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Bottom-up role-mining is the determination of a set of roles given as input a set of users and the permissions those users possess. It is well-established in the research literature, and in practice, as an important problem in information security. A natural objective that has been explored in prior work is for the set of roles to be of minimum size. We address this problem for practical inputs while reconciling foundations, specifically, that the problem is \cnph. We first observe that an approach from prior work that exploits a sufficient condition for an efficient algorithm, while a useful first step, does not scale to more recently proposed benchmark inputs. We propose a new technique: the enumeration of maximal bicliques. We point out that the number of maximal bicliques provides a natural measure of the hardness of an input. We leverage the enumeration of maximal bicliques in two different ways. Our first approach addresses more than half the benchmark inputs to yield exact results. The other approach is needed for hard instances; in it, we identify and adopt as roles those that correspond to large maximal bicliques. We have implemented all our algorithms and carried out an extensive empirical assessment, which suggests that our approaches are promising. Our code is available publicly as open-source., Comment: 23 pages, 7 figures, 9 tables

Following the enactment of the UN Regulation, substantial efforts have been directed toward implementing intrusion detection and prevention systems (IDPSs) and vulnerability analysis in Controller Area Network (CAN). However, Society of Automotive Engineers (SAE) J1939 protocol, despite its extensive application in camping cars and commercial vehicles, has seen limited vulnerability identification, which raises significant safety concerns in the event of security breaches. In this research, we explore and demonstrate attack techniques specific to SAE J1939 communication protocol. We introduce 14 attack scenarios, enhancing the discourse with seven scenarios recognized in the previous research and unveiling seven novel scenarios through our elaborate study. To verify the feasibility of these scenarios, we leverage a sophisticated testbed that facilitates real-time communication and the simulation of attacks. Our testing confirms the successful execution of 11 scenarios, underscoring their imminent threat to commercial vehicle operations. Some attacks will be difficult to detect because they only inject a single message. These results highlight unique vulnerabilities within SAE J1939 protocol, indicating the automotive cybersecurity community needs to address the identified risks., Comment: 18 pages, 7 figures, 5 tables; This is the accepted version of ESCAR USA 2024

High-performance IO demands low-overhead communication between user- and kernel space. This demand can no longer be fulfilled by traditional system calls. Linux's extended Berkeley Packet Filter (BPF) avoids user-/kernel transitions by just-in-time compiling user-provided bytecode and executing it in kernel mode with near-native speed. To still isolate BPF programs from the kernel, they are statically analyzed for memory- and type-safety, which imposes some restrictions but allows for good expressiveness and high performance. However, to mitigate the Spectre vulnerabilities disclosed in 2018, defenses which reject potentially-dangerous programs had to be deployed. We find that this affects 31% to 54% of programs in a dataset with 844 real-world BPF programs from popular open-source projects. To solve this, users are forced to disable the defenses to continue using the programs, which puts the entire system at risk. To enable secure and expressive untrusted Linux kernel extensions, we propose VeriFence, an enhancement to the kernel's Spectre defenses that reduces the number of BPF application programs rejected from 54% to zero. We measure VeriFence's overhead for all mainstream performance-sensitive applications of BPF (i.e., event tracing, profiling, and packet processing) and find that it improves significantly upon the status-quo where affected BPF programs are either unusable or enable transient execution attacks on the kernel., Comment: RAID'24

Physical Unclonable Functions (PUFs) have been shown to be a highly promising solution for enabling high security systems tailored for low-power devices. Commonly, PUFs are utilised to generate cryptographic keys on-the-fly, replacing the need to store keys in vulnerable, non-volatile memories. Due to the physical nature of PUFs, environmental variations cause noise, manifesting themselves as errors which are apparent in the initial PUF measurements. This necessitates expensive active error correction techniques which can run counter to the goal of lightweight security. ML-based techniques for authenticating noisy PUF measurements were explored as an alternative to error correction techniques, bringing about the concept of a PUF Phenotype, where PUF identity is considered as a structure agnostic representation of the PUF, with relevant noise encoding. This work proposes a full noise-tolerant authentication protocol based on the PUF Phenotype concept and methodology for an Internet-of-Things (IoT) network, demonstrating mutual authentication and forward secrecy in a setting suitable for device-to-device communication. Upon conducting security and performance analyses, it is evident that our proposed scheme demonstrates resilience against various attacks compared to the currently existing PUF protocols., Comment: 11 pages, 6 figures

