arXiv subjects

Mauro Conti

Publications and source records attributed to Mauro Conti.

At least 55 records · Page 3Linked to original sources

arXiv2025

Discerning Reliable Cyber Threat Indicators for Timely Cyber Threat Intelligence

In today's dynamic cybersecurity landscape, timely and accurate threat intelligence is essential for proactive defense. This study explores the potential of social media platforms as a valuable resource for extracting actionable Indicators of Compromise (IoCs). Utilizing a Convolutional Neural Network (CNN), we achieved an F1-score of 98.80% and a detection rate of 99.65%, filtering vast social media data to identify key IoCs, including IP addresses, URLs, file hashes, domain addresses, and CVE IDs. These indicators are critical for detecting potential threats and vulnerabilities, and their relevance was evaluated using metrics such as correctness, timeliness, and overlap. Our analysis shows that URLs emerged as the most frequently shared IoC, with 48.67% representing valid threats. To further investigate the role of automated accounts in disseminating IoCs, we applied several machine learning models, with XGBoost delivering the highest performance achieving a macro F1-score of 0.814 and a weighted F1-score of 0.925. These findings highlight the growing significance of social media as a reliable source of actionable threat intelligence, offering valuable insights for cybersecurity professionals to stay ahead of emerging threats.

cs.CR↗

arXiv2025

The Hidden Threat in Plain Text: Attacking RAG Data Loaders

Large Language Models (LLMs) have transformed human-machine interaction since ChatGPT's 2022 debut, with Retrieval-Augmented Generation (RAG) emerging as a key framework that enhances LLM outputs by integrating external knowledge. However, RAG's reliance on ingesting external documents introduces new vulnerabilities. This paper exposes a critical security gap at the data loading stage, where malicious actors can stealthily corrupt RAG pipelines by exploiting document ingestion. We propose a taxonomy of 9 knowledge-based poisoning attacks and introduce two novel threat vectors -- Content Obfuscation and Content Injection -- targeting common formats (DOCX, HTML, PDF). Using an automated toolkit implementing 19 stealthy injection techniques, we test five popular data loaders, finding a 74.4% attack success rate across 357 scenarios. We further validate these threats on six end-to-end RAG systems -- including white-box pipelines and black-box services like NotebookLM and OpenAI Assistants -- demonstrating high success rates and critical vulnerabilities that bypass filters and silently compromise output integrity. Our results emphasize the urgent need to secure the document ingestion process in RAG systems against covert content manipulations.

cs.CR↗

arXiv2025

KeTS: Kernel-based Trust Segmentation against Model Poisoning Attacks

Federated Learning (FL) enables multiple users to collaboratively train a global model in a distributed manner without revealing their personal data. However, FL remains vulnerable to model poisoning attacks, where malicious actors inject crafted updates to compromise the global model's accuracy. We propose a novel defense mechanism, Kernel-based Trust Segmentation (KeTS), to counter model poisoning attacks. Unlike existing approaches, KeTS analyzes the evolution of each client's updates and effectively segments malicious clients using Kernel Density Estimation (KDE), even in the presence of benign outliers. We thoroughly evaluate KeTS's performance against the six most effective model poisoning attacks (i.e., Trim-Attack, Krum-Attack, Min-Max attack, Min-Sum attack, and their variants) on four different datasets (i.e., MNIST, Fashion-MNIST, CIFAR-10, and KDD-CUP-1999) and compare its performance with three classical robust schemes (i.e., Krum, Trim-Mean, and Median) and a state-of-the-art defense (i.e., FLTrust). Our results show that KeTS outperforms the existing defenses in every attack setting; beating the best-performing defense by an overall average of >24% (on MNIST), >14% (on Fashion-MNIST), >9% (on CIFAR-10), >11% (on KDD-CUP-1999). A series of further experiments (varying poisoning approaches, attacker population, etc.) reveal the consistent and superior performance of KeTS under diverse conditions. KeTS is a practical solution as it satisfies all three defense objectives (i.e., fidelity, robustness, and efficiency) without imposing additional overhead on the clients. Finally, we also discuss a simple, yet effective extension to KeTS to handle consistent-untargeted (e.g., sign-flipping) attacks as well as targeted attacks (e.g., label-flipping).

