Reviewerly/exHarmony huggingface dataset:
Improved large margin classifier via bounding hyperellipsoid

Support vector machine (SVM) is an excellent pattern recognition method. Many experiments have shown that SVM can achieve a generalization performance gain by carrying out it in the feature transformation space. Nevertheless, the theoretical foundation behind this phenomenon is presently lack of deep investigation. In the paper, we first give and prove a vital theoretical conclusion that SVM in the feature transformation space can obtain a lower radius-margin bound than one in the feature original space. This means that the performance of SVM can be improved by feature transformation since the radius-margin bound is directly associated with the generalization capacity. Based on this theoretical support, we further propose a novel method called covering-hyperellipsoid-constrained large margin classifier (CHC-LMC). The key characteristic of CHC-LMC is that it jointly learns the minimum bounding hyperellipse and the used classifier by directly minimizing the radius-margin bound in the transformation space, and so embodies the structural risk minimization principle. We develop the linear and nonlinear versions of CHC-LMC and employ an alternate optimization strategy to deal with the corresponding optimization problems. Finally, comprehensive experiments are conducted to verify the validity of CHC-LMC and evaluate the generalization performance by comparing it with the competing methods.
A New Fast and Side-Channel Resistant AES Hardware Architecture

In this paper we present a novel architecture for a high-speed AES crypto core which is resistant against first-order side channel attacks; our design combines Boolean masking techniques, logic obfuscation applied to a composite-fields Sbox implementation, and interleaved computations. This approach reaches a remarkable trade-off between size, performance and need for randomness, showing to be competitive against designs based on threshold approaches and automated masking. We conduct simulation-based and FPGA-based security evaluations, which are shown to corroborate our security objectives.
Enhancing Cyber-Resilience in Self-Healing Cyber-Physical Systems with Implicit Guarantees

Self-Healing Cyber-Physical Systems (SH-CPS) effectively recover from system perceived failures without human intervention. They ensure a level of resilience and tolerance to unforeseen situations that arise from intrinsic system and component degradation, errors, or malicious attacks. Implicit redundancy can be exploited in SH -CPS to structurally adapt without the need to explicitly duplicate components. However, implicitly redundant components do not guarantee the same level of dependability as the primary component used to provide for a given function. Additional processes are needed to restore critical system functionalities as desired. This work introduces implicit guarantees to ensure the dependability of implicitly redundant components and processes. Implicit guarantees can be obtained through inheritance and decomposition. Therefore, a level of dependability can be guaranteed in SH -CPS after adaptation and recovery while complying with requirements. We demonstrate compliance with the requirement guarantees while ensuring resilience in SH-CPS.
Randomized Addressing Countermeasures are Inefficient Against Address-Bit SCA

The resistance of cryptographic implementations against SCA attacks is highly important if devices are physically accessible. The vulnerability of public key approaches to address-bit attacks is not solved yet. Different randomization approaches proposed in the literature as countermeasures have been successfully attacked in the past. In contrast to these countermeasures the low-cost countermeasure presented in [1] was not yet reported as successfully attacked. We present our idea of how the processed scalar can be revealed even when this countermeasure is implemented. We explain how the well-known address randomization countermeasures [1] and [2] can be broken attacking a single trace.
IoT Forensics: Analysis of Ajax Systems' mobile app for the end user

Security appliances that protect modern homes are ubiquitous IoT products. Due to their popularity, such appliances may witness incidents occurring to the IoT security systems they form. These systems are remotely configured and monitored by the end users through mobile applications. Ajax Systems is a manufacturer of such IoT devices which provides a variety of applications that allow remote configuration and monitoring of its products. Research regarding digital forensics of such applications is limited yet this unexplored piece of evidence may hide vital information for a number of investigative questions. In this paper the Ajax Systems' mobile application for the end user is thoroughly analyzed in both Android and iOS operating systems in pursue of evidentiary data that could reside within. Exploiting findings of this study authors developed a Python utility, namely Ajax Systems Log Parser, that can assist investigators with the examination of some of the artifacts found.
Privacy-preserving Federated Learning System for Fatigue Detection

