Cryptography and Security (cs.CR)

Abstract: Rust is an emerging programming language designed for the development of systems software. To facilitate the reuse of Rust code, crates.io, as a central package registry of the Rust ecosystem, hosts thousands of third-party Rust packages. The openness of crates.io enables the growth of the Rust ecosystem but comes with security risks by severe security advisories. Although Rust guarantees a software program to be safe via programming language features and strict compile-time checking, the unsafe keyword in Rust allows developers to bypass compiler safety checks for certain regions of code. Prior studies empirically investigate the memory safety and concurrency bugs in the Rust ecosystem, as well as the usage of unsafe keywords in practice. Nonetheless, the literature lacks a systematic investigation of the security risks in the Rust ecosystem. In this paper, we perform a comprehensive investigation into the security risks present in the Rust ecosystem, asking ``what are the characteristics of the vulnerabilities, what are the characteristics of the vulnerable packages, and how are the vulnerabilities fixed in practice?''. To facilitate the study, we first compile a dataset of 433 vulnerabilities, 300 vulnerable code repositories, and 218 vulnerability fix commits in the Rust ecosystem, spanning over 7 years. With the dataset, we characterize the types, life spans, and evolution of the disclosed vulnerabilities. We then characterize the popularity, categorization, and vulnerability density of the vulnerable Rust packages, as well as their versions and code regions affected by the disclosed vulnerabilities. Finally, we characterize the complexity of vulnerability fixes and localities of corresponding code changes, and inspect how practitioners fix vulnerabilities in Rust packages with various localities.

Abstract: We present new schemes for solving prefix authentication and secure relative timestamping. By casting a new light on antimonotone linking schemes, we improve upon the state of the art in prefix authentication, and in timestamping with rounds of bounded length. Our designs can serve as more efficient alternatives to certificate transparency logs.

Abstract: Modular reduction is a crucial operation in many post-quantum cryptographic schemes, including the Kyber key exchange method or Dilithium signature scheme. However, it can be computationally expensive and pose a performance bottleneck in hardware implementations. To address this issue, we propose a novel approach for computing modular reduction efficiently in hardware for arbitrary static moduli. Unlike other commonly used methods such as Barrett or Montgomery reduction, the method does not require any multiplications. It is not dependent on properties of any particular choice of modulus for good performance and low area consumption. Its major strength lies in its low area consumption, which was reduced by 60% for optimized and up to 90% for generic Barrett implementations for Kyber and Dilithium. Additionally, it is well suited for parallelization and pipelining and scales linearly in hardware resource consumption with increasing operation width. All operations can be performed in the bit-width of the modulus, rather than the size of the number being reduced. This shortens carry chains and allows for faster clocking. Moreover, our method can be executed in constant time, which is essential for cryptography applications where timing attacks can be used to obtain information about the secret key.

Abstract: Blockchain has become a popular decentralized paradigm for various applications in the zero-trust environment. The core of the blockchain is the consensus protocol, which establishes consensus among all the participants. PoW (Proof-of-Work) is one of the most popular consensus protocols. However, the PoW consensus protocol which incentives the participants to use their computing power to solve a meaningless hash puzzle is continuously questioned as energy-wasting. To address these issues, we propose an efficient and secure consensus protocol based on proof of useful federated learning for blockchain (called FedChain). We first propose a secure and robust blockchain architecture that takes federated learning tasks as proof of work. Then a pool aggregation mechanism is integrated to improve the efficiency of the FedChain architecture. To protect model parameter privacy for each participant within a mining pool, a secret sharing-based ring-all reduce architecture is designed. We also introduce a data distribution-based federated learning model optimization algorithm to improve the model performance of FedChain. At last, a zero-knowledge proof-based federated learning model verification is introduced to preserve the privacy of federated learning participants while proving the model performance of federated learning participants. Our approach has been tested and validated through extensive experiments, demonstrating its performance.

