arXiv subjects

Alessandro Brighente

Publications and source records attributed to Alessandro Brighente.

At least 19 recordsLinked to original sources

arXiv2026

VESSI - VLM-Enhanced Support for Surveillance and Investigations

Automated video surveillance analysis has become a critical component of intelligence infrastructures and Law Enforcement agencies. Traditional systems lack the semantic module for comprehensive situational awareness and forensic tasks, limiting their ability to interpret events meaningfully or support post-incident investigations. This slows operational insight and increases the burden on human analysts. Recent advances in Vision-Language Models (VLMs) offer promising pathways to bridge this gap. To address this, we propose VLM-Enhanced Support for Surveillance and Investigations (VESSI), a VLM-based framework designed to enhance automated video surveillance analysis through prompt-driven interrogation of video sequences where salient visual features are converted into textual descriptions. We test our framework with four state-of-the-art models. Since most datasets for this task are unlabeled, we also propose the Composite Model Utility Score (CMUS) to assess VLM performance. Experimental results show that our solution substantially improves the analysis capabilities of human operators and enhances the flexibility of automated surveillance systems. In our evaluation, the most reliable model flagged potentially relevant activity in more than 66% of the videos while reducing review time by more than 85%, offering a practical balance between selectivity and efficiency. The model ordering produced by the reference-free CMUS evaluation was reproduced by the normal-video CMUS evaluation and matched the false-positive-rate ordering obtained from 5,909 manually referenced frames. This agreement supports the operational use of the score within the evaluated setting.

cs.CV↗

arXiv2026

When Authentication Is Not Enough: Breaking Behavior-Based Driver Authentication Systems

Researchers extensively explored behavior-based driver authentication systems in vehicles. Pushed by advances in Artificial Intelligence (AI), these systems employ powerful models to identify drivers based on unique biometric behaviors. However, existing work prioritizes AI performance metrics, neglecting secure integration with real-world automotive environments and the threat of adversarial attacks that can fool the authentication system. In this paper, we propose for the first evasion attacks against behavior-based driver authentication systems, allowing an attacker to impersonate the legitimate driver. Our attacks exploit long-standing CAN bus weaknesses that allow the injection of forged frames without jeopardizing the attacker's safety while stealing the vehicle. When legitimate data samples are available, we propose \textbf{SMARTCAN}, a safety-aware replay attack. If the attacker can only use the authenticator as an oracle, we propose \textbf{GANCAN}, which trains a Generative Adversarial Network's generator using reinforcement learning on the authenticator's responses. Our attacks achieve a success rate up to 100\% against all the considered models and, in the worst case, require 22 minutes to steal a vehicle. Acknowledging our identified vulnerabilities, we discuss the requirements for a safe and effective deployment of these systems in real-world scenarios.

cs.CR↗

arXiv2026

From Source Code to Network Profile: Automated and Traceable MUD Profile Generation for IoT Devices

The Manufacturer Usage Description (MUD) standard allows IoT manufacturers to define expected network behaviors in a MUD file. This file can be translated into enforceable access-control policies, restricting compromised devices to operate solely through manufacturer-defined communication patterns. However, practical adoption of MUD depends on profiles that are accurate, complete, and maintainable. Existing approaches use traffic-based automation but require device deployment and prolonged monitoring, capturing only behavior exercised during observation. Rare, failure-triggered, or configuration-dependent communications may remain absent, producing incomplete policies that disrupt legitimate operation and offer limited insight into the software components responsible for each rule. We present AutoMUD, a source-code-driven tool that generates traceable MUD profiles for IoT devices from their firmware and software source code. AutoMUD combines static and syntactic extraction, retrieval-grounded language-model reasoning, and deterministic validation and compilation to recover the communication behavior characterizing an IoT device and translate eligible endpoints into policy rules. By analyzing code-level evidence, AutoMUD exposes rarely exercised and conditional communication paths, links every generated rule to its source-level provenance, and preserves excluded findings with explicit reasons for review. Our evaluation on a Linux-based repository demonstrates that AutoMUD recovers complete communication behavior, consolidates validated behavior into semantic endpoint groups, and generates structurally valid MUD profiles. Through a controlled semantic fault-injection campaign, we demonstrate that AutoMUD enables analysts to detect, localize, explain, and correct propagated errors, recovering policies semantically identical to their clean counterparts.