Physically Unclonable Functions (PUFs) provide a streamlined solution for lightweight device authentication. Delay-based Arbiter PUFs, with their ease of implementation and vast challenge space, have received significant attention; however, they are not immune to modelling attacks that exploit correlations between their inputs and outputs. Research is therefore polarized between developing modelling-resistant PUFs and devising machine learning attacks against them. This dichotomy often results in exaggerated concerns and overconfidence in PUF security, primarily because there lacks a universal tool to gauge a PUF's security. In many scenarios, attacks require additional information, such as PUF type or configuration parameters. Alarmingly, new PUFs are often branded `secure' if they lack a specific attack model upon introduction. To impartially assess the security of delay-based PUFs, we present a generic framework featuring a Mixture-of-PUF-Experts (MoPE) structure for mounting attacks on various PUFs with minimal adversarial knowledge, which provides a way to compare their performance fairly and impartially. We demonstrate the capability of our model to attack different PUF types, including the first successful attack on Heterogeneous Feed-Forward PUFs using only a reasonable amount of challenges and responses. We propose an extension version of our model, a Multi-gate Mixture-of-PUF-Experts (MMoPE) structure, facilitating multi-task learning across diverse PUFs to recognise commonalities across PUF designs. This allows a streamlining of training periods for attacking multiple PUFs simultaneously. We conclude by showcasing the potent performance of MoPE and MMoPE across a spectrum of PUF types, employing simulated, real-world unbiased, and biased data sets for analysis., Comment: 13 pages, 6 figures, journal

Since the emergence of the internet, IPSEC has undergone significant changes due to changes in the type and behavior of users worldwide. IEEE 802.3ad, while considered a key aspect of the IPSEC model, is predictable and can result in potential design flaws, making it relatively easy to access a secure workstation. Thus, it is critical to leverage the benefits of multiple ISPs (multi-WAN) and a link aggregation model and integrate an aspect of randomisation in the network. This facet of the network is highlighted by the proof of concept in the simulation of a double pendulum. The analysis of POC provided a network topology designed to utilize multiple WAN, 802.3ad link aggregation, and other environmental components to create a sense of true randomness within a network system. An analysis of this approach shows that it accounts for the data stream's size, transmission speed, WANs and VPNs' location, and other environmental factors to create a sense of randomness. Based on the proof concept, it can be concluded that attaining randomisation using multi-WAN, VPN, and 802.3ad is a highly effective model for improving IPSEC., Comment: 13 pages, 2 figures

If the extraction of sensor fingerprints represents nowadays an important forensic tool for sensor attribution, it has been shown recently that images coming from several sensors were more prone to generate False Positives (FP) by presenting a common "leak". In this paper, we investigate the possible cause of this leak and after inspecting the EXIF metadata of the sources causing FP, we found out that they were related to the Adobe Lightroom or Photoshop softwares. The cross-correlation between residuals on images presenting FP reveals periodic peaks showing the presence of a periodic pattern. By developing our own images with Adobe Lightroom we are able to show that all developments from raw images (or 16 bits per channel coded) to 8 bits-coded images also embed a periodic 128x128 pattern very similar to a watermark. However, we also show that the watermark depends on both the content and the architecture used to develop the image. The rest of the paper presents two different ways of removing this watermark, one by removing it from the image noise component, and the other by removing it in the pixel domain. We show that for a camera presenting FP, we were able to prevent the False Positives. A discussion with Adobe representatives informed us that the company decided to add this pattern in order to induce dithering.

Outsourcing a relational database to the cloud offers several benefits, including scalability, availability, and cost-effectiveness. However, there are concerns about the security and confidentiality of the outsourced data. A general approach here would be to encrypt the data with a standardized encryption algorithm and then store the data only encrypted in the cloud. The problem with this approach, however, is that with encryption, important properties of the data such as sorting, format or comparability, which are essential for the functioning of database queries, are lost. One solution to this problem is the use of encryption algorithms, which also preserve these properties in the encrypted data, thus enabling queries to encrypted data. These algorithms range from simple algorithms like Caesar encryption to secure algorithms like mOPE. The report at hand presents a survey on common encryption techniques used for storing data in relation Cloud database services. It presents the applied methods and identifies their characteristics., Comment: 34 pages, 10 figures

Outsourcing a relational database to the cloud offers several benefits, including scalability, availability, and cost-effectiveness. However, there are concerns about the confidentiality and security of the outsourced data. A general approach here would be to encrypt the data with a standardized encryption algorithm and then store the data only encrypted in the cloud. The problem with this approach, however, is that with encryption, important properties of the data such as sorting, format or comparability, which are essential for the functioning of database queries, are lost. One solution to this problem is the use of (e.g. order-preserving) encryption algorithms, which also preserve these properties in the encrypted data, thus enabling queries to encrypted data. These algorithms range from simple algorithms like Caesar encryption to secure algorithms like mOPE. In order to be able to use these algorithms as easy as possible, ``DiCE'' a JDBC driver was developed, that parses SQL queries as a proxy and transparently encrypts and decrypts these queries. This allows to execute many queries on an encrypted database in the cloud with (nearly) the performance as on unencrypted databases. The DiCE driver can be used with any other JDBC driver and therefore supports a variety of databases. The driver can be configured to support different encryption algorithms. To keep track of the operations, the ``Dice Information Client'' has been developed to track the encryption and decryption of the driver. Although the result of the performance analysis shows a certain overhead due to the parsing and encryption of the SQL queries in the Dice driver, this overhead is significantly reduced by other influencing factors such as the network and parallel queries., Comment: 12 pages

