Cryptography and Security
New submissions
[ showing up to 2000 entries per page: fewer | more ]
New submissions for Thu, 9 May 24
- [1] arXiv:2405.04551 [pdf, other]
-
Title: Differentially Private Federated Learning without Noise Addition: When is it Possible?Authors: Jiang Zhang, Yahya H Ezzeldin, Ahmed Roushdy Elkordy, Konstantinos Psounis, Salman AvestimehrSubjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
Federated Learning (FL) with Secure Aggregation (SA) has gained significant attention as a privacy preserving framework for training machine learning models while preventing the server from learning information about users' data from their individual encrypted model updates. Recent research has extended privacy guarantees of FL with SA by bounding the information leakage through the aggregate model over multiple training rounds thanks to leveraging the "noise" from other users' updates. However, the privacy metric used in that work (mutual information) measures the on-average privacy leakage, without providing any privacy guarantees for worse-case scenarios. To address this, in this work we study the conditions under which FL with SA can provide worst-case differential privacy guarantees. Specifically, we formally identify the necessary condition that SA can provide DP without addition noise. We then prove that when the randomness inside the aggregated model update is Gaussian with non-singular covariance matrix, SA can provide differential privacy guarantees with the level of privacy $\epsilon$ bounded by the reciprocal of the minimum eigenvalue of the covariance matrix. However, we further demonstrate that in practice, these conditions are almost unlikely to hold and hence additional noise added in model updates is still required in order for SA in FL to achieve DP. Lastly, we discuss the potential solution of leveraging inherent randomness inside aggregated model update to reduce the amount of addition noise required for DP guarantee.
- [2] arXiv:2405.04554 [pdf, other]
-
Title: Differentially Private Synthetic Data with Private Density EstimationComments: Accepted to ISIT 2024Subjects: Cryptography and Security (cs.CR); Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST); Machine Learning (stat.ML)
The need to analyze sensitive data, such as medical records or financial data, has created a critical research challenge in recent years. In this paper, we adopt the framework of differential privacy, and explore mechanisms for generating an entire dataset which accurately captures characteristics of the original data. We build upon the work of Boedihardjo et al, which laid the foundations for a new optimization-based algorithm for generating private synthetic data. Importantly, we adapt their algorithm by replacing a uniform sampling step with a private distribution estimator; this allows us to obtain better computational guarantees for discrete distributions, and develop a novel algorithm suitable for continuous distributions. We also explore applications of our work to several statistical tasks.
- [3] arXiv:2405.04561 [pdf, other]
-
Title: Inferring Discussion Topics about Exploitation of Vulnerabilities from Underground Hacking ForumsAuthors: Felipe Moreno-VeraComments: 6 pagesJournal-ref: 2023 14th International Conference on Information and Communication Technology Convergence (ICTC)Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
The increasing sophistication of cyber threats necessitates proactive measures to identify vulnerabilities and potential exploits. Underground hacking forums serve as breeding grounds for the exchange of hacking techniques and discussions related to exploitation. In this research, we propose an innovative approach using topic modeling to analyze and uncover key themes in vulnerabilities discussed within these forums. The objective of our study is to develop a machine learning-based model that can automatically detect and classify vulnerability-related discussions in underground hacking forums. By monitoring and analyzing the content of these forums, we aim to identify emerging vulnerabilities, exploit techniques, and potential threat actors. To achieve this, we collect a large-scale dataset consisting of posts and threads from multiple underground forums. We preprocess and clean the data to ensure accuracy and reliability. Leveraging topic modeling techniques, specifically Latent Dirichlet Allocation (LDA), we uncover latent topics and their associated keywords within the dataset. This enables us to identify recurring themes and prevalent discussions related to vulnerabilities, exploits, and potential targets.
- [4] arXiv:2405.04691 [pdf, other]
-
Title: Carbon Filter: Real-time Alert Triage Using Large Scale Clustering and Fast SearchSubjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
"Alert fatigue" is one of the biggest challenges faced by the Security Operations Center (SOC) today, with analysts spending more than half of their time reviewing false alerts. Endpoint detection products raise alerts by pattern matching on event telemetry against behavioral rules that describe potentially malicious behavior, but can suffer from high false positives that distract from actual attacks. While alert triage techniques based on data provenance may show promise, these techniques can take over a minute to inspect a single alert, while EDR customers may face tens of millions of alerts per day; the current reality is that these approaches aren't nearly scalable enough for production environments.
