The energy industry is undergoing significant transformations as it strives to achieve net-zero emissions and future-proof its infrastructure, where every participant in the power grid has the potential to both consume and produce energy resources. Federated learning -- which enables multiple participants to collaboratively train a model without aggregating the training data -- becomes a viable technology. However, the global model parameters that have to be shared for optimization are still susceptible to training data leakage. In this work, we propose confined gradient descent (CGD) that enhances the privacy of federated learning by eliminating the sharing of global model parameters. CGD exploits the fact that a gradient descent optimization can start with a set of discrete points and converges to another set in the neighborhood of the global minimum of the objective function. As such, each participant can independently initiate its own private global model~(referred to as the confined model ), and collaboratively learn it towards the optimum. The updates to their own models are worked out in a secure collaborative way during the training process.In such a manner, CGD retains the ability of learning from distributed data but greatly diminishes information sharing. Such a strategy also allows the proprietary confined models to adapt to the heterogeneity in federated learning, providing inherent benefits of fairness. We theoretically and empirically demonstrate that decentralized CGD øne provides a stronger differential privacy (DP) protection; \two is robust against the state-of-the-art poisoning privacy attacks; þree results in bounded fairness guarantee among participants; and \four provides high test accuracy (comparable with centralized learning) with a bounded convergence rate over four real-world datasets.
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Federated learning enables collaborative AI training across organizations without compromising data privacy. Decentralized federated learning (DFL) improves this by offering enhanced reliability and security through peer-to-peer (P2P) model sharing. However, DFL faces challenges in terms of slow convergence rate due to complex P2P graphs. To address this issue, we propose an efficient algorithm to accelerate DFL by introducing a limited number of k of edges into the P2P graphs. Specifically, we establish a connection between the convergence rate and the second smallest eigenvalue of the laplacian matrix of the P2P graph. We prove that finding the optimal set of edges to maximize this eigenvalue is an NP-complete problem. Our quantitative analysis shows the positive effect of strategic edge additions on improving this eigenvalue. Based on the analysis, we then propose an efficient algorithm to compute the best set of candidate edges to maximize the second smallest eigenvalue, and consequently the convergence rate is maximized. Our algorithm has a low time complexity of O(krn^2). Experimental results on diverse datasets validate the effectiveness of our proposed algorithms in accelerating DFL convergence.
Traditionally, top-level domains (TLDs) are managed by the Internet corporation for assigned names and numbers (ICANN), and the domain names under them are managed by registrars. Against such a centralized management, a blockchain naming service (BNS) has been proposed to manage TLDs on blockchains without authority intervention. BNS users can register TLD strings as non-fungible tokens and manage the TLD root zone. However, such a decentralized management results in the introduction of a new security issue, BNS TLD name collision, wherein the same TLD is registered in several different BNSs. In this study, we investigated BNS TLD name collisions by analyzing TLDs registered on two BNSs: Handshake and Decentraweb. Specifically, we collected TLDs registered in Handshake and Decentraweb and the associated data, and analyzed the data registration status of BNS TLDs and BNS TLD name collisions. The analysis of 11,595,406 Handshake and 11,889 Decentraweb TLDs revealed 6,973 BNS TLD name collisions. In particular, lastname TLDs, which are intended for use as person names, yielded a large number of registered domain names. In addition, the analysis identified 10 name collisions between the BNS and operational ICANN TLDs. Further, the ICANN TLD candidates under review also had name collisions against the BNS TLDs. Consequently, based on the characteristics of these name collisions and discussions in BNS communities, we considered countermeasures against BNS TLD name collisions. For the further development of BNSs, we believe that it is essential to discuss with the existing Internet communities and coexist with the existing Internet.
Serverless computing, also known as Function-as-a-Service (FaaS), triggers web applications in the form of function chains. It uses a central orchestrator to route all requests from end-users and internal functions. Such architecture simplifies application deployment for developers. However, the convenient centralized network architecture compromises the efficiency of function chain communications. Specifically, (i) a centralized API gateway assists in routing requests between functions. This indirect routing scheme raises invocation latency. (ii) The control flow for invoking functions and the data flow for passing function data packets are both forwarded by the API gateway. This results in the API gateway consuming a significant amount of resources. (iii) All data packets of internal function communications go through the same API gateway. This expands the additional attack surface in multi-tenant scenarios. In this paper, we propose DirectFaaS, a clean-slate network architecture to improve the function chain communication performance. By separating coupled control flow and data flow, DirectFaaS releases the API gateway from heavy traffic forwarding, reducing its resource consumption. For this goal, DirectFaaS exploits the network control capabilities of Software-Defined Networking (SDN) to establish direct data forwarding channels to accelerate function chain invocations. In addition, the data flow constrained by fine-grained network policies consolidates multi-tenant traffic security. We implement the DirectFaaS prototype on the popular OpenFaaS platform. Evaluations under real-world serverless applications show that DirectFaaS achieves a reduction in application execution time by up to 30.9% and CPU consumption by up to 30.1% compared to the current architecture.