RISC-V is a recently developed open instruction set architecture gaining a lot of attention. To achieve a lasting security on these systems and design efficient countermeasures, a better understanding of vulnerabilities to novel and potential future attacks is mandatory. This paper demonstrates that RISC-V is sensible to Jump-Oriented Programming, a class of complex code-reuse attacks. We provide an analysis of new dispatcher gadgets we discovered, and show how they can be used together in order to build a stealth attack, bypassing existing protections. A proof-of-concept attack is implemented on an embedded web server compiled for RISC-V, in which we introduced a vulnerability, allowing an attacker to remotely read an arbitrary file from the host machine., Comment: 16 pages. arXiv admin note: text overlap with arXiv:2211.16212

Cyber incident response is critical to business continuity -- we describe a new exercise that challenges professionals to play the role of Chief Information Security Officer (CISO) for a major financial organisation. Teams must decide how organisational team and budget resources should be deployed across Enterprise Architecture (EA) upgrades and cyber incidents. Every choice made has an impact -- some prevent whilst others may trigger new or continue current attacks. We explain how the underlying platform supports these interactions through a reactionary event mechanism that introduces events based on the current attack surface of the organisation. We explore how our platform manages to introduce randomness on top of triggered events to ensure that the exercise is not deterministic and better matches incidents in the real world. We conclude by describing next steps for the exercise and how we plan to use it in the future to better understand risk decision making., Comment: DGASP-57

In applications such as end-to-end encrypted instant messaging, secure email, and device pairing, users need to compare key fingerprints to detect impersonation and adversary-in-the-middle attacks. Key fingerprints are usually computed as truncated hashes of each party's view of the channel keys, encoded as an alphanumeric or numeric string, and compared out-of-band, e.g. manually, to detect any inconsistencies. Previous work has extensively studied the usability of various verification strategies and encoding formats, however, the exact effect of key fingerprint length on the security and usability of key fingerprint verification has not been rigorously investigated. We present a 162-participant study on the effect of numeric key fingerprint length on comparison time and error rate. While the results confirm some widely-held intuitions such as general comparison times and errors increasing significantly with length, a closer look reveals interesting nuances. The significant rise in comparison time only occurs when highly similar fingerprints are compared, and comparison time remains relatively constant otherwise. On errors, our results clearly distinguish between security non-critical errors that remain low irrespective of length and security critical errors that significantly rise, especially at higher fingerprint lengths. A noteworthy implication of this latter result is that Signal/WhatsApp key fingerprints provide a considerably lower level of security than usually assumed., Comment: Accepted to International Conference on Availability, Reliability and Security (ARES 2023)

Machine learning as a service (MLaaS) framework provides intelligent services or well-trained artificial intelligence (AI) models for local devices. However, in the process of model transmission and deployment, there are security issues, i.e. AI model leakage due to the unreliable transmission environments and illegal abuse at local devices without permission. Although existing works study the intellectual property (IP) protection of AI models, they mainly focus on the watermark-based and encryption-based methods and have the following problems: (i) The watermark-based methods only provide passive verification afterward rather than active protection. (ii) Encryption-based methods are low efficiency in computation and low security in key storage. (iii) The existing methods are not device-bind without the ability to avoid illegal abuse of AI models. To deal with these problems, we propose a device-bind and key-storageless hardware AI model IP protection mechanism. First, a physical unclonable function (PUF) and permute-diffusion encryption-based AI model protection framework is proposed, including the PUF-based secret key generation and the geometric-value transformation-based weights encryption. Second, we design a PUF-based key generation protocol, where delay-based Anderson PUF is adopted to generate the derive-bind secret key. Besides, convolutional coding and convolutional interleaving technologies are combined to improve the stability of PUF-based key generation and reconstruction. Third, a permute and diffusion-based intelligent model weights encryption/decryption method is proposed to achieve effective IP protection, where chaos theory is utilized to convert the PUF-based secret key to encryption/decryption keys. Finally, experimental evaluation demonstrates the effectiveness of the proposed intelligent model IP protection mechanism., Comment: 12 pages, 9 figures, 4 tables

To date, a large number of research papers have been written on the classification of malware, its identification, classification into different families and the distinction between malware and goodware. These works have been based on captured malware samples and have attempted to analyse malware and goodware using various techniques, including techniques from the field of artificial intelligence. For example, neural networks have played a significant role in these classification methods. Some of this work also deals with analysing malware using its visualisation. These works usually convert malware samples capturing the structure of malware into image structures, which are then the object of image processing. In this paper, we propose a very unconventional and novel approach to malware visualisation based on dynamic behaviour analysis, with the idea that the images, which are visually very interesting, are then used to classify malware concerning goodware. Our approach opens an extensive topic for future discussion and provides many new directions for research in malware analysis and classification, as discussed in conclusion. The results of the presented experiments are based on a database of 6 589 997 goodware, 827 853 potentially unwanted applications and 4 174 203 malware samples provided by ESET and selected experimental data (images, generating polynomial formulas and software generating images) are available on GitHub for interested readers. Thus, this paper is not a comprehensive compact study that reports the results obtained from comparative experiments but rather attempts to show a new direction in the field of visualisation with possible applications in malware analysis., Comment: This paper is under review; the section describing conversion from malware structure to fractal figure is temporarily erased here to protect our idea. It will be replaced by a full version when accepted