We present Carbon Filter, a statistical learning based system that dramatically reduces the number of alerts analysts need to manually review. Our approach is based on the observation that false alert triggers can be efficiently identified and separated from suspicious behaviors by examining the process initiation context (e.g., the command line) that launched the responsible process. Through the use of fast-search algorithms for training and inference, our approach scales to millions of alerts per day. Through batching queries to the model, we observe a theoretical maximum throughput of 20 million alerts per hour. Based on the analysis of tens of million alerts from customer deployments, our solution resulted in a 6-fold improvement in the Signal-to-Noise ratio without compromising on alert triage performance. - [5] arXiv:2405.04735 [pdf, other]
-
Title: Cryptanalysis of the SIMON Cypher Using Neo4jComments: 10 pages, 10 figures, 2 algorithms, accepted by the 4th International Conference on Electrical, Computer and Energy Technologies (ICECET) to be presented in July 2024Subjects: Cryptography and Security (cs.CR); Data Structures and Algorithms (cs.DS); Information Retrieval (cs.IR)
The exponential growth in the number of Internet of Things (IoT) devices has seen the introduction of several Lightweight Encryption Algorithms (LEA). While LEAs are designed to enhance the integrity, privacy and security of data collected and transmitted by IoT devices, it is hazardous to assume that all LEAs are secure and exhibit similar levels of protection. To improve encryption strength, cryptanalysts and algorithm designers routinely probe LEAs using various cryptanalysis techniques to identify vulnerabilities and limitations of LEAs. Despite recent improvements in the efficiency of cryptanalysis utilising heuristic methods and a Partial Difference Distribution Table (PDDT), the process remains inefficient, with the random nature of the heuristic inhibiting reproducible results. However, the use of a PDDT presents opportunities to identify relationships between differentials utilising knowledge graphs, leading to the identification of efficient paths throughout the PDDT. This paper introduces the novel use of knowledge graphs to identify intricate relationships between differentials in the SIMON LEA, allowing for the identification of optimal paths throughout the differentials, and increasing the effectiveness of the differential security analyses of SIMON.
- [6] arXiv:2405.04753 [pdf, other]
-
Title: AttacKG+:Boosting Attack Knowledge Graph Construction with Large Language ModelsAuthors: Yongheng Zhang, Tingwen Du, Yunshan Ma, Xiang Wang, Yi Xie, Guozheng Yang, Yuliang Lu, Ee-Chien ChangComments: 20 pages, 5 figuresSubjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
Attack knowledge graph construction seeks to convert textual cyber threat intelligence (CTI) reports into structured representations, portraying the evolutionary traces of cyber attacks. Even though previous research has proposed various methods to construct attack knowledge graphs, they generally suffer from limited generalization capability to diverse knowledge types as well as requirement of expertise in model design and tuning. Addressing these limitations, we seek to utilize Large Language Models (LLMs), which have achieved enormous success in a broad range of tasks given exceptional capabilities in both language understanding and zero-shot task fulfillment. Thus, we propose a fully automatic LLM-based framework to construct attack knowledge graphs named: AttacKG+. Our framework consists of four consecutive modules: rewriter, parser, identifier, and summarizer, each of which is implemented by instruction prompting and in-context learning empowered by LLMs. Furthermore, we upgrade the existing attack knowledge schema and propose a comprehensive version. We represent a cyber attack as a temporally unfolding event, each temporal step of which encapsulates three layers of representation, including behavior graph, MITRE TTP labels, and state summary. Extensive evaluation demonstrates that: 1) our formulation seamlessly satisfies the information needs in threat event analysis, 2) our construction framework is effective in faithfully and accurately extracting the information defined by AttacKG+, and 3) our attack graph directly benefits downstream security practices such as attack reconstruction. All the code and datasets will be released upon acceptance.
- [7] arXiv:2405.04758 [pdf, other]
-
Title: Honeyfile Camouflage: Hiding Fake Files in Plain SightComments: 3rd Workshop on the security implications of Deepfakes and Cheapfakes (WDC) co-located at ACM ASIACCS 2024Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Honeyfiles are a particularly useful type of honeypot: fake files deployed to detect and infer information from malicious behaviour. This paper considers the challenge of naming honeyfiles so they are camouflaged when placed amongst real files in a file system. Based on cosine distances in semantic vector spaces, we develop two metrics for filename camouflage: one based on simple averaging and one on clustering with mixture fitting. We evaluate and compare the metrics, showing that both perform well on a publicly available GitHub software repository dataset.