The global deployment of the 5G network has led to a substantial increase in the deployment of edge servers to host web applications, catering to the growing demand for low service latency by edge web users. Yet, running edge servers 24/7 leads to enormous energy consumption and excessive carbon emissions. Energy-efficient edge resource provision is desired to achieve sustainable development goals in the new multi-access edge computing (MEC) architecture. Recently, several approaches have been proposed to solve the demand response problem for energy saving in cloud computing and MEC. However, accurate location information of edge web users should always be provided, which sacrifices users' privacy. To protect edge web users' location privacy while saving energy in MEC, we systematically formulate this location privacy-preserving edge demand response (LEDR) problem. To solve the LEDR problem effectively and efficiently, we propose a system named GEES by incorporating differential geo-obfuscation to secure user privacy while maximizing system utility and energy efficiency through inferences with theoretical analysis. Extensive and comprehensive experiments are conducted based on a synthetic real-world dataset, and the results demonstrate that GEES outperforms representative approaches by 23.02%, 31.47%, and 17.29% on average in terms of energy efficiency, user privacy and system utility.
Anomaly detection on graphs has recently attracted considerable attention due to its broad range of high-impact applications, including cybersecurity, financial transactions, and recommendation systems. Although many efforts have thus far been made, how to effectively handle the high inconsistency between users' behavior and labels, a fundamental issue in anomaly detection, has not yet received sufficient concern. Moreover, the inconsistency problem is hard to investigate and even deteriorates the performance of anomaly detectors. To this end, we propose a novel graph self-supervised learning framework, Capsule Graph Infomax (termed CapsGI), to overcome the inconsistency of anomaly detection. Inspired by the recent advances of capsules on images, we explore another possibility of reforming the node embedding by capsule ideas to represent the unique node's properties. Concretely, by disentangling heterogeneous factors underlying each node representation, we can establish node capsules such that their representation can reflect intrinsic node properties. To strengthen the connection among normal nodes, CapsGI further represents the part-whole contrastive learning between lower-level capsules (part) and higher-level capsules (whole) by explicitly considering the context graph relations. Extensive experiments on multiple real-world datasets demonstrate that our model significantly outperforms state-of-the-art models.
Euphemisms are widely used on social media and darknet markets to evade supervision. For instance, "ice" serves as a euphemism for the target keyword "methamphetamine" in illicit transactions. Thus, euphemism identification which aims to map the euphemism to its secret meaning (target keyword) is a crucial task in ensuring social network security. However, this task poses significant challenges, including resource limitations due to the unavailable of annotated datasets and linguistic challenges arising from subtle differences in meaning between target keywords. Existing methods employed self-supervised schemes to automatically construct labeled training data, addressing the resource limitations. Yet, these methods rely on static embedding methods that fail to distinguish between target keywords with similar meanings. In addition, we observe that different euphemisms in similar contexts confuse the identification results. To overcome these obstacles, we propose a feature fusion and individualization (FFI) method for euphemism identification. First, we reformulate the task as a cloze task, making it more feasible. Next, we develop a feature fusion module to capture both dynamic global and static local features, enhancing discrimination between different euphemisms in similar contexts. Additionally, we employ a feature individualization module to ensure each target keyword has a unique feature representation by projecting features into their orthogonal space. As a result, FFI can effectively identify similar euphemisms that refer to target keywords with similar meanings. Experimental results demonstrate that our method outperforms state-of-the-art methods and large language models, providing robust support for its effectiveness.
Over the last few years, the adoption of encryption in network traffic has been constantly increasing. The percentage of encrypted communications worldwide is estimated to exceed 90%. Although network encryption protocols mainly aim to secure and protect users' online activities and communications, they have been exploited by malicious entities that hide their presence in the network. It was estimated that in 2022, more than 85% of the malware used encrypted communication channels. In this work, we examine state-of-the-art fingerprinting techniques and extend a machine learning pipeline for effective and practical server classification. Specifically, we actively contact servers to initiate communication over the TLS protocol and through exhaustive requests, we extract communication metadata. We investigate which features favor an effective classification, following state-of-the-art approaches. Our extended pipeline can indicate whether a server is malicious or not with 91% precision and 95% recall, while it can specify the botnet family with 99% precision and 99% recall.