RISC-V is an open instruction set architecture recently developed for embedded real-time systems. To achieve a lasting security on these systems and design efficient countermeasures, a better understanding of vulnerabilities to novel and potential future attacks is mandatory. This paper demonstrates that RISC-V is sensible to Jump-Oriented Programming, a class of complex code-reuse attacks, able to bypass existing protections. We provide a first analysis of RISC-V systems' attack surface exploitable by such attacks, and show how they can be chained together in order to build a full-fledged attack. We use a conservative hypothesis on exploited registers and instruction patterns, in an approach we called reserved registers. This approach is implemented on a vulnerable RISC-V application, and successfully applied to expose an AES256 secret., Comment: 9 pages

The effectiveness of binary analysis tools and techniques is often measured with respect to how well they map to a ground truth. We have found that not all ground truths are created equal. This paper challenges the binary analysis community to take a long look at the concept of ground truth, to ensure that we are in agreement with definition(s) of ground truth, so that we can be confident in the evaluation of tools and techniques. This becomes even more important as we move to trained machine learning models, which are only as useful as the validity of the ground truth in the training.

Memory safety is a key security property that stops memory corruption vulnerabilities. Existing sanitizers enforce checks and catch such bugs during development and testing. However, they either provide partial memory safety or have overwhelmingly high performance overheads. Our novel sanitizer PACSan enforces spatial and temporal memory safety with no false positives at low performance overheads. PACSan removes the majority of the overheads involved in pointer tracking by sealing metadata in pointers through ARM PA (Pointer Authentication), and performing the memory safety checks when pointers are dereferenced. We have developed a prototype of PACSan and systematically evaluated its security and performance on the Magma, Juliet, Nginx, and SPEC CPU2017 test suites, respectively. In our evaluation, PACSan shows no false positives together with negligible false negatives, while introducing stronger security guarantees and lower performance overheads than state-of-the-art sanitizers, including HWASan, ASan, SoftBound+CETS, Memcheck, LowFat, and PTAuth. Specifically, PACSan has 0.84x runtime overhead and 1.92x memory overhead on average. Compared to the widely deployed ASan, PACSan has no false positives and much fewer false negatives and reduces 7.172% runtime overheads and 89.063%memory overheads., Comment: 11 pages

We present a Federated Learning (FL) based solution for building a distributed classifier capable of detecting URLs containing GDPR-sensitive content related to categories such as health, sexual preference, political beliefs, etc. Although such a classifier addresses the limitations of previous offline/centralised classifiers,it is still vulnerable to poisoning attacks from malicious users that may attempt to reduce the accuracy for benign users by disseminating faulty model updates. To guard against this, we develop a robust aggregation scheme based on subjective logic and residual-based attack detection. Employing a combination of theoretical analysis, trace-driven simulation, as well as experimental validation with a prototype and real users, we show that our classifier can detect sensitive content with high accuracy, learn new labels fast, and remain robust in view of poisoning attacks from malicious users, as well as imperfect input from non-malicious ones.

Designing and implementing secure software is inarguably more important than ever. However, despite years of research into privilege separating programs, it remains difficult to actually do so and such efforts can take years of labor-intensive engineering to reach fruition. At the same time, new intra-process isolation primitives make strong data isolation and privilege separation more attractive from a performance perspective. Yet, substituting intra-process security boundaries for time-tested process boundaries opens the door to subtle but devastating privilege leaks. In this work, we present Polytope, a language extension to C++ that aims to make efficient privilege separation accessible to a wider audience of developers. Polytope defines a policy language encoded as C++11 attributes that separate code and data into distinct program partitions. A modified Clang front-end embeds source-level policy as metadata nodes in the LLVM IR. An LLVM pass interprets embedded policy and instruments an IR with code to enforce the source-level policy using Intel MPK. A run-time support library manages partitions, protection keys, dynamic memory operations, and indirect call target privileges. An evaluation demonstrates that Polytope provides equivalent protection to prior systems with a low annotation burden and comparable performance overhead. Polytope also renders privilege leaks that contradict intended policy impossible to express.

Evaluation of the resistance of implemented cryptographic algorithms against SCA attacks, as well as detecting of SCA leakage sources at an early stage of the design process, is important for an efficient re-design of the implementation. Thus, effective SCA methods that do not depend on the key processed in the cryptographic operations are beneficially and can be a part of the efficient design methodology for implementing cryptographic approaches. In this work we compare two different methods that are used to analyse power traces of elliptic curve point multiplications. The first method the comparison to the mean is a simple method based on statistical analysis. The second one is K-means - the mostly used unsupervised machine learning algorithm for data clustering. The results of our early work showed that the machine learning algorithm was not superior to the simple approach. In this work we concentrate on the comparison of the attack results using both analysis methods with the goal to understand their benefits and drawbacks. Our results show that the comparison to the mean works properly only if the scalar processed during the attacked kP execution is balanced, i.e. if the number of '1' in the scalar k is about as high as the number of '0'. In contrast to this, K-means is effective also if the scalar is highly unbalanced. It is still effective even if the scalar k contains only a very small number of '0' bits.