cs.CR↗

arXiv2025

Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges

Smart grids are crucial for meeting rising energy demands driven by global population growth and urbanization. By integrating renewable energy sources, they enhance efficiency, reliability, and sustainability. However, ensuring their availability and security requires advanced operational control and safety measures. Although artificial intelligence and machine learning can help assess grid stability, challenges such as data scarcity and cybersecurity threats, particularly adversarial attacks, remain. Data scarcity is a major issue, as obtaining real-world instances of grid instability requires significant expertise, resources, and time. Yet, these instances are critical for testing new research advancements and security mitigations. This paper introduces a novel framework for detecting instability in smart grids using only stable data. It employs a Generative Adversarial Network (GAN) where the generator is designed not to produce near-realistic data but instead to generate Out-Of-Distribution (OOD) samples with respect to the stable class. These OOD samples represent unstable behavior, anomalies, or disturbances that deviate from the stable data distribution. By training exclusively on stable data and exposing the discriminator to OOD samples, our framework learns a robust decision boundary to distinguish stable conditions from any unstable behavior, without requiring unstable data during training. Furthermore, we incorporate an adversarial training layer to enhance resilience against attacks. Evaluated on a real-world dataset, our solution achieves up to 98.1\% accuracy in predicting grid stability and 98.9\% in detecting adversarial attacks. Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.

cs.CR↗

arXiv2025

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?

Adversarial examples are small and often imperceptible perturbations crafted to fool machine learning models. These attacks seriously threaten the reliability of deep neural networks, especially in security-sensitive domains. Evasion attacks, a form of adversarial attack where input is modified at test time to cause misclassification, are particularly insidious due to their transferability: adversarial examples crafted against one model often fool other models as well. This property, known as adversarial transferability, complicates defense strategies since it enables black-box attacks to succeed without direct access to the victim model. While adversarial training is one of the most widely adopted defense mechanisms, its effectiveness is typically evaluated on a narrow and homogeneous population of models. This limitation hinders the generalizability of empirical findings and restricts practical adoption. In this work, we introduce DUMBer, an attack framework built on the foundation of the DUMB (Dataset soUrces, Model architecture, and Balance) methodology, to systematically evaluate the resilience of adversarially trained models. Our testbed spans multiple adversarial training techniques evaluated across three diverse computer vision tasks, using a heterogeneous population of uniquely trained models to reflect real-world deployment variability. Our experimental pipeline comprises over 130k evaluations spanning 13 state-of-the-art attack algorithms, allowing us to capture nuanced behaviors of adversarial training under varying threat models and dataset conditions. Our findings offer practical, actionable insights for AI practitioners, identifying which defenses are most effective based on the model, dataset, and attacker setup.

cs.CR↗

arXiv2025

Elephant in the Room: Dissecting and Reflecting on the Evolution of Online Social Network Research

Billions of individuals engage with Online Social Networks (OSN) daily. The owners of OSN try to meet the demands of their end-users while complying with business necessities. Such necessities may, however, lead to the adoption of restrictive data access policies that hinder research activities from "external" scientists -- who may, in turn, resort to other means (e.g., rely on static datasets) for their studies. Given the abundance of literature on OSN, we -- as academics -- should take a step back and reflect on what we have done so far, after having written thousands of papers on OSN. This is the first paper that provides a holistic outlook to the entire body of research that focused on OSN -- since the seminal work by Acquisti and Gross (2006). First, we search through over 1 million peer-reviewed publications, and derive 13,842 papers that focus on OSN: we organize the metadata of these works in the Minerva-OSN dataset, the first of its kind -- which we publicly release. Next, by analyzing Minerva-OSN, we provide factual evidence elucidating trends and aspects that deserve to be brought to light, such as the predominant focus on Twitter or the difficulty in obtaining OSN data. Finally, as a constructive step to guide future research, we carry out an expert survey (n=50) with established scientists in this field, and coalesce suggestions to improve the status quo such as an increased involvement of OSN owners. Our findings should inspire a reflection to "rescue" research on OSN. Doing so would improve the overall OSN ecosystem, benefiting both their owners and end-users and, hence, our society.

cs.SI↗

arXiv2025

Profiling Electric Vehicles via Early Charging Voltage Patterns

Electric Vehicles (EVs) are rapidly gaining adoption as a sustainable alternative to fuel-powered vehicles, making secure charging infrastructure essential. Despite traditional authentication protocols, recent results showed that attackers may steal energy through tailored relay attacks. One countermeasure is leveraging the EV's fingerprint on the current exchanged during charging. However, existing methods focus on the final charging stage, allowing malicious actors to consume substantial energy before being detected and repudiated. This underscores the need for earlier and more effective authentication methods to prevent unauthorized charging. Meanwhile, profiling raises privacy concerns, as uniquely identifying EVs through charging patterns could enable user tracking. In this paper, we propose a framework for uniquely identifying EVs using physical measurements from the early charging stages. We hypothesize that voltage behavior early in the process exhibits similar characteristics to current behavior in later stages. By extracting features from early voltage measurements, we demonstrate the feasibility of EV profiling. Our approach improves existing methods by enabling faster and more reliable vehicle identification. We test our solution on a dataset of 7408 usable charges from 49 EVs, achieving up to 0.86 accuracy. Feature importance analysis shows that near-optimal performance is possible with just 10 key features, improving efficiency alongside our lightweight models. This research lays the foundation for a novel authentication factor while exposing potential privacy risks from unauthorized access to charging data.