Abstract: The Automatic Dependant Surveillance-Broadcast (ADS-B) message scheme was designed without any authentication or encryption of messages in place. It is therefore easily possible to attack it, e.g., by injecting spoofed messages or modifying the transmitted Global Navigation Satellite System (GNSS) coordinates. In order to verify the integrity of the received information, various methods have been suggested, such as multilateration, the use of Kalman filters, group certification, and many others. However, solutions based on modifications of the standard may be difficult and too slow to be implemented due to legal and regulatory issues. A vantage far less explored is the location verification using public sensor data. In this paper, we propose LoVe, a lightweight message verification approach that uses a geospatial indexing scheme to evaluate the trustworthiness of publicly deployed sensors and the ADS-B messages they receive. With LoVe, new messages can be evaluated with respect to the plausibility of their reported coordinates in a location privacy-preserving manner, while using a data-driven and lightweight approach. By testing our approach on two open datasets, we show that LoVe achieves very low false positive rates (between 0 and 0.00106) and very low false negative rates (between 0.00065 and 0.00334) while providing a real-time compatible approach that scales well even with a large sensor set. Compared to currently existing approaches, LoVe neither requires a large number of sensors, nor for messages to be recorded by as many sensors as possible simultaneously in order to verify location claims. Furthermore, it can be directly applied to currently deployed systems thus being backward compatible.

Abstract: Blockchains are decentralized and immutable databases that are shared among the nodes of the network. Although blockchains have attracted a great scale of attention in the recent years by disrupting the traditional financial systems, the transaction privacy is still a challenging issue that needs to be addressed and analysed. We propose a Private Token Transfer System (PTTS) for the Ethereum public blockchain in the first part of this paper. For the proposed framework, zero-knowledge based protocol has been designed using Zokrates and integrated into our private token smart contract. With the help of web user interface designed, the end users can interact with the smart contract without any third-party setup. In the second part of the paper, we provide security and privacy analysis including the replay attack and the balance range privacy attack which has been modelled as a network flow problem. It is shown that in case some balance ranges are deliberately leaked out to particular organizations or adversial entities, it is possible to extract meaningful information about the user balances by employing minimum cost flow network algorithms that have polynomial complexity. The experimental study reports the Ethereum gas consumption and proof generation times for the proposed framework. It also reports network solution times and goodness rates for a subset of addresses under the balance range privacy attack with respect to number of addresses, number of transactions and ratio of leaked transfer transaction amounts.

Abstract: Risk-based authentication (RBA) aims to protect end-users against attacks involving stolen or otherwise guessed passwords without requiring a second authentication method all the time. Online services typically set limits on what is still seen as normal and what is not, as well as the actions taken afterward. Consequently, RBA monitors different features, such as geolocation and device during login. If the features' values differ from the expected values, then a second authentication method might be requested. However, only a few online services publish information about how their systems work. This hinders not only RBA research but also its development and adoption in organizations. In order to understand how the RBA systems online services operate, black box testing is applied. To verify the results, we re-evaluate the three large providers: Google, Amazon, and Facebook. Based on our test setup and the test cases, we notice differences in RBA based on account creation at Google. Additionally, several test cases rarely trigger the RBA system. Our results provide new insights into RBA systems and raise several questions for future work.

Abstract: Data breaches resulting from targeted attacks against organizations, e.g., by advanced persistent threat groups, often involve social engineering (SE) as the initial attack vector before malicious software is used, e.g., for persistence, lateral movement, and data exfiltration. While technical security controls, such as the automated detection of phishing emails, can contribute to mitigating SE risks, raising awareness for SE attacks through education and motivation of personnel is an important building block to increasing an organization's resilience. To facilitate hands-on SE awareness training as one component of broader SE awareness campaigns, we created a SE tabletop game called Tabletop As Social Engineering Prevention (TASEP) in two editions for (a) small and medium enterprises and (b) large corporations, respectively. Its game design is inspired by Dungeons & Dragons role-playing games and facilitates LEGO models of the in-game target organizations. Participants switch roles by playing a group of SE penetration testers and conducting a security audit guided by the game master. We evaluated the created game with different student groups, achieving highly immersive and flexible training, resulting in an entertaining way of learning about SE and raising awareness.