Side Channel Analysis attacks take advantage of the information leaked from the implementations of cryptographic algorithms. In this paper we describe two key revealing methods which are based on machine learning algorithms: K-means and PCA. We performed the attacks against ECDSA implementations without any prior knowledge about the key and achieved 100% accuracy for an implementation without any countermeasures against horizontal attacks and 88.7% accuracy for an implementation with bus address sequencing. In the scenario where the kP operation inputs are controlled by the attacker (as during signature verification), we achieved 98.3% accuracy for the implementation with countermeasures.

Over-provisioning technology is typically introduced as a means to improve the performance of storage systems, such as databases. The over-provisioning area is both hidden and difficult for normal users to access. This paper focuses on attack models for such hidden areas. Malicious hackers use advanced over-provisioning techniques that vary capacity according to workload, and as such, our focus is on attack models that use variable over-provisioning technology. According to these attack models, it is possible to scan for invalid blocks containing original data or malware code that is hidden in the over-provisioning area. In this paper, we outline the different forensic processes performed for each memory cell type of the over-provisioning area and disclose security enhancement techniques that increase immunity to these attack models. This leads to a discussion of forensic possibilities and countermeasures for SSDs that can change the over-provisioning area. We also present information-hiding attacks and information-exposing attacks on the invalidation area of the SSD. Our research provides a good foundation upon which the performance and security of SSD-based databases can be further improved., Comment: resubmitted(Nov. 25, 2021) and accepted(Dec. 15, 2021)in IEEE ACCESS(Access-2021-39427)

The deployment of monoculture software stacks can cause a devastating damage even by a single exploit against a single vulnerability. Inspired by the resilience benefit of biological diversity, the concept of software diversity has been proposed in the security domain. Although it is intuitive that software diversity may enhance security, its effectiveness has not been quantitatively investigated. Currently, no theoretical or empirical study has been explored to measure the security effectiveness of network diversity. In this paper, we take a first step towards ultimately tackling the problem. We propose a systematic framework that can model and quantify the security effectiveness of network diversity. We conduct simulations to demonstrate the usefulness of the framework. In contrast to the intuitive belief, we show that diversity does not necessarily improve security from a whole-network perspective. The root cause of this phenomenon is that the degree of vulnerability in diversified software implementations plays a critical role in determining the security effectiveness of software diversity.

The Internet of Vehicles (IoV), whereby interconnected vehicles communicate with each other and with road infrastructure on a common network, has promising socio-economic benefits but also poses new cyber-physical threats. Data on vehicular attackers can be realistically gathered through cyber threat intelligence using systems like honeypots. Admittedly, configuring honeypots introduces a trade-off between the level of honeypot-attacker interactions and any incurred overheads and costs for implementing and monitoring these honeypots. We argue that effective deception can be achieved through strategically configuring the honeypots to represent components of the IoV and engage attackers to collect cyber threat intelligence. In this paper, we present HoneyCar, a novel decision support framework for honeypot deception in IoV. HoneyCar builds upon a repository of known vulnerabilities of the autonomous and connected vehicles found in the Common Vulnerabilities and Exposure (CVE) data within the National Vulnerability Database (NVD) to compute optimal honeypot configuration strategies. By taking a game-theoretic approach, we model the adversarial interaction as a repeated imperfect-information zero-sum game in which the IoV network administrator chooses a set of vulnerabilities to offer in a honeypot and a strategic attacker chooses a vulnerability of the IoV to exploit under uncertainty. Our investigation is substantiated by examining two different versions of the game, with and without the re-configuration cost to empower the network administrator to determine optimal honeypot configurations. We evaluate HoneyCar in a realistic use case to support decision makers with determining optimal honeypot configuration strategies for strategic deployment in IoV.

Radio Frequency Identification (RFID) systems are among the most widespread computing technologies with technical potential and profitable opportunities in numerous applications worldwide. Further, RFID is the core technology behind the Internet of Things (IoT), which can accomplish the real-time transmission of information between objects without manual operation. However, RFID security has been taken for granted for several years, causing multiple vulnerabilities that can even damage human functionalities. The latest ISO/IEC 18000-63:2015 standard concerning RFID dates to 2015, and much freedom has been given to manufacturers responsible for making their devices secure. The lack of a substantial standard for devices that implement RFID technology creates many vulnerabilities that expose end-users to elevated risk. Hence, this paper gives the reader a clear overview of the technology, and it analyzes 23 well-known RFID attacks such as Reverse Engineering, Buffer Overflow, Eavesdropping, and Malware. Moreover, given the exceptional capabilities and utilities of RFID devices, this paper has focused on security measures and defenses for protecting them, such as Active Jamming, Shielding tag, and Authentication., Comment: 9 pages, 4 figures