cs.CR↗

arXiv2025

SimProcess: High Fidelity Simulation of Noisy ICS Physical Processes

Industrial Control Systems (ICS) manage critical infrastructures like power grids and water treatment plants. Cyberattacks on ICSs can disrupt operations, causing severe economic, environmental, and safety issues. For example, undetected pollution in a water plant can put the lives of thousands at stake. ICS researchers have increasingly turned to honeypots -- decoy systems designed to attract attackers, study their behaviors, and eventually improve defensive mechanisms. However, existing ICS honeypots struggle to replicate the ICS physical process, making them susceptible to detection. Accurately simulating the noise in ICS physical processes is challenging because different factors produce it, including sensor imperfections and external interferences. In this paper, we propose SimProcess, a novel framework to rank the fidelity of ICS simulations by evaluating how closely they resemble real-world and noisy physical processes. It measures the simulation distance from a target system by estimating the noise distribution with machine learning models like Random Forest. Unlike existing solutions that require detailed mathematical models or are limited to simple systems, SimProcess operates with only a timeseries of measurements from the real system, making it applicable to a broader range of complex dynamic systems. We demonstrate the framework's effectiveness through a case study using real-world power grid data from the EPIC testbed. We compare the performance of various simulation methods, including static and generative noise techniques. Our model correctly classifies real samples with a recall of up to 1.0. It also identifies Gaussian and Gaussian Mixture as the best distribution to simulate our power systems, together with a generative solution provided by an autoencoder, thereby helping developers to improve honeypot fidelity. Additionally, we make our code publicly available.

cs.CR↗

arXiv2025

CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations

Despite being a legacy protocol with various known security issues, Controller Area Network (CAN) still represents the de-facto standard for communications within vehicles, ships, and industrial control systems. Many research works have designed Intrusion Detection Systems (IDSs) to identify attacks by training machine learning classifiers on bus traffic or its properties. Actions to take after detection are, on the other hand, less investigated, and prevention mechanisms usually include protocol modification (e.g., adding authentication). An effective solution has yet to be implemented on a large scale in the wild. The reasons are related to the effort to handle sporadic false positives, the inevitable delay introduced by authentication, and the closed-source automobile environment that does not easily permit modifying Electronic Control Units (ECUs) software. In this paper, we propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations. It employs a new classification of attacks based on the attacker's need in terms of access level to the bus, distinguishing between Frame Injection Attacks (FIAs) (i.e., using frame-level access) and Single-Bit Attacks (SBAs) (i.e., employing bit-level access). CANTXSec detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature. We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs. Moreover, to encourage developers to employ CANTXSec, we discuss implementation details, providing an analysis based on each user's risk assessment.

cs.CR↗

arXiv2025

ACRIC: Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check

The increasing integration of modern IT technologies into OT technologies and industrial systems is expanding the vulnerability surface of legacy infrastructures, which often rely on outdated protocols and resource-constrained devices. Recent security incidents in safety-critical industries exposed how the lack of proper message authentication enables attackers to inject malicious commands or alter system behavior, revealing fundamental security weaknesses in existing architectures. These shortcomings have thus prompted new regulations that emphasize the pressing need to strengthen cybersecurity, particularly in legacy systems. Authentication is widely recognized as a fundamental security measure that enhances system resilience. However, its adoption in legacy industrial environments is limited due to practical challenges like backward compatibility, message format changes, and hardware replacement or upgrades costs. In this paper, we introduce ACRIC, a message authentication solution to secure legacy industrial communications explicitly tailored to overcome those challenges all at once. ACRIC uniquely leverages cryptographic computations applied to the CRC field - already present in most industrial communication protocols - ensuring robust message integrity protection and authentication without requiring additional hardware or modifications to existing message formats. ACRIC's backward compatibility and protocol-agnostic nature enable coexistence with non-secured devices, thus facilitating gradual security upgrades in legacy infrastructures. Formal security assessment and experimental evaluation on an industrial-grade testbed demonstrate that ACRIC provides robust security guarantees with minimal computational overhead (~ 4 us). These results underscore ACRIC's practicality, cost-effectiveness, and suitability for effective adoption in resource-constrained industrial environments.