Context:. Drowsiness affects the driver's cognitive abilities, which are all important for safe driving. Fatigue detection is a critical technique to avoid traffic accidents. Data sharing among vehicles can be used to optimize fatigue detection models and ensure driving safety. However, data privacy issues hinder the sharing process. To tackle these challenges, we propose a Federated Learning (FL) approach for fatigue-driving behavior monitoring. However, in the FL system, the privacy information of the drivers might be leaked. In this paper, we propose to combine the concept of differential privacy (DP) with Federated Learning for the fatigue detection application, in which artificial noise is added to parameters at the drivers' side before aggregating. This approach will ensure the privacy of drivers' data and the convergence of the federated learning algorithms. In this paper, the privacy level in the system is determined in order to achieve a balance between the noise scale and the model's accuracy. In addition, we have evaluated our models resistance against a model inversion attack. The effectiveness of the attack is measured by the Mean Squared Error (MSE) between the reconstructed data point and the training data. The proposed approach, compared to the non-DP case, has a 6% accuracy loss while decreasing the effectiveness of the attacks by increasing the MSE from 5.0 to 7.0, so a balance between accuracy and noise scale is achieved.
Minimizing Software-Rooted Risk through Library Implementation Selection

In contemporary Internet of Things (IoT) systems, complex software artifacts are deployed to realize the required functionalities. The business logic of these software artifacts is uniquely composed through code that is customly developed according to the requirements, while all software artifacts utilize libraries that implement generic functionalities, which are needed in the context of the realized operations. Libraries, however, often entail vulnerabilities, which may be exploited by threat agents to attack the system. In many cases, the functionality required by an application is realized by a number of alternative libraries, with each library having its own list of vulnerabilities, while differentiations in other non-functional properties (e.g. execution efficiency, memory footprint etc.) may also be present. In this paper, we present an approach for automating the task of minimizing the risk level of IoT systems that is owing to the vulnerabilities of libraries required by software artifacts. The proposed approach exploits knowledge on which libraries provide equivalent implementations of the same functionalities, and automatically assesses the risk level of candidate library combinations and finally selects the library configuration exhibiting the minimum risk level to bundle into the executable software artifact. Additionally, the risk level of candidate implementations is constantly monitored for new vulnerability identifications or fixes in the implementations, triggering new risk assessments and producing new executables as appropriate. The proposed approach can be used in IoT platform deployment to minimize the software-rooted risk level.
An Open Sharing Method for Digital Heritage Assets Based on Self-Sovereign Identity Technology

To address the issues of ownership and trustworthiness that are prevalent in the flow of Digital Heritage Assets(DHAs), this study proposes a method for DHA open sharing based on self-sovereign identity technology. Through decentralized identifier technology and blockchain technology, the trusted identity registration and verification of a participating subject are realized; through verifiable credential technology, the registration and flow of a DHA are realized; through verifiable credential technology and digital signature technology, the access and trustworthiness verification of a DHA are realized. This method can guarantee the trustworthiness of the identity of a participating subject, maintain the ownership attribution and trustworthiness of a DHA in the flow, and ultimately promote the scope and efficiency of DHA open sharing.
Phishing and Smishing Detection Using Machine Learning

We propose the use of a hybridized machine learning model to detect phishing and smishing - phishing using SMS messages - attacks with the use of several extracted features related to domains, coupled with natural language processing (NLP) trained on actual smishing messages to accurately detect attacks. Moreover, we propose an integration of the detection system with the open-source threat intelligence platform, MISP (Malware Information Sharing Platform). This allows for more effective storage and use of flagged phishing domains. The model was trained and tested using publicly available data as well as data provided by TELUS Corp. The results show an accuracy of 99.40% and an Fl score in excess of 99%.
Evaluation of Applying LDA to Redacted Documents in Security and Safety Analysis

Cyber attacks are often executed by imitating existing attacks and combining them. Using existing vulnerability databases, we have presented a way to semi-automatically determine the presence of vulnerabilities in the design documents of products under development. We have calculated the similarity between documents using the Latent Dirichlet Allocation (LDA) technology and compared the design document of the new product with the vulnerability database. When this comparison processing is conducted by a third party as a service, it may be desirable to not inadvertently disclose a part of the design document of the new product to the third party. In this study, we used the LDA technique to experimentally verify that the calculated similarity value does not deteriorate even when a portion of the design document is encrypted or obfuscated. In conclusion, we discovered no substantial difference in similarity with the original document; however, there are changes in numerical values depending on the words to be encrypted/obfuscated. In particular, the degradation of similarity is very small when the version number is encrypted/obfuscated.
Key Update for the IoT Security Standard OSCORE