Security is a key problem for the transmission, interchange and storage process of multimedia systems and applications. In 2018, M. Li et al. proposed in-depth security analysis on an image encryption algorithm based on a first-order time-delay system (IEATD) and gave a specific chosen-plaintext attack on it. Moreover, an enhanced version called as IEACD was designed to fix the reported security defects. This paper analyzes the essential structures of the two algorithms and evaluates their real security performances: 1) no efficient nonlinear operations are adopted to assure the sensibility of keystream; 2) the equivalent secret key of IEATD can be efficiently recovered from one known plain-image and the corresponding cipher-image; 3) IEACD can still be efficiently cracked with a chosen-plaintext attack. Both rigorous theoretical analyses and detailed experimental results are provided to demonstrate effectiveness of the advanced cryptanalytic methods., Comment: 10 pages, 5 figures, 2 tables

The security of messaging applications against person-in-the-middle attacks relies on the authenticity of the exchanged keys. For users unable to meet in person, a manual key fingerprint verification is necessary to ascertain key authenticity. Such fingerprints can be exchanged visually or verbally, and it is not clear in which condition users perform best. This paper reports the results of a 62-participant study that investigated differences in performance and perceived usability of visual and verbal comparisons of word-based key fingerprints, and the influence of the individual's cognitive learning style. The results show visual comparisons to be more effective against non-security critical errors and are perceived to provide increased confidence, yet participants perceive verbal comparisons to be easier and require less mental effort. Besides, limited evidence was found on the influence of the individual's learning style on their performance., Comment: This is an accepted manuscript to appear in the proceedings of the 15th International Symposium on Human Aspects of Information Security & Assurance, HAISA 2021

In this paper we will present the Multidimensional Byzantine Agreement (MBA) Protocol, a leaderless Byzantine agreement protocol defined for complete and synchronous networks that allows a network of nodes to reach consensus on a vector of relevant information regarding a set of observed events. The consensus process is carried out in parallel on each component, and the output is a vector whose components are either values with wide agreement in the network (even if no individual node agrees on every value) or a special value $\bot$ that signals irreconcilable disagreement. The MBA Protocol is probabilistic and its execution halts with probability 1, and the number of steps necessary to halt follows a Bernoulli-like distribution. The design combines a Multidimensional Graded Consensus and a Multidimensional Binary Byzantine Agreement, the generalization to the multidimensional case of two protocols by Micali and Feldman. We prove the correctness and security of the protocol assuming a synchronous network where less than a third of the nodes are malicious., Comment: 15 pages, 0 figures

Information sharing is vital in resisting cyberattacks, and the volume and severity of these attacks is increasing very rapidly. Therefore responders must triage incoming warnings in deciding how to act. This study asked a very specific question: "how can the addition of confidence information to alerts and warnings improve overall resistance to cyberattacks." We sought, in particular, to identify current practices, and if possible, to identify some "best practices." The research involved literature review and interviews with subject matter experts at every level from system administrators to persons who develop broad principles of policy. An innovative Modified Online Delphi Panel technique was used to elicit judgments and recommendations from experts who were able to speak with each other and vote anonymously to rank proposed practices., Comment: 50pp. CCICADA Technical Report

A privacy-preserving Context-Aware Publish-Subscribe System (CA-PSS) enables an intermediary (broker) to match the content from a publisher and the subscription by a subscriber based on the current context while preserving confidentiality of the subscriptions and notifications. While a privacy-preserving Ride-Hailing Service (RHS) enables an intermediary (service provider) to match a ride request with a taxi driver in a privacy-friendly manner. In this work, we attack a privacy-preserving CA-PSS proposed by Nabeel et al. (2013), where we show that any entity in the system including the broker can learn the confidential subscriptions of the subscribers. We also attack a privacy-preserving RHS called lpRide proposed by Yu et al. (2019), where we show that any rider/driver can efficiently recover the secret keys of all other riders and drivers. Also, we show that any rider/driver will be able to learn the location of any rider. The attacks are based on our cryptanalysis of the modified Paillier cryptosystem proposed by Nabeel et al. that forms a building block for both the above protocols.

Cookie banners are devices implemented by websites to allow users to manage their privacy settings with respect to the use of cookies. They are part of a user's daily web browsing experience since legislation in Europe requires websites to show such notices. In this paper, we carry out a large-scale study of more than 17,000 websites including more than 7,500 cookie banners in Greece and the UK to determine compliance and tracking transparency levels. Our analysis shows that although more than 60% of websites store third-party cookies in both countries, only less than 50% show a cookie notice and hence a substantial proportion do not comply with the law even at the very basic level. We find only a small proportion of the surveyed websites providing a direct opt-out option, with an overwhelming majority either nudging users towards privacy-intrusive choices or making cookie rejection much harder than consent. Our results differ significantly in some cases from previous smaller-scale studies and hence underline the importance of large-scale studies for a better understanding of the big picture in cookie practices., Comment: 15 pages, 6 figures, 4 tables