cs.CR↗

arXiv2025

SoK: Stealing Cars Since Remote Keyless Entry Introduction and How to Defend From It

Remote Keyless Entry (RKE) systems have been the target of thieves since their introduction in automotive industry. Robberies targeting vehicles and their remote entry systems are booming again without a significant advancement from the industrial sector being able to protect against them. Researchers and attackers continuously play cat and mouse to implement new methodologies to exploit weaknesses and defense strategies for RKEs. In this fragment, different attacks and defenses have been discussed in research and industry without proper bridging. In this paper, we provide a Systematization Of Knowledge (SOK) on RKE and Passive Keyless Entry and Start (PKES), focusing on their history and current situation, ranging from legacy systems to modern web-based ones. We provide insight into vehicle manufacturers' technologies and attacks and defense mechanisms involving them. To the best of our knowledge, this is the first comprehensive SOK on RKE systems, and we address specific research questions to understand the evolution and security status of such systems. By identifying the weaknesses RKE still faces, we provide future directions for security researchers and companies to find viable solutions to address old attacks, such as Relay and RollJam, as well as new ones, like API vulnerabilities.

cs.CR↗

arXiv2025

Acoustic Side-Channel Attacks on a Computer Mouse

Acoustic Side-Channel Attacks (ASCAs) extract sensitive information by using audio emitted from a computing devices and their peripherals. Attacks targeting keyboards are popular and have been explored in the literature. However, similar attacks targeting other human interface peripherals, such as computer mice, are under-explored. To this end, this paper considers security leakage via acoustic signals emanating from normal mouse usage. We first confirm feasibility of such attacks by showing a proof-of-concept attack that classifies four mouse movements with 97% accuracy in a controlled environment. We then evolve the attack towards discerning twelve unique mouse movements using a smartphone to record the experiment. Using Machine Learning (ML) techniques, the model is trained on an experiment with six participants to be generalizable and discern among twelve movements with 94% accuracy. In addition, we experiment with an attack that detects a user action of closing a full-screen window on a laptop. Achieving an accuracy of 91%, this experiment highlights exploiting audio leakage from computer mouse movements in a realistic scenario.

cs.CR↗

arXiv2025

WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems

In the era of data expansion, ensuring data privacy has become increasingly critical, posing significant challenges to traditional AI-based applications. In addition, the increasing adoption of IoT devices has introduced significant cybersecurity challenges, making traditional Network Intrusion Detection Systems (NIDS) less effective against evolving threats, and privacy concerns and regulatory restrictions limit their deployment. Federated Learning (FL) has emerged as a promising solution, allowing decentralized model training while maintaining data privacy to solve these issues. However, despite implementing privacy-preserving technologies, FL systems remain vulnerable to adversarial attacks. Furthermore, data distribution among clients is not heterogeneous in the FL scenario. We propose WeiDetect, a two-phase, server-side defense mechanism for FL-based NIDS that detects malicious participants to address these challenges. In the first phase, local models are evaluated using a validation dataset to generate validation scores. These scores are then analyzed using a Weibull distribution, identifying and removing malicious models. We conducted experiments to evaluate the effectiveness of our approach in diverse attack settings. Our evaluation included two popular datasets, CIC-Darknet2020 and CSE-CIC-IDS2018, tested under non-IID data distributions. Our findings highlight that WeiDetect outperforms state-of-the-art defense approaches, improving higher target class recall up to 70% and enhancing the global model's F1 score by 1% to 14%.

cs.CR↗

arXiv2025

A Survey on Decentralized Identifiers and Verifiable Credentials

Digital identity has always been considered the keystone for implementing secure and trustworthy communications among parties. The ever-evolving digital landscape has gone through many technological transformations that have also affected the way entities are digitally identified. During this digital evolution, identity management has shifted from centralized to decentralized approaches. The last era of this journey is represented by the emerging Self-Sovereign Identity (SSI), which gives users full control over their data. SSI leverages decentralized identifiers (DIDs) and verifiable credentials (VCs), which have been recently standardized by the World Wide Web Community (W3C). These technologies have the potential to build more secure and decentralized digital identity systems, remarkably contributing to strengthening the security of communications that typically involve many distributed participants. It is worth noting that the scope of DIDs and VCs extends beyond individuals, encompassing a broad range of entities including cloud, edge, and Internet of Things (IoT) resources. However, due to their novelty, existing literature lacks a comprehensive survey on how DIDs and VCs have been employed in different application domains, which go beyond SSI systems. This paper provides readers with a comprehensive overview of such technologies from different perspectives. Specifically, we first provide the background on DIDs and VCs. Then, we analyze available implementations and offer an in-depth review of how these technologies have been employed across different use-case scenarios. Furthermore, we examine recent regulations and initiatives that have been emerging worldwide. Finally, we present some challenges that hinder their adoption in real-world scenarios and future research directions.