- [8] arXiv:2405.04760 [pdf, other]
-
Title: Large Language Models for Cyber Security: A Systematic Literature ReviewAuthors: HanXiang Xu, ShenAo Wang, Ningke Li, Yanjie Zhao, Kai Chen, Kailong Wang, Yang Liu, Ting Yu, HaoYu WangComments: 46 pages,6 figuresSubjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in various domains, including cybersecurity. As the volume and sophistication of cyber threats continue to grow, there is an increasing need for intelligent systems that can automatically detect vulnerabilities, analyze malware, and respond to attacks. In this survey, we conduct a comprehensive review of the literature on the application of LLMs in cybersecurity (LLM4Security). By comprehensively collecting over 30K relevant papers and systematically analyzing 127 papers from top security and software engineering venues, we aim to provide a holistic view of how LLMs are being used to solve diverse problems across the cybersecurity domain. Through our analysis, we identify several key findings. First, we observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection. Second, we find that the datasets used for training and evaluating LLMs in these tasks are often limited in size and diversity, highlighting the need for more comprehensive and representative datasets. Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training. Finally, we discuss the main challenges and opportunities for future research in LLM4Security, including the need for more interpretable and explainable models, the importance of addressing data privacy and security concerns, and the potential for leveraging LLMs for proactive defense and threat hunting. Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.
- [9] arXiv:2405.04803 [pdf, other]
-
Title: Blockchains for Internet of Things: Fundamentals, Applications, and ChallengesComments: IEEE NetworkSubjects: Cryptography and Security (cs.CR); Networking and Internet Architecture (cs.NI)
Internet of Things (IoT) services necessitate the storage, transmission, and analysis of diverse data for inference, autonomy, and control. Blockchains, with their inherent properties of decentralization and security, offer efficient database solutions for these devices through consensus-based data sharing. However, it's essential to recognize that not every blockchain system is suitable for specific IoT applications, and some might be more beneficial when excluded with privacy concerns. For example, public blockchains are not suitable for storing sensitive data. This paper presents a detailed review of three distinct blockchains tailored for enhancing IoT applications. We initially delve into the foundational aspects of three blockchain systems, highlighting their strengths, limitations, and implementation needs. Additionally, we discuss the security issues in different blockchains. Subsequently, we explore the blockchain's application in three pivotal IoT areas: edge AI, communications, and healthcare. We underscore potential challenges and the future directions for integrating different blockchains in IoT. Ultimately, this paper aims to offer a comprehensive perspective on the synergies between blockchains and the IoT ecosystem, highlighting the opportunities and complexities involved.
- [10] arXiv:2405.04824 [pdf, ps, other]
-
Title: Quantum-Edge Cloud Computing: A Future Paradigm for IoT ApplicationsAuthors: Mohammad Ikbal Hossain, Shaharier Arafat Sumon, Habib Md. Hasan, Fatema Akter, Md Bahauddin Badhon, Mohammad Nahid Ul IslamSubjects: Cryptography and Security (cs.CR)
The Internet of Things (IoT) is expanding rapidly, which has created a need for sophisticated computational frameworks that can handle the data and security requirements inherent in modern IoT applications. However, traditional cloud computing frameworks have struggled with latency, scalability, and security vulnerabilities. Quantum-Edge Cloud Computing (QECC) is a new paradigm that effectively addresses these challenges by combining the computational power of quantum computing, the low-latency benefits of edge computing, and the scalable resources of cloud computing. This study has been conducted based on a published literature review, performance improvements, and metrics data from Bangladesh on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. We have discussed the integration of quantum cryptography to enhance data integrity, the role of edge computing in reducing response times, and how cloud computing's resource abundance can support large IoT networks. We examine case studies, such as the use of quantum sensors in self-driving vehicles, to illustrate the real-world impact of QECC. Furthermore, the paper identifies future research directions, including developing quantum-resistant encryption and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC promises to overcome the existing limitations of IoT frameworks and set a new standard for the future of IoT applications.
- [11] arXiv:2405.04825 [pdf, other]
-
Title: Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature AttributionSubjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Ownership verification is currently the most critical and widely adopted post-hoc method to safeguard model copyright. In general, model owners exploit it to identify whether a given suspicious third-party model is stolen from them by examining whether it has particular properties `inherited' from their released models. Currently, backdoor-based model watermarks are the primary and cutting-edge methods to implant such properties in the released models. However, backdoor-based methods have two fatal drawbacks, including harmfulness and ambiguity. The former indicates that they introduce maliciously controllable misclassification behaviors ($i.e.$, backdoor) to the watermarked released models. The latter denotes that malicious users can easily pass the verification by finding other misclassified samples, leading to ownership ambiguity.