The General Data Protection Regulation (GDPR) aims to ensure that all personal data processing activities are fair and transparent for the European Union (EU) citizens, regardless of whether these are carried out within the EU or anywhere else. To this end, it sets strict requirements to transfer personal data outside the EU. However, checking these requirements is a daunting task for supervisory authorities, particularly in the mobile app domain due to the huge number of apps available and their dynamic nature. In this paper, we propose a fully automated method to assess the compliance of mobile apps with the GDPR requirements for cross-border personal data transfers. We have applied the method to the top-free 10,080 apps from the Google Play Store. The results reveal that there is still a very significant gap between what app providers and third-party recipients do in practice and what is intended by the GDPR. A substantial 56% of analysed apps are potentially non-compliant with the GDPR cross-border transfer requirements., Comment: Author's copy of the submitted manuscript

This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and Harmonious Data Augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intra-sample and inter-sample data transformations that can be used in harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. {For instance, in the more challenging and realistic evaluation scenario with WTF-PAD based defense, our HDA method surpasses the previous state-of-the-art results by more than 4% in absolute classification accuracy in the 20-shot learning case., Comment: 10 pages, 3 figures

Tor and i2p networks are two of the most popular darknets. Both darknets have become an area of illegal activities highlighting the necessity to study and analyze them to identify and report illegal content to Law Enforcement Agencies (LEAs). This paper analyzes the connections between the Tor network and the i2p network. We created the first dataset that combines information from Tor and i2p networks. The dataset contains more than 49k darknet services. The process of building and analyzing the dataset shows that it is not possible to explore one of the networks without considering the other. Both networks work as an ecosystem and there are clear paths between them. Using graph analysis, we also identified the most relevant domains, the prominent types of services in each network, and their relations. Findings are relevant to LEAs and researchers aiming to crawl and investigate i2p and Tor networks.

Research ethics in Information and Communications Technology has seen a resurgence in popularity in recent years. Although a number of general ethics standards have been issued, cyber security specifically has yet to see one. Furthermore, such standards are often abstract, lacking in guidance on specific practices. In this paper we compare peer-reviewed ethical analyses of condemned research papers to analyses derived from a knowledge base (KB) of concrete cyber security research ethics best practices. The KB we employ was compiled in prior work from a large random survey of research papers. We demonstrate preliminary evidence that such a KB can be used to yield comparable or more extensive ethical analyses of published cyber security research than expert application of standards like the Menlo Report. We extend the ethical analyses of the reviewed manuscripts, and calculate measures of the efficiency with which the expert versus KB methods yield ethical insights., Comment: 9 pages, To appear in Computer Security Symposium 2020 (CSS 2020). Readers viewing this document after 10/19/2020 please refer to the most recent version on arXiv.org. Proceedings to be made available at: https://www.iwsec.org/css/2020/proceedings.html

The growing demand for computer security and the cyberization trend are hallmarks of the 21st century. The rise in cyber-crime, digital currency, e-governance, and more, is well met by a corresponding recent jump in investment in new technology for securing computers around the globe. Recently, business and government sectors have begun to focus efforts on comprehensive cyber security solutions. With this growth has emerged the need for greater methods of collabo-ration and measurement of security. Despite all these efforts, this need has not been met, and there is still too little cross-disciplinary collaboration in the realm of computer security. This paper reviews the new trends in cyber security research, their contributions, and some identifiable limitations. We argue that these limitations are due largely to the absence of co-operation required to address a problem that is clearly multifaceted. We then identify a need for further standardization of terminology in computer security and propose guidelines for the global Internet multistakeholder community to consider when crafting such standards. We also assess the viability of some specific terms, including whether cyber should be used as a separate word when it is a descriptor (e.g. cyber security or cybersecurity), and conclude with recommendations for writing future papers on cyber security or the broader new field of all things relating to cyberspace, which has recently been dubbed Cybermatics, a term we also examine and propose alternatives to, like Cyber or Cybernomics. By furthering the effort of standardizing cyber security terminology, this paper lays groundwork for cross-disciplinary collaboration, agreement between technical and nontechnical stakeholders, and the drafting of universal Internet governance laws., Comment: Accepted version of IEEE Access paper published under the Open Access Publishing agreement

Cybersecurity tools are increasingly automated with artificial intelligent (AI) capabilities to match the exponential scale of attacks, compensate for the relatively slower rate of training new cybersecurity talents, and improve of the accuracy and performance of both tools and users. However, the safe and appropriate usage of autonomous cyber attack tools - especially at the development stages for these tools - is still largely an unaddressed gap. Our survey of current literature and tools showed that most of the existing cyber range designs are mostly using manual tools and have not considered augmenting automated tools or the potential security issues caused by the tools. In other words, there is still room for a novel cyber range design which allow security researchers to safely deploy autonomous tools and perform automated tool testing if needed. In this paper, we introduce Pandora, a safe testing environment which allows security researchers and cyber range users to perform experiments on automated cyber attack tools that may have strong potential of usage and at the same time, a strong potential for risks. Unlike existing testbeds and cyber ranges which have direct compatibility with enterprise computer systems and the potential for risk propagation across the enterprise network, our test system is intentionally designed to be incompatible with enterprise real-world computing systems to reduce the risk of attack propagation into actual infrastructure. Our design also provides a tool to convert in-development automated cyber attack tools into to executable test binaries for validation and usage realistic enterprise system environments if required. Our experiments tested automated attack tools on our proposed system to validate the usability of our proposed environment. Our experiments also proved the safety of our environment by compatibility testing using simple malicious code., Comment: 20 pages, 10 figures, to be published in SSCC 2020