cs.CR↗

arXiv2025

Optimal Graph Stretching for Distributed Averaging

The performance of distributed averaging depends heavily on the underlying topology. In various fields, including compressed sensing, multi-party computation, and abstract graph theory, graphs may be expected to be free of short cycles, i.e. to have high girth. Though extensive analyses and heuristics exist for optimising the performance of distributed averaging in general networks, these studies do not consider girth. As such, it is not clear what happens to convergence time when a graph is stretched to a higher girth. In this work, we introduce the optimal graph stretching problem, wherein we are interested in finding the set of edges for a particular graph that ensures optimal convergence time under constraint of a minimal girth. We compare various methods for choosing which edges to remove, and use various convergence heuristics to speed up the searching process. We generate many graphs with varying parameters, stretch and optimise them, and measure the duration of distributed averaging. We find that stretching by itself significantly increases convergence time. This decrease can be counteracted with a subsequent repair phase, guided by a convergence time heuristic. Existing heuristics are capable, but may be suboptimal.

cs.DC↗

arXiv2025

Can LLMs Classify CVEs? Investigating LLMs Capabilities in Computing CVSS Vectors

Common Vulnerability and Exposure (CVE) records are fundamental to cybersecurity, offering unique identifiers for publicly known software and system vulnerabilities. Each CVE is typically assigned a Common Vulnerability Scoring System (CVSS) score to support risk prioritization and remediation. However, score inconsistencies often arise due to subjective interpretations of certain metrics. As the number of new CVEs continues to grow rapidly, automation is increasingly necessary to ensure timely and consistent scoring. While prior studies have explored automated methods, the application of Large Language Models (LLMs), despite their recent popularity, remains relatively underexplored. In this work, we evaluate the effectiveness of LLMs in generating CVSS scores for newly reported vulnerabilities. We investigate various prompt engineering strategies to enhance their accuracy and compare LLM-generated scores against those from embedding-based models, which use vector representations classified via supervised learning. Our results show that while LLMs demonstrate potential in automating CVSS evaluation, embedding-based methods outperform them in scoring more subjective components, particularly confidentiality, integrity, and availability impacts. These findings underscore the complexity of CVSS scoring and suggest that combining LLMs with embedding-based methods could yield more reliable results across all scoring components.

cs.CR↗

arXiv2025

PQ-CAN: A Framework for Simulating Post-Quantum Cryptography in Embedded Systems

The rapid development of quantum computers threatens traditional cryptographic schemes, prompting the need for Post-Quantum Cryptography (PQC). Although the NIST standardization process has accelerated the development of such algorithms, their application in resource-constrained environments such as embedded systems remains a challenge. Automotive systems relying on the Controller Area Network (CAN) bus for communication are particularly vulnerable due to their limited computational capabilities, high traffic, and need for real-time response. These constraints raise concerns about the feasibility of implementing PQC in automotive environments, where legacy hardware and bit rate limitations must also be considered. In this paper, we introduce PQ-CAN, a modular framework for simulating the performance and overhead of PQC algorithms in embedded systems. We consider the automotive domain as our case study, testing a variety of PQC schemes under different scenarios. Our simulation enables the adjustment of embedded system computational capabilities and CAN bus bit rate constraints. We also provide insights into the trade-offs involved by analyzing each algorithm's security level and overhead for key encapsulation and digital signature. By evaluating the performance of these algorithms, we provide insights into their feasibility and identify the strengths and limitations of PQC in securing automotive communications in the post-quantum era.

cs.CR↗

arXiv2025

TeLL Me what you cant see

During criminal investigations, images of persons of interest directly influence the success of identification procedures. However, law enforcement agencies often face challenges related to the scarcity of high-quality images or their obsolescence, which can affect the accuracy and success of people searching processes. This paper introduces a novel forensic mugshot augmentation framework aimed at addressing these limitations. Our approach enhances the identification probability of individuals by generating additional, high-quality images through customizable data augmentation techniques, while maintaining the biometric integrity and consistency of the original data. Several experimental results show that our method significantly improves identification accuracy and robustness across various forensic scenarios, demonstrating its effectiveness as a trustworthy tool law enforcement applications. Index Terms: Digital Forensics, Person re-identification, Feature extraction, Data augmentation, Visual-Language models.

cs.CV↗