Reviewerly/exHarmony huggingface dataset:
Multi-Attribute Decision Making-based Trust Score Calculation in Trust Management in IoT

The proliferation of IoT networks across various sectors necessitates robust Trust Management mechanisms for secure and reliable operations. This paper proposes a Multi-Attribute Decision Making (MADM)-based approach for trust score calculation in IoT Trust Management. This solution addresses limitations of existing methods by considering multiple attributes and providing a comprehensive evaluation of trustworthiness. The methodology computes a device's trust score by integrating factors such as Cyber Risk, Ease of Access, and Security Level using a weighted sum-based calculation. The Analytical Hierarchy Process (AHP) to determine the factors’ weights is utilized, contributing a novel approach to IoT Trust Management. Furthermore, this approach includes dynamic trust score updates throughout the device's lifetime, accommodating changes in the device's Cyber Risk for accurate trust assessment. A trust score penalization mechanism for devices below a predefined threshold is also introduced, enabling prompt risk mitigation. A simulated assessment, considering varying numbers of IoT devices, evaluates the effectiveness of the proposed methodology. By addressing limitations and introducing innovative components, the proposed MADM-based approach enhances security, reliability, and overall performance of IoT networks. This research advances trust management in IoT and provides valuable insights for developing secure and trustworthy IoT ecosystems.
STIXnet: A Novel and Modular Solution for Extracting All STIX Objects in CTI Reports

The automatic extraction of information from Cyber Threat Intelligence (CTI) reports is crucial in risk management. The increased frequency of the publications of these reports has led researchers to develop new systems for automatically recovering different types of entities and relations from textual data. Most state-of-the-art models leverage Natural Language Processing (NLP) techniques, which perform greatly in extracting a few types of entities at a time but cannot detect heterogeneous data or their relations. Furthermore, several paradigms, such as STIX, have become de facto standards in the CTI community and dictate a formal categorization of different entities and relations to enable organizations to share data consistently.
An inclusive Lifecycle Approach for IoT Devices Trust and Identity Management

ERATOSTHENES is an EC, co-funded, research project strongly considering modern security challenges in the domain of Internet of Things in mind of their huge penetration into our day to day lives. There are a series of recent challenges that recently have been converted into obstacles or risk points that could block the secure operation of IoT networks in all day to day activities, from home to office, to leisure and security. These include examples such as the highly increased number of connected devices (at all network levels) that are on top forming inhomogeneous networks and systems of systems. Different vendor characteristics further increase the attack surface that is expected to further rise in the upcoming years. Such, highly critical, characteristics, dramatically increase the needs for confidentiality access control, user and things’ privacy, devices’ trustworthiness and compliance that require lifecycle considerations. The ERATOSTHENES project orchestrates a novel distributed, automated, auditable, yet privacy-respectful, Trust and Identity Management Framework and Reference Architecture with the ultimate scope to dynamically and holistically manage IoT devices in a lifecycle approach, strengthening trust, identities, and resilience in the entire IoT ecosystem while supporting the enforcement of the NIS directive, GDPR and Cybersecurity Act. This publication describes the ERATOSTHENES technical concept and reference architecture as well as design considerations, architecture characteristics, connectivity and interoperability.
Key Management Systems for Large-Scale Quantum Key Distribution Networks

The Key Management System (KMS) is an important component in scaling up from link-to-link key generation to large key distribution networks. In this work we provide an overview of a KMS in the context of Quantum Key Distribution Networks (QKDN) and give a thorough summary of the functionality of a KMS in such an application. Beyond classical QKDNs, we discuss Post Quantum Cryptography (PQC) hybridization techniques at the KMS level. These methods add an additional layer of security against quantum computer driven attacks. We also discuss selected topics regarding the development, deployment and operation of components for such security infrastructure. In addition, relevant standards in the realm of Quantum Key Distribution (QKD) are outlined and analyzed. As some of the necessary interfaces have not been standardized, namely the interface between two KMS instances and the interface between the KMS and the Software Defined Network (SDN) Agent, we propose APIs for these two cases. The design of the interface between the KMS and QKD modules is discussed and, considering their resource constraints, a push mode for the ETSI GS QKD 004 standard is proposed. Finally, implementation details of a prototype KMS are outlined and trade-offs are discussed.
Confidential Quantum Computing