In this paper, we argue that both limitations stem from the `zero-bit' nature of existing watermarking schemes, where they exploit the status ($i.e.$, misclassified) of predictions for verification. Motivated by this understanding, we design a new watermarking paradigm, $i.e.$, Explanation as a Watermark (EaaW), that implants verification behaviors into the explanation of feature attribution instead of model predictions. Specifically, EaaW embeds a `multi-bit' watermark into the feature attribution explanation of specific trigger samples without changing the original prediction. We correspondingly design the watermark embedding and extraction algorithms inspired by explainable artificial intelligence. In particular, our approach can be used for different tasks ($e.g.$, image classification and text generation). Extensive experiments verify the effectiveness and harmlessness of our EaaW and its resistance to potential attacks. - [12] arXiv:2405.04837 [pdf, ps, other]
-
Title: Enhancing Data Integrity and Traceability in Industry Cyber Physical Systems (ICPS) through Blockchain Technology: A Comprehensive ApproachSubjects: Cryptography and Security (cs.CR); Systems and Control (eess.SY)
Blockchain technology, heralded as a transformative innovation, has far-reaching implications beyond its initial application in cryptocurrencies. This study explores the potential of blockchain in enhancing data integrity and traceability within Industry Cyber-Physical Systems (ICPS), a crucial aspect in the era of Industry 4.0. ICPS, integrating computational and physical components, is pivotal in managing critical infrastructure like manufacturing, power grids, and transportation networks. However, they face challenges in security, privacy, and reliability. With its inherent immutability, transparency, and distributed consensus, blockchain presents a groundbreaking approach to address these challenges. It ensures robust data reliability and traceability across ICPS, enhancing transaction transparency and facilitating secure data sharing. This research unearths various blockchain applications in ICPS, including supply chain management, quality control, contract management, and data sharing. Each application demonstrates blockchain's capacity to streamline processes, reduce fraud, and enhance system efficiency. In supply chain management, blockchain provides real-time auditing and compliance. For quality control, it establishes tamper-proof records, boosting consumer confidence. In contract management, smart contracts automate execution, enhancing efficiency. Blockchain also fosters secure collaboration in ICPS, which is crucial for system stability and safety. This study emphasizes the need for further research on blockchain's practical implementation in ICPS, focusing on challenges like scalability, system integration, and security vulnerabilities. It also suggests examining blockchain's economic and organizational impacts in ICPS to understand its feasibility and long-term advantages.
- [13] arXiv:2405.04866 [pdf, ps, other]
-
Title: Systematic review, analysis, and characterisation of malicious industrial network traffic datasets for aiding Machine Learning algorithm performance testingComments: 28 pages, preprint submitted to Engineering Applications of Artificial Intelligence (Elsevier)Subjects: Cryptography and Security (cs.CR)
The adoption of the Industrial Internet of Things (IIoT) as a complementary technology to Operational Technology (OT) has enabled a new level of standardised data access and process visibility. This convergence of Information Technology (IT), OT, and IIoT has also created new cybersecurity vulnerabilities and risks that must be managed. Artificial Intelligence (AI) is emerging as a powerful tool to monitor OT/IIoT networks for malicious activity and is a highly active area of research. AI researchers are applying advanced Machine Learning (ML) and Deep Learning (DL) techniques to the detection of anomalous or malicious activity in network traffic. They typically use datasets derived from IoT/IIoT/OT network traffic captures to measure the performance of their proposed approaches. Therefore, there is a widespread need for datasets for algorithm testing. This work systematically reviews publicly available network traffic capture-based datasets, including categorisation of contained attack types, review of metadata, and statistical as well as complexity analysis. Each dataset is analysed to provide researchers with metadata that can be used to select the best dataset for their research question. This results in an added benefit to the community as researchers can select the best dataset for their research more easily and according to their specific Machine Learning goals.
- [14] arXiv:2405.04874 [pdf, other]
-
Title: Critical Infrastructure Protection: Generative AI, Challenges, and OpportunitiesAuthors: Yagmur Yigit, Mohamed Amine Ferrag, Iqbal H. Sarker, Leandros A. Maglaras, Christos Chrysoulas, Naghmeh Moradpoor, Helge JanickeSubjects: Cryptography and Security (cs.CR)
Critical National Infrastructure (CNI) encompasses a nation's essential assets that are fundamental to the operation of society and the economy, ensuring the provision of vital utilities such as energy, water, transportation, and communication. Nevertheless, growing cybersecurity threats targeting these infrastructures can potentially interfere with operations and seriously risk national security and public safety. In this paper, we examine the intricate issues raised by cybersecurity risks to vital infrastructure, highlighting these systems' vulnerability to different types of cyberattacks. We analyse the significance of trust, privacy, and resilience for Critical Infrastructure Protection (CIP), examining the diverse standards and regulations to manage these domains. We also scrutinise the co-analysis of safety and security, offering innovative approaches for their integration and emphasising the interdependence between these fields. Furthermore, we introduce a comprehensive method for CIP leveraging Generative AI and Large Language Models (LLMs), giving a tailored lifecycle and discussing specific applications across different critical infrastructure sectors. Lastly, we discuss potential future directions that promise to enhance the security and resilience of critical infrastructures. This paper proposes innovative strategies for CIP from evolving attacks and enhances comprehension of cybersecurity concerns related to critical infrastructure.