The standard Constrained Application Protocol (CoAP) is a lightweight, web-transfer protocol based on the REST paradigm and specifically suitable for constrained devices and the Internet-of-Things. Object Security for Constrained RESTful Environment (OSCORE) is a standard, lightweight security protocol that provides end-to-end protection of CoAP messages. A number of methods exist for managing keying material for OSCORE, as to its establishment and update. This paper provides a detailed comparison of such methods, in terms of their features, limitations and security properties. Also, it especially considers the new key update protocol KUDOS, for which it provides a more extended discussion about its features and mechanics, as well as a formal verification of its security properties.
Cyber Resilience, Societal Situational Awareness for SME

In our paper, we describe the landscape that has led to the realization from the nineties of the last century that cyber is a social good: “Cyber is a social good, “said Cybersecurity and Infrastructure Security Agency Director Jen Easterly. “It's about societal resilience. And my last message (at CES 2023) is that we need to fundamentally change the relationship between government and industry.” This realization is build upon the belief that trust can be reinvented on three levels: that of data chains in devices, information chains in the supply chain (can I trust my supplier, my client), and trust in the realness, the ‘reality’ level of the contexts evoked by these chains in an age of deep fakes, Chat GPT and the Metaverse. We argue that there is a crisis of trust on all levels, a crisis which inevitability is part of the digital turn itself. As we move, as Mark Weiser wrote in his seminal text The Computer for the 21st century, to a form of computing that will disappear into “the fabric of everyday life”, and will only succeed as a success when it disappears fully from the experience of humans. It is the infrastructure itself that acquires a new layer and becomes ‘smart’. It has become an integral part of society that was before governed by rules of the kinetic realities of the world. These rules were built with certain threats in mind. The hybrid reality, layers of analogue/kinetic that interact sometimes, leads to new everyday practices that become social behavior. Leveling new threats then indeed becomes a social good. We argue that this is especially the case for small and medium-sized enterprises (SMEs), who by forming 99% of all business in Europe, not only pose a large fragmented threat vector, but also they are fighting cybercrime in isolation. We purpose a novel solution to exchange cybersecurity risk information with context among SMEs in a peer to peer mesh network. Additionally, a graph based risk analysis and prioritization method which takes into account the context information of assets and their environment.
A Taxonomic Survey of Model Extraction Attacks

A model extraction attack aims to clone a machine learning target model deployed in the cloud solely by querying the target in a black-box manner. Once a clone is obtained it is possible to launch further attacks with the aid of the local model. In this survey, we analyze existing approaches and present a taxonomic overview of this field based on several important aspects that affect attack efficiency and performance. We present both early works and recently explored directions. We conclude with an analysis of future directions based on recent developments in machine learning methodology.
Mitigating Membership Inference Attacks in Machine Learning as a Service

The increasing use of Machine Learning as a Service (MLaaS) has raised privacy and security issues due to membership inference attacks. These attacks can extract sensitive information such as the identification of an individual's participation in a training dataset, by exploiting a binary classifier with limited access. The attacks exploit weaknesses in the decision boundaries of the model, and can lead to the disclosure of private information. However, the current defenses against such attacks, such as those based on differential privacy or regularization, have significant limitations. Therefore, further research is needed to develop effective defenses that maintain the utility of machine learning models while providing formal guarantees, even in the presence of strategic adversaries. In this paper, we focus on mitigating the risks of black-box inference attacks against machine learning models as a service. We propose a defense mechanism that brings the attacker's inference classifier into a zone of uncertainty, rendering it unable to classify a data point as a member or non-member. This mechanism takes into account the attacker's behavior by modeling the interaction between defense and attacker as a game, considering potential gains in confidentiality and costs. Our experiments on two datasets demonstrate the effectiveness of our approach in mitigating membership inference attacks. Furthermore, our defense mechanism outperforms existing defenses by offering superior privacy-utility-performance tradeoffs.
Breaking AES-128: Machine Learning-Based SCA Under Different Scenarios and Devices

Machine learning-based side-channel attacks (MLSCAs) have demonstrated the capability to extract secret keys from AES by learning the correlation between leakages from power traces or timing of AES execution. Previous work has focused on unmasked AES, the captured power traces for profiling and testing have been collected from the same device, and they are primarily implemented on microcontrollers. In this paper, we present a comprehensive MLSCA that considers both masked and unmasked AES running on software and hardware with a side-channel leakage model under four scenarios involving two target boards (Artix-7 XC7AI00T FPGAs and STM32F415 microcontrollers) and different keys for training and testing the model. Our implementation results indicate that support vector machines outperformed other machine learning techniques on masked software and unmasked software AES with only 4 traces. Long short-term memory networks were found to outperform other techniques on unmasked hardware AES (FPGA) with only 283 power traces.
Understanding Human Factors of Cybersecurity: Drivers of Insider Threats