Quantum computing is becoming more accessible with increasing numbers of quantum platforms. The confidentiality and integrity of data and algorithms running on these systems are important assets that need to be protected from untrusted parties. Previous approaches focus on the encryption of individual sub-areas, often using at least hybrid clients, and do not take the entire path from the classical client via a platform to the quantum computing hardware into consideration. Based on the classification of quantum algorithms we show the assets worth protecting, evolve the data flow on third-party quantum hardware and quantum computing platforms, and propose a concept architecture addressing confidentiality and integrity of processed data and code. Our approach shows that confidentiality can already be achieved for data with classical clients, while code confidentiality remains an open question. Our approach covers integrity for most complexity classes.
A hybrid anonymization pipeline to improve the privacy-utility balance in sensitive datasets for ML purposes

The modern world is data-driven. Businesses increasingly take strategic decisions based on customer data, and companies are founded with a sole focus of performing machine-learning driven data analytics for third parties. External data sources containing sensitive records are often required to build qualitative machine learning models and, hence, perform accurate and meaningful predictions. However, exchanging sensitive datasets is no sinecure. Personal data must be managed according to privacy regulation. Similarly, loss of strategic data can negatively impact the competitiveness of a company. In both cases, dataset anonymization can overcome the aforementioned obstacles.
Automated Side-Channel Attacks using Black-Box Neural Architecture Search

The application of convolutional neural networks (CNNs) to break cryptographic systems through hardware side-channels facilitated rapid and adaptable attacks on cryptographic systems like smart cards and Trusted Platform Modules (TPMs). However, current approaches rely on manually designed CNN architectures by domain experts, which are time-consuming and impractical for attacking new systems.
Simulated environment for multiparty quantum digital signature across the network

With the quantum computational paradigm rapidly developing across the world, new vulnerabilities on currently deployed cryptography protocols are to be taken in consideration. A possible answer to said vulnerabilities is using quantum technologies on cryptography. For a quantum-assisted cryptography protocol, key pairs can be generated via quantum state measurements. This protocol is known as quantum key distribution, of extended use for providing security against any algorithm-based attack to the keys. Ensured the key protection against eavesdroppers, a QKD-assisted protocol can focus its security analysis on party behaviour. Depending on the protocol rules and steps to follow, users can attempt attacks on it with the purpose of causing disagreement or misunderstanding between parties. In this work, we present a simulation environment for a hybrid, quantum-assisted digital signature (QDS) protocol. The configurations available for the simulated network as well as the digital signature protocol make it possible to design specific communication scenarios, such as attacks on the message integrity, forging and repudiation attempts or noisy communication across the quantum channels.
VeriDevOps Software Methodology: Security Verification and Validation for DevOps Practices

VeriDevOps offers a methodology and a set of integrated mechanisms that significantly improve automation in DevOps to protect systems at operations time and prevent security issues at development time by (1) specifying security requirements, (2) generating trace monitors, (3) locating root causes of vulnerabilities, and (4) identifying security flaws in code and designs. This paper presents a methodology that enhances productivity and enables the continuous integration/delivery of trustworthy systems. We outline the methodology, its application to relevant scenarios, and offer recommendations for engineers and managers adopting the VeriDevOps approach. Practitioners applying the VeriDevOps methodology should include security modeling in the DevOps process, integrate security verification throughout all stages, utilize automated test generation tools for security requirements, and implement a comprehensive security monitoring system, with regular review and update procedures to maintain relevance and effectiveness.
Mitigating Intersection Attacks in Anonymous Microblogging

Anonymous microblogging systems are known to be vulnerable to intersection attacks due to network churn. An adversary that monitors all communications can leverage the churn to learn who is publishing what with increasing confidence over time. In this paper, we propose a protocol for mitigating intersection attacks in anonymous microblogging systems by grouping users into anonymity sets based on similarities in their publishing behavior. The protocol provides a configurable communication schedule for users in each set to manage the inevitable trade-off between latency and bandwidth overhead. In our evaluation, we use real-world datasets from two popular microblogging platforms, Twitter and Reddit, to simulate user publishing behavior. The results demonstrate that the protocol can protect users against intersection attacks at low bandwidth overhead when the users adhere to communication schedules. In addition, the protocol can sustain a slow degradation in the size of the anonymity set over time under various churn rates.
Real-world actor-based image steganalysis via classifier inconsistency detection

In this paper, we propose a robust method for detecting guilty actors in image steganography while effectively addressing the Cover Source Mismatch (CSM) problem, which arises when classifying images from one source using a classifier trained on images from another source. Designed for an actor-based scenario, our method combines the use of Detection of Classifier Inconsistencies (DCI) prediction with EfficientNet neural networks for feature extraction, and a Gradient Boosting Machine for the final classification. The proposed approach successfully determines whether an actor is innocent or guilty, or if they should be discarded due to excessive CSM. We show that the method remains reliable even in scenarios with high CSM, consistently achieving accuracy above 80% and outperforming the baseline method. This novel approach contributes to the field of steganalysis by offering a practical and efficient solution for handling CSM and detecting guilty actors in real-world applications.
Conversational Search with Random Walks over Entity Graphs