- [15] arXiv:2405.04878 [pdf, ps, other]
-
Title: The Need Of Trustworthy Announcements To Achieve Driving ComfortComments: Journal ArticleSubjects: Cryptography and Security (cs.CR)
An Intelligent Transport System (ITS) is more demanding nowadays and it can be achieved through deploying Vehicular Ad Hoc Networks (VANETs). Vehicles and Roadside Units (RSUs) exchange traffic events. Malicious drivers generate false events. Thus, they need to be identified to maintain trustworthy communication. When an authorised user acts maliciously, the security scheme typically fails. However, a trust model can isolate false messages. In this paper, the significance of trustworthy announcements for VANETs is analysed. To this end, a series of experiments is conducted in Veins to illustrate how the trustworthiness of announcements affects travel time. A traffic scenario is created where vehicles detour to an alternate route with an announcement from the leading vehicle. Both true and false announcements are considered. Results confirm that false announcements and refraining from announcements increase travel time. However, the travel time is reduced with trustworthy announcements. From this analysis, it can be concluded that trustworthy announcements facilitate driver comfort.
- [16] arXiv:2405.04885 [pdf, ps, other]
-
Title: A trust management framework for vehicular ad hoc networksComments: Journal ArticleSubjects: Cryptography and Security (cs.CR)
Vehicular Ad Hoc Networks (VANETs) enable road users and public infrastructure to share information that improves the operation of roads and driver experience. However, these are vulnerable to poorly behaved authorized users. Trust management is used to address attacks from authorized users in accordance with their trust score. By removing the dissemination of trust metrics in the validation process, communication overhead and response time are lowered. In this paper, we propose a new Tamper-Proof Device (TPD) based trust management framework for controlling trust at the sender side vehicle that regulates driver behaviour. Moreover, the dissemination of feedback is only required when there is conflicting information in the VANET. If a conflict arises, the Road-Side Unit (RSU) decides, using the weighted voting system, whether the originator is to be believed, or not. The framework is evaluated against a centralized reputation approach and the results demonstrate that it outperforms the latter.
- [17] arXiv:2405.04892 [pdf, ps, other]
-
Title: A fuzzy reward and punishment scheme for vehicular ad hoc networksComments: Journal ArticleSubjects: Cryptography and Security (cs.CR)
Trust management is an important security approach for the successful implementation of Vehicular Ad Hoc Networks (VANETs). Trust models evaluate messages to assign reward or punishment. This can be used to influence a driver's future behaviour. In the author's previous work, a sender side based trust management framework is developed which avoids the receiver evaluation of messages. However, this does not guarantee that a trusted driver will not lie. These "untrue attacks" are resolved by the RSUs using collaboration to rule on a dispute, providing a fixed amount of reward and punishment. The lack of sophistication is addressed in this paper with a novel fuzzy RSU controller considering the severity of incident, driver past behaviour, and RSU confidence to determine the reward or punishment for the conflicted drivers. Although any driver can lie in any situation, it is expected that trustworthy drivers are more likely to remain so, and vice versa. This behaviour is captured in a Markov chain model for sender and reporter drivers where their lying characteristics depend on trust score and trust state. Each trust state defines the driver's likelihood of lying using different probability distribution. An extensive simulation is performed to evaluate the performance of the fuzzy assessment and examine the Markov chain driver behaviour model with changing the initial trust score of all or some drivers in Veins simulator. The fuzzy and the fixed RSU assessment schemes are compared, and the result shows that the fuzzy scheme can encourage drivers to improve their behaviour.
- [18] arXiv:2405.05022 [pdf, other]
-
Title: Adversarial Threats to Automatic Modulation Open Set Recognition in Wireless NetworksSubjects: Cryptography and Security (cs.CR); Social and Information Networks (cs.SI)
Automatic Modulation Open Set Recognition (AMOSR) is a crucial technological approach for cognitive radio communications, wireless spectrum management, and interference monitoring within wireless networks. Numerous studies have shown that AMR is highly susceptible to minimal perturbations carefully designed by malicious attackers, leading to misclassification of signals. However, the adversarial security issue of AMOSR has not yet been explored. This paper adopts the perspective of attackers and proposes an Open Set Adversarial Attack (OSAttack), aiming at investigating the adversarial vulnerabilities of various AMOSR methods. Initially, an adversarial threat model for AMOSR scenarios is established. Subsequently, by analyzing the decision criteria of both discriminative and generative open set recognition, OSFGSM and OSPGD are proposed to reduce the performance of AMOSR. Finally, the influence of OSAttack on AMOSR is evaluated utilizing a range of qualitative and quantitative indicators. The results indicate that despite the increased resistance of AMOSR models to conventional interference signals, they remain vulnerable to attacks by adversarial examples.