As the implementation of computer systems has continued to grow, so has the number of employee-driven cyberspace infractions. Although employee malicious behaviors have continued to have detrimental effects on cybersecurity strategies, most studies have focused on technological gaps rather than human risk vulnerabilities. This grounded theory study was designed to identify human factors that drive cyberspace insider threat activities from cybersecurity experts. A literature analysis suggests a need for more qualitative studies to understand human factors of cybersecurity threats. This study's findings provide a framework to connect drivers and factors associated with insider threats through detailed themes. These six drivers lend a deeper understanding of individual, cultural, and technological influences on cyber threats. The findings also introduce connections between employee insider threat activities driven by unwitting, witting, unmalicious, and/or malicious behaviors.
Multi-party Computation for Privacy and Security in Machine Learning: a Practical Review

Machine Learning, particularly Deep Learning, is transforming society in any of its fundamental domains - healthcare, culture, finance, transportation, education, just to mention a few. However Machine Learning suffers from serious weaknesses in privacy and security due to the large amount of data (datasets for training and parameters in trained models) and the probabilistic approximation inherent in any ML function. Multi-Party Computation (MPC) is a family of techniques and tactic with a sound scientific and operative base that can be applied to mitigate some relevant weaknesses of ML. In particular, privacy in training may be assured by MPC with federated learning techniques (these may be considered particular interpretations and implementation of a general MPC method) and also security in training and inference may be enforced by continuous model testing using MPC is a technique that allows multiple parties to evaluate a machine learning model on their private data without revealing it to each other. This brief paper is a practical and essential review on how to use MPC to mitigate privacy and security issues in ML.
Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers

- The rapid evolvement of deepfake creation technologies is seriously threating media information trustworthiness. The consequences impacting targeted individuals and institutions can be dire. In this work, we study the evolutions of deep learning architectures, particularly CNNs and Transformers. We identified eight promising deep learning architectures, designed and developed our deepfake detection models and conducted experiments over well-established deepfake datasets. These datasets included the latest second and third generation deepfake datasets. We evaluated the effectiveness of our developed single model detectors in deepfake detection and cross datasets evaluations. We achieved 88.74%, 99.53%, 97.68%, 99.73% and 92.02% accuracy and 99.95%, 100%, 99.88%, 99.99% and 97.61 % AUC, in the detection of FF++ 2020, Google DFD, Celeb-DF, Deeper Forensics and DFDC deepfakes, respectively. We also identified and showed the unique strengths of CNNs and Transformers models and analysed the observed relationships among the different deepfake datasets, to aid future developments in this area.
BTV2P: Blockchain-based Trust Model for Secure Vehicles and Pedestrians Networks

With the arrival of connected and autonomous vehicles, Vehicle-to-Pedestrian (V2P) communications are promising to facilitate efficient future of mobility on the road by ensuring maximum protection and safety for both drivers and pedestrians. However, this new technology poses new security and privacy challenges that should be taken into account. For instance, a probable malicious node claiming to be a legitimate pedestrian or vehicle within the network can impact the traffic flow, or even cause serious congestion and traffic accidents by broadcasting fake observations or phenomena on the roads. Therefore, it is crucial to identify legitimate vehicles and road users against adversaries pretending to be one. The aim of this paper is to address these issues, by proposing a distributed trust management scheme that relies on blockchain technology and a trust computation approach for efficient and secure management of trust relationships between pedestrians and vehicles in Vehicle-to-Pedestrian (V2P) networks.
A Taxonomy for Tsunami Security Scanner Plugins

Vulnerability scanning tools are essential in detecting systems weaknesses caused by vulnerabilities in their components or wrong configurations. Corporations may use these tools to assess a system in advance and fix its vulnerabilities, thus preventing or mitigating the impact of real attacks. A set of these tools are organized by plugins, each intended to check a specific vulnerability, such as the case of the Tsunami Security Scanner tool released in 2020 by Google. Multiple plugins for this tool were proposed in a community-based approach and thus, it is important for the users and research community to have these plugins in a framework consistently categorized across multiple sources and types. This paper proposes a comprehensive taxonomy for all the 61 plugins available, hierarchically sorted into 2 main categories, 4 categories, 4 subcategories, and 7 types. An analysis and a discussion on statistics by categories and types over time are also provided. The analysis shows that, so far, there are 4 main contributors, being Google, Community, Facebook, and Govtech. The Google source is still the top contributor counting 39 out of 61 plugins and the highest number of plugins available are in the RCE subcategory. The plugins available are mainly focused on critical and high vulnerabilities.