Despite the relentless efforts on developing anti-phishing techniques, phishing attacks continue to proliferate, often incorporating evasion techniques to bypass detection. While recent studies have continuously enhanced our understanding of their evasion techniques in desktop environments, few studies have been conducted to explore how the phishing attack is being handled in mobile environments, specifically WebView. In this study, we systematically evaluate the blocking processes of anti-phishing entities in individual apps in the real world by designing the phishing attack tailored to WebView. Specifically, we select eight well-known apps using WebView, and report 80 typical phishing sites (without evasion techniques) and 130 user-agent-specific phishing sites (accessible exclusively via each app's WebView). For scalable analysis, we develop an autonomous evaluation framework and investigate accessibility of both apps and Safe Browsing entities. As a result, we find that user-agent-specific (UA-specific) phishing sites successfully evade blocking across all of the eight Android apps. We also investigate accessing strategies of anti-phishing crawlers of both the apps and Safe Browsing entities; and find that only two apps' crawlers can access UA-specific phishing sites without any subsequent actions such as blocking the link. Based on our experiment results, we present security recommendations to take proactive phishing cautions using link preview bots. To the best of our knowledge, this is the first study that explores how the WebView environments handle phishing attacks and disclose their limitation in the real world.
The increasing demand for remote work and virtual interactions has heightened the usage of business collaboration platforms~(BCPs), with Google Workspace as a prominent example. These platforms enhance team collaboration by integrating Google Docs, Slides, Calendar, and feature-rich third-party applications (add-ons). However, such integration of multiple users and entities has inadvertently introduced new and complex attack surfaces, elevating security and privacy risks in resource management to unprecedented levels. In this study, we conduct a systematic study on the effectiveness of the cross-entity resource management in Google Workspace, the most popular BCP. Our study unveils the access control enforcement in real-world BCPs for the first time. Based on this, we formulate the attack surfaces inherent in BCPs and conduct a comprehensive assessment, pinpointing three vulnerability types leading to distinct attacks. An analysis of 4,732 marketplace add-ons reveals that approximately 70% are potentially vulnerable to these attacks. We propose robust countermeasures to improve BCP security, urging immediate action and setting a foundation for future research.
Domain fronting is a network communication technique that involves leveraging (or abusing) content delivery networks (CDNs) to disguise the final destination of network packets by presenting them as if they were intended for a different domain than their actual endpoint. This technique can be used for both benign and malicious purposes, such as circumventing censorship or hiding malware-related communications from network security systems. Since domain fronting has been known for a few years, some popular CDN providers have implemented traffic filtering approaches to curb its use at their CDN infrastructure. However, it remains unclear to what extent domain fronting has been mitigated. To better understand whether domain fronting can still be effectively used, we propose a systematic approach to discover CDNs that are still prone to domain fronting. To this end, we leverage passive and active DNS traffic analysis to pinpoint domain names served by CDNs and build an automated tool that can be used to discover CDNs that allow domain fronting in their infrastructure. Our results reveal that domain fronting is feasible in 22 out of 30 CDNs that we tested, including some major CDN providers like Akamai and Fastly. This indicates that domain fronting remains widely available and can be easily abused for malicious purposes.
Upgradeable smart contracts (USCs) have been widely adopted to enable modifying deployed smart contracts. While USCs bring great flexibility to developers, improper usage might introduce new security issues, potentially allowing attackers to hijack USCs and their users. In this paper, we conduct a large-scale measurement study to characterize USCs and their security implications in the wild. We summarize six commonly used USC patterns and develop a tool, USCDetector, to identify USCs without needing source code. Particularly, USCDetector collects various information such as bytecode and transaction information to construct upgrade chains for USCs and disclose potentially vulnerable ones. We evaluate USCDetector using verified smart contracts (i.e., with source code) as ground truth and show that USCDetector can achieve high accuracy with a precision of 96.26%. We then use USCDetector to conduct a large-scale study on Ethereum, covering a total of 60,251,064 smart contracts. USCDetecor constructs 10,218 upgrade chains and discloses multiple real-world USCs with potential security issues.