cs.CR↗

arXiv2026

Safety-Aware Zero Trust Enforcement for IoT and Cyber-Physical Systems

Zero Trust (ZT) replaces the implicit trust of perimeter-based security with explicit, continuous, context-aware authorization. This shift is particularly relevant to IoT and cyber-physical systems, whose heterogeneous, long-lived, and remotely connected components make persistent trust untenable. Yet their physical coupling complicates ZT adoption: restricting a suspicious component can reduce cyber exposure while removing telemetry or control capabilities required for operation. Existing work mainly models physical harm caused by attacks, with less attention to consequences introduced by enforcement itself. We introduce Safety-Aware Zero Trust (SA-ZT), which treats restriction-induced physical consequences as policy inputs. We map the NIST ZT tenets to nine IoT/CPS convergence strains, distinguish IoT-amplified challenges from those specific to cyber-physical coupling, and derive corresponding operational requirements. SA-ZT extends the NIST ZT Architecture with a Safety Engine and a Telemetry Broker. The Safety Engine selects among admissible responses by jointly considering residual cyber risk and restriction-induced consequences, while the Telemetry Broker mediates raw telemetry visibility and estimator influence. With command-side enforcement, these entities separate raw visibility, automated influence, and state-changing authority, preserving observations for monitoring while constraining their influence on automated control. An IEEE 30-bus case study under false-data-injection attack illustrates how SA-ZT makes cyber containment, telemetry visibility and influence, physical consequences, and authorization timing explicit, providing an implementable and inspectable representation of cyber-physical enforcement trade-offs.

cs.CR↗

arXiv2026

Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection

Provenance-Based Intrusion Detection Systems (PIDSs) detect Advanced Persistent Threats (APTs) by analyzing system interactions. However, existing methods largely treat relations uniformly, overlooking statistical heterogeneity; in CADETS, relation frequencies differ by approximately $140{,}000\times$. This may cause PIDSs to focus more on frequent relations and overlook differences in normal error levels across relations, increasing the risk of false alarms and missed detections. We present RECAL, an unsupervised framework using relation-balanced masked graph learning to better capture rare interaction patterns. It further calibrates reconstruction errors against each relation's benign error distribution to produce comparable anomaly evidence, helping distinguish attacks from benign behavior and reduce false alarms. On three DARPA E3 datasets, RECAL achieves F1 scores of 99.99\%, 99.93\%, and 99.99\%, outperforming the best baseline on each dataset by 0.88, 0.82, and 0.42 percentage points, respectively. Compared with the baseline reporting the lowest FPR, RECAL reduces mean FPR by approximately $105\times$, $4\times$, and $41\times$.

cs.CR↗

arXiv2026

FIDEM: A Standard-Compliant Framework for Secure Binding of MUD Profiles to IoT Devices

The Manufacturer Usage Description (MUD) enables enforcement of network restrictions for IoT devices based on their expected network traffic, as specified by manufacturers in a MUD file. Devices advertise a URL pointing to this file, yet the standard does not define how to securely bind the issuing device to its profile. As a result, malicious devices can manipulate network policy enforcement by advertising valid URLs referencing genuine MUD profiles, but not intended for that device. Although MUD defines a certificate-based secure issuance method, current deployments rely on the insecure DHCP-based extension due to simpler integration. Existing solutions either depend on Public Key Infrastructure (PKI), break standard compliance, require excessive active manufacturer involvement, or overlook secure profile updates. In this paper, we present FIDEM, a standard-compliant framework for securing DHCP-based MUD URL issuance. FIDEM provides cryptographic binding between IoT devices and their MUD profiles by leveraging Zero-Knowledge-Proof authentication, without requiring device certificates or device-side PKI, minimizing manufacturers' involvement, and supporting secure profile updates. Formal analysis shows that FIDEM withstands stronger adversaries than in prior work, including supply-chain compromise and attacks using legitimate devices as cryptographic oracles. Our real-world evaluation on two reference constrained devices (ESP32-S3 and ESP32-C6) demonstrates minimal overhead compared to standard DHCP (~5 ms, 20 mJ) and significant improvements over certificate-based benchmarks (x21 faster, ~23\%\) less energy consumption) on an ESP32-C6 device.