The entities that emerge during a conversation can be used to model topics, but not all entities are equally useful for this task. Modeling the conversation with entity graphs and predicting each entity's centrality in the conversation provides additional information that improves the retrieval of answer passages for the current question. Experiments show that using random walks to estimate entity centrality on conversation entity graphs improves top precision answer passage ranking over competitive transformer-based baselines.
A Generic IoT Quantum-Safe Watchdog Timer Protocol

This paper presents a quantum-safe watchdog timer protocol designed and implemented using various quantum-safe digital signature algorithms. The protocol is specifically tailored to be used in the context of the Internet of Things (IoT) to address the security risks posed by quantum computing to classical protocols. Our approach replaces the classical protocol with a quantum-safe watchdog timer protocol, which ensures that an IoT device’s communication channels remain secure from adversarial attacks.
Simple and efficient threshold changeable secret sharing

Shamir's (t, n) threshold secret sharing (SS) is one of the most important cryptographic primitives in various security applications designed to protect highly sensitive information, such as intellectual property and network security. A dealer in a (t, n) threshold SS divides the secret into n shares such that with t or more than t of these shares, the secret can be retrieved; but with less than t of these shares, no information of the secret can be revealed. In order to adapt the dynamic environment in various applications, the threshold changeable SS (TCSS) allows the threshold of the original secret to be dynamically adjusted. Note that Shamir's original SS is unable to achieve this property. In the literature, most existing TCSS schemes are suffering from limitations that either it requires a trusted dealer to generate and distribute new shares, or requires interactions among shareholders to generate new shares. In this paper, we propose a novel idea to construct a dealer-free and non-interactive TCSS based on pairwise keys between users. Research papers related to pairwise key distribution schemes have been published over last 40 years and it has become one of fundamental tools used in design of cryptographic solutions. First, we show that our idea can be used to construct a scheme for anonymously releasing data and a secure multiparty computation scheme. Both schemes are very simple and security analysis can be understood easily. Second, we demonstrate that a TCSS can also be designed based on same approach. Our design of TCSS is novel and it can be applied to convert any existing SS into a TCSS.
HTTP/2 Attacks Generation using 5Greplay

5G networks become increasingly pervasive, ensuring the robustness and integrity of network functions. The adoption of HTTP/2 in 5G core functions brings notable performance benefits but also introduces potential security risks. By analyzing HTTP/2 related threats, this research aims to shed light on the security challenges faced by 5G networks. The paper proposes effective security testing methodologies using an open-source solution called 5Greplay to detect these security breaches, enabling network operators to protect against potential attacks, safeguard user privacy, and ensure uninterrupted service continuity. By addressing the specific concerns of HTTP/2 related threats, this research contributes to the overall security posture of 5G network functions and provides valuable insights for the secure deployment of 5G networks in an evolving threat landscape.
An Analysis of PhotoDNA

PhotoDNA is a popular hash used to combat CSAM. So far, only limited information has been provided about this hash in terms of its performance. In this paper, we provide an overview of robustness and false positives, as well as some basic observations about its structure. We show that it is robust against typical image processing such as lossy compression. On the other hand, it is only of limited robustness against cropping. We also give some suggestions for improving the algorithm or its use.
Actions Speak Louder Than Passwords: Dynamic Identity for Machine-to-Machine Communication

Machine-to-Machine (M2M) communication is communication between computers without a human user involved. This is a very common paradigm whenever automated tasks are executed routinely, e.g., backup data to a cloud storage, update a local database cache, fetch the latest updates for software, etc. One challenge in this setting is that the credentials to establish secure connections between machines during execution must be available to the machines without any human interaction. Typically that means the credentials must reside on the machine itself, in the form of a secret such as a password, API key, single sign-on token, etc. In practice the secret is often embedded directly into an automatically executed script, but regardless it needs to be stored either in the clear or encrypted with another secret that is available to the machine during execution. This exposes the credentials to anyone who can gain access to the machine. In this paper we present ActionID, a scheme that mitigates the problem of credential exposure by making a desired sequence of actions for execution as part of the machine’s identity. This way, even if the credentials are exposed, they are only temporarily valid for one particular action sequence that cannot be changed for future executions. We introduce a trusted third party who issues new identities, validates new action requests, and acts as a centralised location for managing access control policies for an arbitrary number of clients and servers. In addition to yielding strong security guarantees, it also simplifies the management of complex access control for an organisation. We present detailed protocols for ActionID, along with a thorough security analysis. We implement ActionID as a Python library to show the ease of integration into existing applications, and to demonstrate the performance of the scheme, which is on par with SSH.
Retrieving Webpages Using Online Discussions