- [19] arXiv:2405.05023 [pdf, other]
-
Title: HackCar: a test platform for attacks and defenses on a cost-contained automotive architectureComments: 7 pages, 2 figures, accepted for publication at the 35th IEEE Intelligent Vehicles Symposium (IEEE IV 2024)Subjects: Cryptography and Security (cs.CR)
In this paper, we introduce the design of HackCar, a testing platform for replicating attacks and defenses on a generic automotive system without requiring access to a complete vehicle. This platform empowers security researchers to illustrate the consequences of attacks targeting an automotive system on a realistic platform, facilitating the development and testing of security countermeasures against both existing and novel attacks. The HackCar platform is built upon an F1-10th model, to which various automotive-grade microcontrollers are connected through automotive communication protocols. This solution is crafted to be entirely modular, allowing for the creation of diverse test scenarios. Researchers and practitioners can thus develop innovative security solutions while adhering to the constraints of automotive-grade microcontrollers. We showcase our design by comparing it with a real, licensed, and unmodified vehicle. Additionally, we analyze the behavior of the HackCar in both an attack-free scenario and a scenario where an attack on in-vehicle communication is deployed.
- [20] arXiv:2405.05040 [pdf, other]
-
Title: Gröbner Basis Cryptanalysis of Ciminion and HydraAuthors: Matthias Johann SteinerSubjects: Cryptography and Security (cs.CR)
Ciminion and Hydra are two recently introduced symmetric key Pseudo-Random Functions for Multi-Party Computation applications. For efficiency both primitives utilize quadratic permutations at round level. Therefore, polynomial system solving-based attacks pose a serious threat to these primitives. For Ciminion we construct a quadratic degree reverse lexicographic (DRL) Gr\"obner basis for the iterated polynomial model via affine transformations. For Hydra we provide a computer-aided proof in SageMath that a quadratic DRL Gr\"obner basis is already contained within the iterated polynomial system for the Hydra heads after affine transformations and a linear change of coordinates.
Our Ciminion DRL Gr\"obner basis simplifies cryptanalysis, since one does not need to impose genericity assumptions, like being regular or semi-regular, anymore to derive complexity estimates on key recovery attacks.
In the Hydra proposal it was claimed that $r_\mathcal{H} = 31$ rounds for the heads are sufficient to achieve $128$ bits of security against Gr\"obner basis attacks for key recovery. However, for $r_\mathcal{H} = 31$ standard term order conversion to a lexicographic (LEX) Gr\"obner basis for our Hydra DRL Gr\"obner basis requires just $126$ bits. Moreover, via the Eigenvalue Method up to $r_\mathcal{H} = 33$ rounds can be attacked below $128$ bits. - [21] arXiv:2405.05117 [pdf, other]
-
Title: (In)Security of Mobile Apps in Developing Countries: A Systematic Literature ReviewComments: 18 pages, 1 figure, submitted to a well-known journalSubjects: Cryptography and Security (cs.CR)
In developing countries, several key sectors, including education, finance, agriculture, and healthcare, mainly deliver their services via mobile app technology on handheld devices. As a result, mobile app security has emerged as a paramount issue in developing countries. In this paper, we investigate the state of research on mobile app security, focusing on developing countries. More specifically, we performed a systematic literature review exploring the research directions taken by existing works, the different security concerns addressed, and the techniques used by researchers to highlight or address app security issues. Our main findings are: (1) the literature includes only a few studies on mobile app security in the context of developing countries ; (2) among the different security concerns that researchers study, vulnerability detection appears to be the leading research topic; (3) FinTech apps are revealed as the main target in the relevant literature. Overall, our work highlights that there is largely room for developing further specialized techniques addressing mobile app security in the context of developing countries.
- [22] arXiv:2405.05175 [pdf, other]
-
Title: Air Gap: Protecting Privacy-Conscious Conversational AgentsAuthors: Eugene Bagdasaryan, Ren Yi, Sahra Ghalebikesabi, Peter Kairouz, Marco Gruteser, Sewoong Oh, Borja Balle, Daniel RamageSubjects: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG)
The growing use of large language model (LLM)-based conversational agents to manage sensitive user data raises significant privacy concerns. While these agents excel at understanding and acting on context, this capability can be exploited by malicious actors. We introduce a novel threat model where adversarial third-party apps manipulate the context of interaction to trick LLM-based agents into revealing private information not relevant to the task at hand.