The continuing use of proprietary cryptography in embedded systems across many industry verticals, from physical access control systems and telecommunications to machine-to-machine authentication, presents a significant obstacle to black-box security-evaluation efforts. In-depth security analysis requires locating and classifying the algorithm in often very large binary images, thus rendering manual inspection, even when aided by heuristics, time consuming. In this paper, we present a novel approach to automate the identification and classification of (proprietary) cryptographic primitives within binary code. Our approach is based on Data Flow Graph (DFG) isomorphism, previously proposed by Lestringant et al. Unfortunately, their DFG isomorphism approach is limited to known primitives only, and relies on heuristics for selecting code fragments for analysis. By combining the said approach with symbolic execution, we overcome all limitations of their work, and are able to extend the analysis into the domain of unknown, proprietary cryptographic primitives. To demonstrate that our proposal is practical, we develop various signatures, each targeted at a distinct class of cryptographic primitives, and present experimental evaluations for each of them on a set of binaries, both publicly available (and thus providing reproducible results), and proprietary ones. Lastly, we provide a free and open-source implementation of our approach, called Where's Crypto?, in the form of a plug-in for the popular IDA disassembler., Comment: A proof-of-concept implementation can be found at https://github.com/wheres-crypto/wheres-crypto

Deep Neural Networks (DNNs) models become one of the most valuable enterprise assets due to their critical roles in all aspects of applications. With the trend of privatization deployment of DNN models, the data leakage of the DNN models is becoming increasingly serious and widespread. All existing model-extraction attacks can only leak parts of targeted DNN models with low accuracy or high overhead. In this paper, we first identify a new attack surface -- unencrypted PCIe traffic, to leak DNN models. Based on this new attack surface, we propose a novel model-extraction attack, namely Hermes Attack, which is the first attack to fully steal the whole victim DNN model. The stolen DNN models have the same hyper-parameters, parameters, and semantically identical architecture as the original ones. It is challenging due to the closed-source CUDA runtime, driver, and GPU internals, as well as the undocumented data structures and the loss of some critical semantics in the PCIe traffic. Additionally, there are millions of PCIe packets with numerous noises and chaos orders. Our Hermes Attack addresses these issues by huge reverse engineering efforts and reliable semantic reconstruction, as well as skillful packet selection and order correction. We implement a prototype of the Hermes Attack, and evaluate two sequential DNN models (i.e., MINIST and VGG) and one consequential DNN model (i.e., ResNet) on three NVIDIA GPU platforms, i.e., NVIDIA Geforce GT 730, NVIDIA Geforce GTX 1080 Ti, and NVIDIA Geforce RTX 2080 Ti. The evaluation results indicate that our scheme is able to efficiently and completely reconstruct ALL of them with making inferences on any one image. Evaluated with Cifar10 test dataset that contains 10,000 images, the experiment results show that the stolen models have the same inference accuracy as the original ones (i.e., lossless inference accuracy)., Comment: The paper will appear in Usenix Security Symposium 2021

When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluctant to relinquish control of private information to third parties. Privacy-preserving techniques distribute computation in order to ensure that data remains in the control of the owner while learning takes place. However, architectures distributed amongst multiple agents introduce an entirely new set of security and trust complications. These include data poisoning and model theft. This paper outlines a distributed infrastructure which is used to facilitate peer-to-peer trust between distributed agents; collaboratively performing a privacy-preserving workflow. Our outlined prototype sets industry gatekeepers and governance bodies as credential issuers. Before participating in the distributed learning workflow, malicious actors must first negotiate valid credentials. We detail a proof of concept using Hyperledger Aries, Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs) to establish a distributed trust architecture during a privacy-preserving machine learning experiment. Specifically, we utilise secure and authenticated DID communication channels in order to facilitate a federated learning workflow related to mental health care data., Comment: To be published in the proceedings of the 17th International Conference on Trust, Privacy and Security in Digital Business - TrustBus2020

In this work we analyse five popular commercial password managers for security vulnerabilities. Our analysis is twofold. First, we compile a list of previously disclosed vulnerabilities through a comprehensive review of the academic and non-academic sources and test each password manager against all the previously disclosed vulnerabilities. We find a mixed picture of fixed and persisting vulnerabilities. Then we carry out systematic functionality tests on the considered password managers and find four new vulnerabilities. Notably, one of the new vulnerabilities we identified allows a malicious app to impersonate a legitimate app to two out of five widely-used password managers we tested and as a result steal the user's password for the targeted service. We implement a proof-of-concept attack to show the feasibility of this vulnerability in a real-life scenario. Finally, we report and reflect on our experience of responsible disclosure of the newly discovered vulnerabilities to the corresponding password manager vendors., Comment: This is an accepted manuscript to appear in the proceedings of the 35th Int'l Conf. on ICT Systems Security & Privacy Protection (IFIP SEC 2020), Maribor, Slovenia