Online discussions are a ubiquitous aspect of everyday life. An Internet user who interacts with an online discussion may benefit from seeing hyperlinks to webpages relevant to the discussion because the relevant webpages can provide added context, act as citations for background sources, or condense information so that conversations can proceed seamlessly at a high level. In this paper, we propose and study a new task of retrieving relevant webpages given an online discussion. We frame the task as a novel retrieval problem where we treat a sequence of comments in an online discussion as a query and use such a query to retrieve relevant webpages. We construct a new data set using Reddit, an online discussion forum, to study this new problem. We explore and evaluate multiple representative retrieval methods to examine their effectiveness for solving this new problem. We also propose to leverage the comments that contain hyperlinks as training data to enable supervised learning and further improve retrieval performance. We find that results using modern retrieval methods are promising and that leveraging comments with hyperlinks as training data can further improve performance. We release our data set and code to enable additional research in this direction.
Assessment of the Quality of Topic Models for Information Retrieval Applications

Topic modelling is an approach to generation of descriptions of document collections as a set of topics where each has a distinct theme and documents are a blend of topics. It has been applied to retrieval in a range of ways, but there has been little prior work on measurement of whether the topics are descriptive in this context. Moreover, existing methods for assessment of topic quality do not consider how well individual documents are described. To address this issue we propose a new measure of topic quality, which we call specificity; the basis of this measure is the extent to which individual documents are described by a limited number of topics. We also propose a new experimental protocol for validating topic-quality measures, a 'noise dial' that quantifies the extent to which the measure's scores are altered as the topics are degraded by addition of noise. The principle of the mechanism is that a meaningful measure should produce low scores if the 'topics' are essentially random. We show that specificity is at least as effective as existing measures of topic quality and does not require external resources. While other measures relate only to topics, not to documents, we further show that specificity correlates to the extent to which topic models are informative in the retrieval process.
A Method for Improving AlexNet’s Performance in The Area of Facial Expressions Recognition

Facial Expression Recognition (FER) through digital images has undergone significant development in line with the development of computer vision technology and artificial intelligence. Facial expression recognition that has utilized deep learning shows promising results. By using deep learning, classifying millions of digital images can be easier and more accurate. However, misclassification of facial expressions sometimes still occurs. This paper proposes a method for improving the AlexNet model for application in the FER area. Some pre-processing procedures were performed on the image dataset, including resizing the image size to 227x227, converting the image to RGB (Red Blue Green) format, adjusting the contrast level of the image using CLAHE (Contrast Limited Adaptive Histogram Equalization), and augmenting by cropping the dataset image. Meanwhile, fine-tuning the AlexNet model was done by changing the ReLU activation function to Leaky ReLU, input normalization from cross channel to batch normalization, and two dropout values (from 0.5 to 0.3 and 0), and changing the number of output classifications from 1000 to 7. The experimental results show that the proposed method enhances standard AlexNet’s performance by improving its accuracy to 24.82% on the CK+ dataset and 20.05% on the KDEF dataset. There is no misclassification of facial expressions when using the proposed method, as it occurs when using the standard AlexNet model.
Liver Tumor Detection and Classification Using GWO-ELM Model

Multimodal intelligence-based systems for medical analytics and decision-making are crucial in the healthcare industry. One of the most common types of cancer is liver cancer, and early detection is essential for successful treatment. The severity of irregular tumor forms varies depending on the malignancy stage and the tumor type. Identifying the liver and subsequent tumor segmentation are the two primary stages of tumor segmentation in the liver. In addition to detecting cancers from publically available data of liver scans, this research offers a novel deep learning-based segmentation with a grey wolf Optimization-Extreme Learning Model approach that exhibits excellent efficiency in results. To improve the efficacy of the liver tumor detection system, this work applies the GWO-ELM classifier and Haar wavelet transform. It uses one of the most widely used feature extractions. The GWO-ELM acts like a Support Vector Machine with a Neural Network structure and can solve multi and binary classification problems. In contrast, the Haar wavelet transform can extract the most pertinent features with low dimensionality. As a result, the GWO-ELM classifier and Haar wavelet transform characteristics are used to provide a useful method for classifying and extracting features from liver tumors. According to the results, the proposed GWO-ELM model performed very well, achieving an accuracy of 99.41 % for a multi-class dataset. This reveals that the GWO-ELM and Haar wavelet transform is a robust classifier for identifying liver tumors and might be used to handle various types of image data.