cs.CR↗

arXiv2026

ORCA - An Automated Threat Analysis Pipeline for O-RAN Continuous Development

The Open-Radio Access Network (O-RAN) integrates numerous software components in a cloud-like deployment, opening the radio access network to previously unconsidered security threats. With the ever-evolving threat landscape, integrating security practices through a DevSecOps approach is essential for fast and secure releases. Current vulnerability assessment practices often rely on manual, labor-intensive, and subjective investigations, leading to inconsistencies in the threat analysis. To mitigate these issues, we establish an automated pipeline that leverages Natural Language Processing (NLP) to minimize human intervention and associated biases. By mapping real-world vulnerabilities to predefined threat lists with a standardized input format, our approach is the first to enable iterative, quantitative, and efficient assessments, generating reliable threat scores for both individual vulnerabilities and entire system components within O-RAN. We illustrate the effectiveness of our framework through an example implementation for O-RAN, showcasing how continuous security testing can integrate into automated testing pipelines to address the unique security challenges of this paradigm shift in telecommunications.

cs.CR↗

arXiv2025

A Practical Solution to Systematically Monitor Inconsistencies in SBOM-based Vulnerability Scanners

Software Bill of Materials (SBOM) provides new opportunities for automated vulnerability identification in software products. While the industry is adopting SBOM-based Vulnerability Scanning (SVS) to identify vulnerabilities, we increasingly observe inconsistencies and unexpected behavior, that result in false negatives and silent failures. In this work, we present the background necessary to understand the underlying complexity of SVS and introduce SVS-TEST, a method and tool to analyze the capability, maturity, and failure conditions of SVS-tools in real-world scenarios. We showcase the utility of SVS-TEST in a case study evaluating seven real-world SVS-tools using 16 precisely crafted SBOMs and their respective ground truth. Our results unveil significant differences in the reliability and error handling of SVS-tools; multiple SVS-tools silently fail on valid input SBOMs, creating a false sense of security. We conclude our work by highlighting implications for researchers and practitioners, including how organizations and developers of SVS-tools can utilize SVS-TEST to monitor SVS capability and maturity. All results and research artifacts are made publicly available and all findings were disclosed to the SVS-tool developers ahead of time.

cs.SE↗

arXiv2025

Obfuscated Location Disclosure for Remote ID Enabled Drones

The Remote ID (RID) regulation recently introduced by several aviation authorities worldwide (including the US and EU) forces commercial drones to regularly (max. every second) broadcast plaintext messages on the wireless channel, providing information about the drone identifier and current location, among others. Although these regulations increase the accountability of drone operations and improve traffic management, they allow malicious users to track drones via the disclosed information, possibly leading to drone capture and severe privacy leaks. In this paper, we propose Obfuscated Location disclOsure for RID-enabled drones (OLO-RID), a solution modifying and extending the RID regulation while preserving drones' location privacy. Rather than disclosing the actual drone's location, drones equipped with OLO-RID disclose a differentially private obfuscated location in a mobile scenario. OLO-RID also extends RID messages with encrypted location information, accessible only by authorized entities and valuable to obtain the current drone's location in safety-critical use cases. We design, implement, and deploy OLO-RID on a Raspberry Pi 3 and release the code of our implementation as open-source. We also perform an extensive performance assessment of the runtime overhead of our solution in terms of processing, communication, memory, and energy consumption. We show that OLO-RID can generate RID messages on a constrained device in less than 0.16 s while also requiring a minimal energy toll on a relevant device (0.0236% of energy for a DJI Mini 2). We also evaluate the utility of the proposed approach in the context of three reference use cases involving the drones' location usage, demonstrating minimal performance degradation when trading off location privacy and utility for next-generation RID-compliant drone ecosystems.