Abstract: The General Data Protection Regulation (GDPR) was implemented in 2018 to strengthen and harmonize the data protection of individuals within the European Union. One key aspect is Article 15, which gives individuals the right to access their personal data in an understandable format. Organizations offering services to Europeans had five years' time to optimize their processes and functions to comply with Article 15. This study aims to explore the process of submitting and receiving the responses of organizations to GDPR Article 15 requests. A quantitative analysis obtains data from various websites to understand the level of conformity, the data received, and the challenges faced by individuals who request their data. The study differentiates organizations operating worldwide and in Germany, browser website- and app-based usage, and different types of websites. Thereby, we conclude that some websites still compile the data manually, resulting in longer waiting times. A few exceptions did not respond with any data or deliver machine-readable data (GDRP Article 20). The findings of the study additionally reveal ten patterns individuals face when requesting and accessing their data.

Abstract: Phishing is a major cyber threat to organizations that can cause financial and reputational damage, threatening their existence. The technical measures against phishing should be complemented by awareness training for employees. However, there is little validation of awareness measures. Consequently, organizations have an additional burden when integrating awareness training, as there is no consensus on which method brings the best success. This paper examines how awareness concepts can be successfully implemented and validated. For this purpose, various factors, such as requirements and possible combinations of methods, are taken into account in our case study at a small- and medium-sized enterprise (SME). To measure success, phishing exercises are conducted. The study suggests that pleasant campaigns result in better performance in the simulated phishing exercise. In addition, significant improvements and differences in the target groups could be observed. The implementation of awareness training with integrated key performance indicators can be used as a basis for other organizations.

Abstract: This report describes the state of the art in verifiable computation. The problem being solved is the following: The Verifiable Computation Problem (Verifiable Computing Problem) Suppose we have two computing agents. The first agent is the verifier, and the second agent is the prover. The verifier wants the prover to perform a computation. The verifier sends a description of the computation to the prover. Once the prover has completed the task, the prover returns the output to the verifier. The output will contain proof. The verifier can use this proof to check if the prover computed the output correctly. The check is not required to verify the algorithm used in the computation. Instead, it is a check that the prover computed the output using the computation specified by the verifier. The effort required for the check should be much less than that required to perform the computation. This state-of-the-art report surveys 128 papers from the literature comprising more than 4,000 pages. Other papers and books were surveyed but were omitted. The papers surveyed were overwhelmingly mathematical. We have summarised the major concepts that form the foundations for verifiable computation. The report contains two main sections. The first, larger section covers the theoretical foundations for probabilistically checkable and zero-knowledge proofs. The second section contains a description of the current practice in verifiable computation. Two further reports will cover (i) military applications of verifiable computation and (ii) a collection of technical demonstrators. The first of these is intended to be read by those who want to know what applications are enabled by the current state of the art in verifiable computation. The second is for those who want to see practical tools and conduct experiments themselves.

Abstract: Cyclostationarity involves periodic statistical variations in signals and processes, commonly used in signal analysis and network security. In the context of attacks, cyclostationarity helps detect malicious behaviors within network traffic, such as traffic patterns in Distributed Denial of Service (DDoS) attacks or hidden communication channels in malware. This approach enhances security by identifying abnormal patterns and informing Network Intrusion Detection Systems (NIDSs) to recognize potential attacks, enhancing protection against both known and novel threats. This research focuses on identifying cyclostationary malware behavior and its detection. The main goal is to pinpoint essential cyclostationary features used in NIDSs. These features are extracted using algorithms such as Boruta and Principal Component Analysis (PCA), and then categorized to find the most significant cyclostationary patterns. The aim of this article is to reveal periodically changing malware behaviors through cyclostationarity. The study highlights the importance of spotting cyclostationary malware in NIDSs by using established datasets like KDD99, NSL-KDD, and the UGRansome dataset. The UGRansome dataset is designed for anomaly detection research and includes both normal and abnormal network threat categories of zero-day attacks. A comparison is made using the Random Forest (RF) and Support Vector Machine (SVM) algorithms, while also evaluating the effectiveness of Boruta and PCA. The findings show that PCA is more promising than using Boruta alone for extracting cyclostationary network feature patterns. Additionally, the analysis identifies the internet protocol as the most noticeable cyclostationary feature pattern used by malware. Notably, the UGRansome dataset outperforms the KDD99 and NSL-KDD, achieving 99% accuracy in signature malware detection using the RF algorithm and 98% with the SVM.

