing automated CAPTCHA breaking methods have difficulties in overcoming these dark web challenges. As such, solving dark web text-based CAPTCHA has been relying heavily on human involvement, which is labor-intensive and time-consuming. In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection. This framework encompasses a novel generative method to recognize dark web text-based CAPTCHA with noisy background and variable character length. To eliminate the need for human involvement, the proposed framework utilizes Generative Adversarial Network (GAN) to counteract dark web background noise and leverages an enhanced character segmentation algorithm to handle CAPTCHA images with variable character length. Our proposed framework, DW-GAN, was systematically evaluated on multiple dark web CAPTCHA testbeds. DW-GAN significantly outperformed the state-of-the-art benchmark methods on all datasets, achieving over 94.4% success rate on a carefully collected real-world dark web dataset. We further conducted a case study on an emergent Dark Net Marketplace (DNM) to demonstrate that DW-GAN eliminated human involvement by automatically solving CAPTCHA challenges with no more than three attempts. Our research enables the CTI community to develop advanced, large-scale dark web monitoring. We make DW-GAN code available to the community as an open-source tool in GitHub. 

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3. Good Bot, Bad Bot: Characterizing Automated Browsing Activity

   Li, Xigao; Amin Azad, Babak; Rahmati, Amir; Nikiforakis, Nick (January 2021, Proceedings of the 42nd IEEE Symposium on Security and Privacy)

   null (Ed.)

   As the web keeps increasing in size, the number of vulnerable and poorly-managed websites increases commensurately. Attackers rely on armies of malicious bots to discover these vulnerable websites, compromising their servers, and exfiltrating sensitive user data. It is therefore crucial for the security of the web to understand the population and behavior of malicious bots. In this paper, we report on the design, implementation, and results of Aristaeus, a system for deploying large numbers of honeysites, i.e., websites that exist for the sole purpose of attracting and recording bot traffic. Through a seven-month-long experiment with 100 dedicated honeysites, Aristaeus recorded 26.4 million requests sent by more than 287K unique IP addresses, with 76K of them belonging to clearly malicious bots. By analyzing the type of requests and payloads that these bots send, we discover that the average honeysite received more than 37K requests each month, with more than 50% of these requests attempting to brute-force credentials, fingerprint the deployed web applications, and exploit large numbers of different vulnerabilities. By comparing the declared identity of these bots with their TLS handshakes and HTTP headers, we uncover that more than 86.2% of bots claiming to be Mozilla Firefox and Google Chrome are lying about their identity and are instead built on HTTP libraries and command-line tools. 

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4. A Verified Confidential Computing as a Service Framework for Privacy Preservation

   Chen, Hongbo; Chen, Haobin; Sun, Mingshen; Li, Kang; Chen, Zhaofeng; Wang, XiaoFeng (August 2023, the Proceedings of the 32nd USENIX Security Symposium)

   Joe Calandrino and Carmela Troncoso (Ed.)

   As service providers are moving to the cloud, users are forced to provision sensitive data to the cloud. Confidential computing leverages hardware Trusted Execution Environment (TEE) to protect data in use, no longer requiring users’ trust to the cloud. The emerging service model, Confidential Computing as a Service (CCaaS), is adopted by service providers to offer service similar to the Function-as-a-Serivce manner. However, privacy concerns are raised in CCaaS, especially in multi-user scenarios. CCaaS need to assure the data providers that the service does not leak their privacy to any unauthorized parties and clear their data after the service. To address such privacy concerns with security guarantees, we first formally define the security objective, Proof of Being Forgotten (PoBF), and prove under which security constraints PoBF can be satisfied. Then, these constraints serve as guidelines in the implementation of the PoBF-compliant Framework (PoCF). PoCF consists of a generic library for different hardware TEEs, CCaaS prototype enclaves, and a verifier to prove PoBF-compliance. PoCF leverages Rust’s robust type system and security features, to construct a verified state machine with privacy-preserving contracts. Last, the experiment results show that the protections introduced by PoCF incur minor runtime performance overhead. 

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5. TEE-Graph: efficient privacy and ownership protection for cloud-based graph spectral analysis

   https://doi.org/10.3389/fdata.2023.1296469  

   Alam, A. K.; Chen, Keke (November 2023, Frontiers in Big Data)

   IntroductionBig graphs like social network user interactions and customer rating matrices require significant computing resources to maintain. Data owners are now using public cloud resources for storage and computing elasticity. However, existing solutions do not fully address the privacy and ownership protection needs of the key involved parties: data contributors and the data owner who collects data from contributors. MethodsWe propose a Trusted Execution Environment (TEE) based solution: TEE-Graph for graph spectral analysis of outsourced graphs in the cloud. TEEs are new CPU features that can enable much more efficient confidential computing solutions than traditional software-based cryptographic ones. Our approach has several unique contributions compared to existing confidential graph analysis approaches. (1) It utilizes the unique TEE properties to ensure contributors' new privacy needs, e.g., the right of revocation for shared data. (2) It implements efficient access-pattern protection with a differentially private data encoding method. And (3) it implements TEE-based special analysis algorithms: the Lanczos method and the Nystrom method for efficiently handling big graphs and protecting confidentiality from compromised cloud providers. ResultsThe TEE-Graph approach is much more efficient than software crypto approaches and also immune to access-pattern-based attacks. Compared with the best-known software crypto approach for graph spectral analysis, PrivateGraph, we have seen that TEE-Graph has 10³−10⁵times lower computation, storage, and communication costs. Furthermore, the proposed access-pattern protection method incurs only about 10%-25% of the overall computation cost. DiscussionOur experimentation showed that TEE-Graph performs significantly better and has lower costs than typical software approaches. It also addresses the unique ownership and access-pattern issues that other TEE-related graph analytics approaches have not sufficiently studied. The proposed approach can be extended to other graph analytics problems with strong ownership and access-pattern protection. 

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