cs.CR↗

arXiv2025

Cross-Service Token: Finding Attacks in 5G Core Networks

5G marks a major departure from previous cellular architectures, by transitioning from a monolithic design of the core network to a Service-Based Architecture (SBA) where services are modularized as Network Functions (NFs) which communicate with each other via standard-defined HTTP-based APIs called Service-Based Interfaces (SBIs). These NFs are deployed in private and public cloud infrastructure, and an access control framework based on OAuth restricts how they communicate with each other and obtain access to resources. Given the increased vulnerabilities of clouds to insiders, it is important to study the security of the 5G Core services for vulnerabilities that allow attackers to use compromised NFs to obtain unauthorized access to resources. We present FivGeeFuzz, a grammar-based fuzzing framework designed to uncover security flaws in 5G core SBIs. FivGeeFuzz automatically derives grammars from 3GPP API specifications to generate malformed, unexpected, or semantically inconsistent inputs, and it integrates automated bug detection with manual validation and root-cause analysis. We evaluate our approach on free5GC, the only open-source 5G core implementing Release 17-compliant SBIs with an access control mechanism. Using FivGeeFuzz, we discovered 8 previously unknown vulnerabilities in free5GC, leading to runtime crashes, improper error handling, and unauthorized access to resources, including a very severe attack we call Cross-Service Token Attack. All bugs were confirmed by the free5GC team, 7 have already been patched, and the remaining one has a patch under development.

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

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

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↗

arXiv2024

The Impact of SBOM Generators on Vulnerability Assessment in Python: A Comparison and a Novel Approach

The Software Supply Chain (SSC) security is a critical concern for both users and developers. Recent incidents, like the SolarWinds Orion compromise, proved the widespread impact resulting from the distribution of compromised software. The reliance on open-source components, which constitute a significant portion of modern software, further exacerbates this risk. To enhance SSC security, the Software Bill of Materials (SBOM) has been promoted as a tool to increase transparency and verifiability in software composition. However, despite its promise, SBOMs are not without limitations. Current SBOM generation tools often suffer from inaccuracies in identifying components and dependencies, leading to the creation of erroneous or incomplete representations of the SSC. Despite existing studies exposing these limitations, their impact on the vulnerability detection capabilities of security tools is still unknown. In this paper, we perform the first security analysis on the vulnerability detection capabilities of tools receiving SBOMs as input. We comprehensively evaluate SBOM generation tools by providing their outputs to vulnerability identification software. Based on our results, we identify the root causes of these tools' ineffectiveness and propose PIP-sbom, a novel pip-inspired solution that addresses their shortcomings. PIP-sbom provides improved accuracy in component identification and dependency resolution. Compared to best-performing state-of-the-art tools, PIP-sbom increases the average precision and recall by 60%, and reduces by ten times the number of false positives.

cs.CR↗

arXiv2024

Your Car Tells Me Where You Drove: A Novel Path Inference Attack via CAN Bus and OBD-II Data

Despite its well-known security issues, the Controller Area Network (CAN) is still the main technology for in-vehicle communications. Attackers posing as diagnostic services or accessing the CAN bus can threaten the drivers' location privacy to know the exact location at a certain point in time or to infer the visited areas. This represents a serious threat to users' privacy, but also an advantage for police investigations to gather location-based evidence. In this paper, we present On Path Diagnostic - Intrusion \& Inference (OPD-II), a novel path inference attack leveraging a physical car model and a map matching algorithm to infer the path driven by a car based on CAN bus data. Differently from available attacks, our approach only requires the attacker to know the initial location and heading of the victim's car and is not limited by the availability of training data, road configurations, or the need to access other victim's devices (e.g., smartphones). We implement our attack on a set of four different cars and a total number of 41 tracks in different road and traffic scenarios. We achieve an average of 95% accuracy on reconstructing the coordinates of the recorded path by leveraging a dynamic map-matching algorithm that outperforms the 75% and 89% accuracy values of other proposals while removing their set of assumptions.

cs.CR↗

arXiv2024

Hyperloop: A Cybersecurity Perspective

Hyperloop is among the most prominent future transportation systems. It involves novel technologies to allow traveling at a maximum speed of 1220km/h while guaranteeing sustainability. Due to the system's performance requirements and the critical infrastructure it represents, its safety and security must be carefully considered. In transportation systems, cyberattacks could lead to safety issues with catastrophic consequences for the population and the surrounding environment. To this day, no research investigated the cybersecurity issues of the Hyperloop technology. In this paper, we provide the first analysis of the cybersecurity challenges of the interconnections between the different components of the Hyperloop ecosystem. We base our analysis on the currently available Hyperloop implementations, distilling those features that will likely be present in its final design. Moreover, we investigate possible infrastructure management approaches and their security concerns. Finally, we discuss countermeasures and future directions for the security of the Hyperloop design.

cs.CR↗