Abstract: The Common Vulnerability Scoring System (CVSS) is a popular method for evaluating the severity of vulnerabilities in vulnerability management. In the evaluation process, a numeric score between 0 and 10 is calculated, 10 being the most severe (critical) value. The goal of CVSS is to provide comparable scores across different evaluators. However, previous works indicate that CVSS might not reach this goal: If a vulnerability is evaluated by several analysts, their scores often differ. This raises the following questions: Are CVSS evaluations consistent? Which factors influence CVSS assessments? We systematically investigate these questions in an online survey with 196 CVSS users. We show that specific CVSS metrics are inconsistently evaluated for widespread vulnerability types, including Top 3 vulnerabilities from the ''2022 CWE Top 25 Most Dangerous Software Weaknesses'' list. In a follow-up survey with 59 participants, we found that for the same vulnerabilities from the main study, 68% of these users gave different severity ratings. Our study reveals that most evaluators are aware of the problematic aspects of CVSS, but they still see CVSS as a useful tool for vulnerability assessment. Finally, we discuss possible reasons for inconsistent evaluations and provide recommendations on improving the consistency of scoring.

Abstract: The privacy-preserving data aggregation is a critical problem for many applications where multiple parties need to collaborate with each other privately to arrive at certain results. Blockchain, as a database shared across the network, provides an underlying platform on which such aggregations can be carried out with a decentralized manner. Therefore, in this paper, we have proposed a scalable privacy-preserving data aggregation protocol for summation on the Ethereum blockchain by integrating several cryptographic primitives including commitment scheme, asymmetric encryption and zero-knowledge proof along with the hypercube network topology. The protocol consists of four stages as contract deployment, user registration, private submission and proof verification. The analysis of the protocol is made with respect to two main perspectives as security and scalability including computational, communicational and storage overheads. In the paper, the zero-knowledge proof, smart contract and web user interface models for the protocol are provided. We have performed an experimental study in order to identify the required gas costs per individual and per system. The general formulation is provided to characterize the changes in gas costs for the increasing number of users. The zero-knowledge proof generation and verification times are also measured.

Abstract: We study an adversary who attacks a Proof-of-Work (POW) blockchain by selfishly constructing an alternative longest chain. We characterize optimal strategies employed by the adversary when a difficulty adjustment rule al\`a Bitcoin applies. As time (namely the times-tamp specified in each block) in most permissionless POW blockchains is somewhat subjective, we focus on two extreme scenarios: when time is completely verifiable, and when it is completely unverifiable. We conclude that an adversary who faces a difficulty adjustment rule will find a longest-chain attack very challenging when timestamps are verifiable. POW blockchains with frequent difficulty adjustments relative to time reporting flexibility will be substantially more vulnerable to longest-chain attacks. Our main fining provides guidance on the design of difficulty adjustment rules and demonstrates the importance of timestamp verifiability.

Abstract: Blockchains offer strong security gurarantees, but cannot protect users against the ordering of transactions. Players such as miners, bots and validators can reorder various transactions and reap significant profits, called the Maximal Extractable Value (MEV). In this paper, we propose an MEV aware protocol design called Masquerade, and show that it will increase user satisfaction and confidence in the system. We propose a strict per-transaction level of ordering to ensure that a transaction is committed either way even if it is revealed. In this protocol, we introduce the notion of a "token" to mitigate the actions taken by an adversary in an attack scenario. Such tokens can be purchased voluntarily by users, who can then choose to include the token numbers in their transactions. If the users include the token in their transactions, then our protocol requires the block-builder to order the transactions strictly according to token numbers. We show through extensive simulations that this reduces the probability that the adversaries can benefit from MEV transactions as compared to existing current practices.