Grounded in the framework of contextual integrity, we introduce AirGapAgent, a privacy-conscious agent designed to prevent unintended data leakage by restricting the agent's access to only the data necessary for a specific task. Extensive experiments using Gemini, GPT, and Mistral models as agents validate our approach's effectiveness in mitigating this form of context hijacking while maintaining core agent functionality. For example, we show that a single-query context hijacking attack on a Gemini Ultra agent reduces its ability to protect user data from 94% to 45%, while an AirGapAgent achieves 97% protection, rendering the same attack ineffective. - [23] arXiv:2405.05193 [pdf, other]
-
Title: Systematic Use of Random Self-Reducibility against Physical AttacksAuthors: Ferhat Erata, TingHung Chiu, Anthony Etim, Srilalith Nampally, Tejas Raju, Rajashree Ramu, Ruzica Piskac, Timos Antonopoulos, Wenjie Xiong, Jakub SzeferSubjects: Cryptography and Security (cs.CR)
This work presents a novel, black-box software-based countermeasure against physical attacks including power side-channel and fault-injection attacks. The approach uses the concept of random self-reducibility and self-correctness to add randomness and redundancy in the execution for protection. Our approach is at the operation level, is not algorithm-specific, and thus, can be applied for protecting a wide range of algorithms. The countermeasure is empirically evaluated against attacks over operations like modular exponentiation, modular multiplication, polynomial multiplication, and number theoretic transforms. An end-to-end implementation of this countermeasure is demonstrated for RSA-CRT signature algorithm and Kyber Key Generation public key cryptosystems. The countermeasure reduced the power side-channel leakage by two orders of magnitude, to an acceptably secure level in TVLA analysis. For fault injection, the countermeasure reduces the number of faults to 95.4% in average.
- [24] arXiv:2405.05196 [pdf, other]
-
Title: SINBAD: Saliency-informed detection of breakage caused by ad blockingAuthors: Saiid El Hajj Chehade (1), Sandra Siby (2), Carmela Troncoso (1) ((1) EPFL, (2) Imperial College London)Comments: 19 pages, 13 figures, Appearing in IEEE S&P 2024Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
Privacy-enhancing blocking tools based on filter-list rules tend to break legitimate functionality. Filter-list maintainers could benefit from automated breakage detection tools that allow them to proactively fix problematic rules before deploying them to millions of users. We introduce SINBAD, an automated breakage detector that improves the accuracy over the state of the art by 20%, and is the first to detect dynamic breakage and breakage caused by style-oriented filter rules. The success of SINBAD is rooted in three innovations: (1) the use of user-reported breakage issues in forums that enable the creation of a high-quality dataset for training in which only breakage that users perceive as an issue is included; (2) the use of 'web saliency' to automatically identify user-relevant regions of a website on which to prioritize automated interactions aimed at triggering breakage; and (3) the analysis of webpages via subtrees which enables fine-grained identification of problematic filter rules.
- [25] arXiv:2405.05206 [pdf, other]
-
Title: Anomaly Detection in Certificate Transparency LogsSubjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
We propose an anomaly detection technique for X.509 certificates utilizing Isolation Forest. This method can be beneficial when compliance testing with X.509 linters proves unsatisfactory, and we seek to identify anomalies beyond standards compliance. The technique is validated on a sample of certificates from Certificate Transparency logs.