The web ecosystem is a fast-paced environment. In this dynamic landscape, new security features are offered one after another to enhance the security and robustness of web applications and the operations they handle. This paper focuses on a fragile but still in-use security feature, text-based CAPTCHAs, that had been wildly used by web applications in the past to protect against automated attacks such as credential stuffing and account hijacking. The paper first investigates what it takes to develop automated scanners that can solve previously unseen text-based CAPTCHAs. We evaluated the possibility of developing and integrating a pre-trained CAPTCHA solver in the automated web scanning process without using a significantly large training dataset. We also perform an analysis of the impact of such autonomous scanners on CAPTCHA-enabled websites. Our analysis shows that solvable text-based CAPTCHAs on login, contact, and comment pages of websites are not uncommon. In particular, we identified over 3,100 text-based CAPTCHA websites in critical sectors such as finance, government, and health with hundreds of thousands of users. We showed that a web scanner with a pre-trained solver could solve more than 20% of previously unseen CAPTCHAs in just one single attempt. This result is worrisome considering the substantial potential to autonomously run the operation across thousands of websites on a daily basis with minimal training. The findings suggest that the integration of autonomous scanning with pre-training and local optimization of models can significantly increase adversaries' asymmetric power to launch their attacks cheaper and faster.
Prototype-based languages like JavaScript are susceptible to prototype pollution vulnerabilities, enabling an attacker to inject arbitrary properties into an object's prototype. The attacker can subsequently capitalize on the injected properties by executing otherwise benign pieces of code, so-called gadgets, that perform security-sensitive operations. The success of an attack largely depends on the presence of gadgets, leading to high-profile exploits such as privilege escalation and arbitrary code execution (ACE). This paper proposes Dasty, the first semi-automated pipeline to help developers identify gadgets in their applications' software supply chain. Dasty targets server-side Node.js applications and relies on an enhancement of dynamic taint analysis which we implement with the dynamic AST-level instrumentation. Moreover, Dasty provides support for visualization of code flows with an IDE, thus facilitating the subsequent manual analysis for building proof-of-concept exploits. To illustrate the danger of gadgets, we use Dasty in a study of the most dependent-upon NPM packages to analyze the presence of gadgets leading to ACE. Dasty identifies 1,269 server-side packages, of which 631 have code flows that may reach dangerous sinks. We manually prioritize and verify the candidate flows to build proof-of-concept exploits for 49 NPM packages, including popular packages such as ejs, nodemailer and workerpool. To investigate how Dasty integrates with existing tools to find end-to-end exploits, we conduct an in-depth analysis of a popular data visualization dashboard to find one high-severity CVE-2023-31415 leading to remote code execution.
Phishing attacks have persistently remained a prevalent and widespread cybersecurity threat for several years. This leads to numerous endeavors aimed at comprehensively understanding the phishing attack ecosystem, with a specific focus on presenting new attack tactics and defense mechanisms against phishing attacks. Unfortunately, little is known about how client-side resources (e.g., JavaScript libraries) are used in phishing websites, compared to those in their corresponding legitimate target brand websites. This understanding can help us gain insights into the construction and techniques of phishing websites and phishing attackers' behaviors when building phishing websites. In this paper, we gain a deeper understanding of how client-side resources (especially, JavaScript libraries) are used in phishing websites by comparing them with the resources used in the legitimate target websites. For our study, we collect both client-side resources from phishing websites and their corresponding legitimate target brand websites for 25 months: 3.4M phishing websites (1.1M distinct phishing domains). Our study reveals that phishing websites tend to employ more diverse JavaScript libraries than their legitimate websites do. However, these libraries in phishing websites are older (nearly 21.2 months) and distinct in comparison. For example, Socket.IO is uniquely used in phishing websites to send victims' information to an external server in real time. Furthermore, we find that a considerable portion of them still maintain a basic and simplistic structure (e.g., simply displaying a login form or image), while phishing websites have significantly evolved to bypass anti-phishing measures. Finally, through HTML structure and style similarities, we can identify specific target webpages of legitimate brands that phishing attackers reference and use to mimic for their phishing attacks.
Detecting recurring vulnerabilities has become a popular means of static vulnerability detection in recent years because they do not require labor-intensive vulnerability modeling. Recently, a body of work, with HiddenCPG as a representative, has redefined the problem of statically identifying recurring vulnerabilities as the subgraph isomorphism problem. More specifically, these approaches represent known vulnerable code as graph-based structures (e.g., PDG or CPG), and then identify subgraphs within target applications that match the vulnerable graphs. However, since these methods are highly sensitive to changes in the code graph, they may miss a significant number of recurring vulnerabilities with slight code differences from known vulnerabilities. In this paper, we propose a novel approach, namely RecurScan, which can accurately detect recurring vulnerabilities with resilience to code differences. To achieve this goal, RecurScan works around security patches and symbolic tracking techniques, detecting recurring vulnerabilities by comparing symbolic expressions and selective constraints between the target applications and known vulnerabilities. Benefiting from this design, RecurScan can tolerate the code differences arising from complex data or control flows within the applications. We evaluated RecurScan on 200 popular PHP web applications using 184 known vulnerability patches. The results demonstrate that RecurScan discovered 232 previously unknown vulnerabilities, 174 of which were assigned CVE identifiers, outperforming state-of-the-art approach (i.e., HiddenCPG) by 25.98% in precision and 87.09% in recall.