Cross-lists for Thu, 9 May 24
- [26] arXiv:2405.04762 (cross-list from cs.DC) [pdf, ps, other]
-
Title: Nearly-Optimal Consensus Tolerating Adaptive Omissions: Why is a Lot of Randomness is Needed?Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Cryptography and Security (cs.CR); Data Structures and Algorithms (cs.DS)
We study the problem of reaching agreement in a synchronous distributed system by $n$ autonomous parties, when the communication links from/to faulty parties can omit messages. The faulty parties are selected and controlled by an adaptive, full-information, computationally unbounded adversary. We design a randomized algorithm that works in $O(\sqrt{n}\log^2 n)$ rounds and sends $O(n^2\log^3 n)$ communication bits, where the number of faulty parties is $\Theta(n)$. Our result is simultaneously tight for both these measures within polylogarithmic factors: due to the $\Omega(n^2)$ lower bound on communication by Abraham et al. (PODC'19) and $\Omega(\sqrt{n/\log n})$ lower bound on the number of rounds by Bar-Joseph and Ben-Or (PODC'98). We also quantify how much randomness is necessary and sufficient to reduce time complexity to a certain value, while keeping the communication complexity (nearly) optimal. We prove that no MC algorithm can work in less than $\Omega(\frac{n^2}{\max\{R,n\}\log n})$ rounds if it uses less than $O(R)$ calls to a random source, assuming a constant fraction of faulty parties. This can be contrasted with a long line of work on consensus against an {\em adversary limited to polynomial computation time}, thus unable to break cryptographic primitives, culminating in a work by Ghinea et al. (EUROCRYPT'22), where an optimal $O(r)$-round solution with probability $1-(cr)^{-r}$ is given. Our lower bound strictly separates these two regimes, by excluding such results if the adversary is computationally unbounded. On the upper bound side, we show that for $R\in\tilde{O}(n^{3/2})$ there exists an algorithm solving consensus in $\tilde{O}(\frac{n^2}{R})$ rounds with high probability, where tilde notation hides a polylogarithmic factor. The communication complexity of the algorithm does not depend on the amount of randomness $R$ and stays optimal within polylogarithmic factor.
Replacements for Thu, 9 May 24
- [27] arXiv:2205.10580 (replaced) [pdf, other]
-
Title: Towards Secure Virtual Elections: Multiparty Computation of Order Based Voting RulesSubjects: Cryptography and Security (cs.CR)
- [28] arXiv:2308.15259 (replaced) [pdf, other]
-
Title: Shedding Light on CVSS Scoring Inconsistencies: A User-Centric Study on Evaluating Widespread Security VulnerabilitiesComments: To appear in the Proceedings of the IEEE Symposium on Security and Privacy (S&P) 2024Subjects: Cryptography and Security (cs.CR)
- [29] arXiv:2312.00025 (replaced) [pdf, other]
-
Title: Secure Transformer Inference ProtocolSubjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
- [30] arXiv:2401.04929 (replaced) [pdf, other]
-
Title: Learning-Based Difficulty Calibration for Enhanced Membership Inference AttacksSubjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
- [31] arXiv:2403.13237 (replaced) [pdf, ps, other]
-
Title: Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0Authors: Jiana Liao, Jinbo Wen, Jiawen Kang, Changyan Yi, Yang Zhang, Yutao Jiao, Dusit Niyato, Dong In Kim, Shengli XieSubjects: Cryptography and Security (cs.CR); Optimization and Control (math.OC)
- [32] arXiv:2404.11815 (replaced) [pdf, other]
-
Title: AquaSonic: Acoustic Manipulation of Underwater Data Center Operations and Resource ManagementAuthors: Jennifer Sheldon, Weidong Zhu, Adnan Abdullah, Sri Hrushikesh Varma Bhupathiraju, Takeshi Sugawara, Kevin R. B. Butler, Md Jahidul Islam, Sara RampazziComments: Accepted to IEEE S&P 2024Subjects: Cryptography and Security (cs.CR)
- [33] arXiv:2405.04032 (replaced) [pdf, other]
-
Title: Locally Differentially Private In-Context LearningComments: This paper was published at LREC-Coling 2024Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
- [34] arXiv:2405.04355 (replaced) [pdf, other]
-
Title: SmmPack: Obfuscation for SMM Modules with TPM Sealed KeyComments: 21 pages, 7 figures, 4 tables. Accepted at the 21st Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA 2024)Subjects: Cryptography and Security (cs.CR)
- [35] arXiv:2210.03394 (replaced) [pdf, ps, other]
-
Title: One-Wayness in Quantum CryptographyComments: 50 pages, 1 figureJournal-ref: TQC 2024, ProceedingsSubjects: Quantum Physics (quant-ph); Cryptography and Security (cs.CR)
- [36] arXiv:2308.00836 (replaced) [pdf, other]
-
Title: Differentially Private Linear Regression with Linked DataSubjects: Methodology (stat.ME); Cryptography and Security (cs.CR)
- [37] arXiv:2310.06771 (replaced) [pdf, other]
-
Title: Correlated Noise Provably Beats Independent Noise for Differentially Private LearningAuthors: Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep ThakurtaComments: Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, and Krishna Pillutla contributed equallyJournal-ref: ICLR 2024Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Optimization and Control (math.OC)
- [38] arXiv:2404.19227 (replaced) [pdf, other]
-
Title: Espresso: Robust Concept Filtering in Text-to-Image ModelsSubjects: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
[ showing up to 2000 entries per page: fewer | more ]
Disable MathJax (What is MathJax?)
Links to: arXiv, form interface, find, cs, recent, 2405, contact, help (Access key information)