Browser fingerprinting is often associated with cross-site user tracking, a practice that many browsers (e.g., Safari, Brave, Edge, Firefox, and Chrome) want to block. However, less is publicly known about its uses to enhance online safety, where it can provide an additional security layer against service abuses (e.g., in combination with CAPTCHAs) or during user authentication. To the best of our knowledge, no fingerprinting defenses deployed thus far consider this important distinction when blocking fingerprinting attempts, so they might negatively affect website functionality and security. To address this issue we make three main contributions. First, we introduce a novel machine learning-based method to automatically identify authentication pages (i.e. login and sign-up pages). Our supervised algorithm achieves 96-98% precision and recall on a manually-labelled dataset of almost 1,000 popular sites. Second, we compare our algorithm with methods from prior works on the same dataset, showing that it significantly outperforms all of them. Third, we quantify the prevalence of fingerprinting scripts across login and sign-up pages (10.2%) versus those executed on other pages (9.2%); while the rates of fingerprinting are similar, home pages and authentication pages differ in the third-party scripts they include and how often these scripts are labeled as tracking. We also highlight the substantial differences in fingerprinting on login and sign-up pages. Our work sheds light on the complicated reality that fingerprinting is used to both protect user security and invade user privacy; this dual nature must be considered by fingerprinting mitigations.
Malicious traffic detection has been a focal point in the field of network security, and deep learning-based approaches are emerging as a new paradigm. However, most of them are supervised methods, which highly depend on well-labeled data, and fail to handle unknown or continuously evolving attacks. Unsupervised methods alleviate the need for labeled data, but existing methods are often limited to detecting anomalies either in vertical perspective through historical comparisons or in horizontal perspective by comparing with concurrent entities. Relying on data from a single perspective is unreliable, and it limits the model's accuracy and generalizability. In this paper, we propose a novel method ContraMTD based on contrastive learning, which comprehensively considers both vertical and horizontal perspectives. ContraMTD extracts local behavior features and global interaction features from normal network traffic by proposed SEC and DE-GAT respectively, then employs contrastive learning to learn the relationship, especially consistency between them, and finally detects malicious traffic through a multi-round scoring approach. We conduct extensive experiments on three datasets, including a self-collected dataset, and the results demonstrate that our method outperforms many state-of-the-art methods in the domain of unsupervised malicious traffic detection.
Serverless computing is supplanting past versions of cloud computing as the easiest way to rapidly prototype and deploy applications. However, the reentrant and ephemeral nature of serverless functions only exacerbates the challenge of correctly specifying security policies. Unfortunately, with role-based access control solutions like Amazon Identity and Access Management (IAM) already suffering from pervasive misconfiguration problems, the likelihood of policy failures in serverless applications is high. In this work, we introduce GRASP, a graph-based analysis framework for modeling serverless access control policies as queryable reachability graphs. GRASP generates reusable models that represent the principals of a serverless application and the interactions between those principals. We implement GRASP for Amazon IAM in Prolog, then deploy it on a corpus of 731 open source Amazon Lambda applications. We find that serverless policies tend to be short and highly permissive, e.g., 92% of surveyed policies are comprised of just 10 statements and 30% exhibit full reachability between all application functions and resources. We then use GRASP to identify potential attack vectors permitted by these policies, including hundreds of sensitive access channels, a dozen publicly-exposed resources, and four channels that may permit an attacker to exfiltrate an application's private resources through one of its public resources. These findings demonstrate GRASP's utility as a means of identifying opportunities for hardening application policies and highlighting potential exfiltration channels.
Permissionless blockchains promise resilience against censorship by a single entity. This suggests that deterministic rules, not third-party actors, decide whether a transaction is appended to the blockchain. In 2022, the U.S. ØFAC sanctioned a Bitcoin mixer and an Ethereum application, challenging the neutrality of permissionless blockchains. In this paper, we formalize, quantify, and analyze the security impact of blockchain censorship. We start by defining censorship, followed by a quantitative assessment of current censorship practices. We find that 46% of Ethereum blocks were made by censoring actors complying with OFAC sanctions, indicating the significant impact of OFAC sanctions on the neutrality of public blockchains. We discover that censorship affects not only neutrality but also security. After Ethereum's transition to ¶oS, censored transactions faced an average delay of 85%, compromising their security and strengthening sandwich adversaries.