arXiv subjects

Alessandro Brighente

Publications and source records attributed to Alessandro Brighente.

At least 37 records · Page 2Linked to original sources

arXiv2024

GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction

The smart grid represents a pivotal innovation in modernizing the electricity sector, offering an intelligent, digitalized energy network capable of optimizing energy delivery from source to consumer. It hence represents the backbone of the energy sector of a nation. Due to its central role, the availability of the smart grid is paramount and is hence necessary to have in-depth control of its operations and safety. To this aim, researchers developed multiple solutions to assess the smart grid's stability and guarantee that it operates in a safe state. Artificial intelligence and Machine learning algorithms have proven to be effective measures to accurately predict the smart grid's stability. Despite the presence of known adversarial attacks and potential solutions, currently, there exists no standardized measure to protect smart grids against this threat, leaving them open to new adversarial attacks. In this paper, we propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99. Also by manipulating authentic data and sensor values, the attacker can amplify grid issues, potentially undetected due to a compromised stability prediction system. These results underscore the imperative of fortifying smart grid security mechanisms against adversarial manipulation to uphold system stability and reliability.

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arXiv2024

Reduce to the MACs -- Privacy Friendly Generic Probe Requests

Abstract. Since the introduction of active discovery in Wi-Fi networks, users can be tracked via their probe requests. Although manufacturers typically try to conceal Media Access Control (MAC) addresses using MAC address randomisation, probe requests still contain Information Elements (IEs) that facilitate device identification. This paper introduces generic probe requests: By removing all unnecessary information from IEs, the requests become indistinguishable from one another, letting single devices disappear in the largest possible anonymity set. Conducting a comprehensive evaluation, we demonstrate that a large IE set contained within undirected probe requests does not necessarily imply fast connection establishment. Furthermore, we show that minimising IEs to nothing but Supported Rates would enable 82.55% of the devices to share the same anonymity set. Our contributions provide a significant advancement in the pursuit of robust privacy solutions for wireless networks, paving the way for more user anonymity and less surveillance in wireless communication ecosystems.

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arXiv2024

Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers?

Autonomous driving is a research direction that has gained enormous traction in the last few years thanks to advancements in Artificial Intelligence (AI). Depending on the level of independence from the human driver, several studies show that Autonomous Vehicles (AVs) can reduce the number of on-road crashes and decrease overall fuel emissions by improving efficiency. However, security research on this topic is mixed and presents some gaps. On one hand, these studies often neglect the intrinsic vulnerabilities of AI algorithms, which are known to compromise the security of these systems. On the other, the most prevalent attacks towards AI rely on unrealistic assumptions, such as access to the model parameters or the training dataset. As such, it is unclear if autonomous driving can still claim several advantages over human driving in real-world applications. This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs and establishing a pragmatic threat model. Through our analysis, we develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios. Our evaluation serves as a foundation for providing essential takeaway messages, guiding both researchers and practitioners at various stages of the automation pipeline. In doing so, we contribute valuable insights to advance the discourse on the security and viability of autonomous driving in real-world applications.

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arXiv2024

Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments

In this paper, we investigate the security implications of virtualized and software-based Open Radio Access Network (RAN) systems, specifically focusing on the architecture proposed by the O-RAN ALLIANCE and O-Cloud deployments based on the O-RAN Software Community (OSC) stack and infrastructure. Our key findings are based on a thorough security assessment and static scanning of the OSC Near Real-Time RAN Intelligent Controller (RIC) cluster. We highlight the presence of potential vulnerabilities and misconfigurations in the Kubernetes infrastructure supporting the RIC, also due to the usage of outdated versions of software packages, and provide an estimation of their criticality using various deployment auditing frameworks (e.g., MITRE ATT&CK and the NSA CISA). In addition, we propose methodologies to minimize these issues and harden the Open RAN virtualization infrastructure. These encompass the integration of security evaluation methods into the deployment process, implementing deployment hardening measures, and employing policy-based control for RAN components. We emphasize the need to address the problems found in order to improve the overall security of virtualized Open RAN systems.

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arXiv2024

FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids

Predicting and classifying faults in electricity networks is crucial for uninterrupted provision and keeping maintenance costs at a minimum. Thanks to the advancements in the field provided by the smart grid, several data-driven approaches have been proposed in the literature to tackle fault prediction tasks. Implementing these systems brought several improvements, such as optimal energy consumption and quick restoration. Thus, they have become an essential component of the smart grid. However, the robustness and security of these systems against adversarial attacks have not yet been extensively investigated. These attacks can impair the whole grid and cause additional damage to the infrastructure, deceiving fault detection systems and disrupting restoration. In this paper, we present FaultGuard, the first framework for fault type and zone classification resilient to adversarial attacks. To ensure the security of our system, we employ an Anomaly Detection System (ADS) leveraging a novel Generative Adversarial Network training layer to identify attacks. Furthermore, we propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. We comprehensively evaluate the framework's performance against various adversarial attacks using the IEEE13-AdvAttack dataset, which constitutes the state-of-the-art for resilient fault prediction benchmarking. Our model outclasses the state-of-the-art even without considering adversaries, with an accuracy of up to 0.958. Furthermore, our ADS shows attack detection capabilities with an accuracy of up to 1.000. Finally, we demonstrate how our novel training layers drastically increase performances across the whole framework, with a mean increase of 154% in ADS accuracy and 118% in model accuracy.

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arXiv2024

Penetration Testing of 5G Core Network Web Technologies

Thanks to technologies such as virtual network function the Fifth Generation (5G) of mobile networks dynamically allocate resources to different types of users in an on-demand fashion. Virtualization extends up to the 5G core, where software-defined networks and network slicing implement a customizable environment. These technologies can be controlled via application programming interfaces and web technologies, inheriting hence their security risks and settings. An attacker exploiting vulnerable implementations of the 5G core may gain privileged control of the network assets and disrupt its availability. However, there is currently no security assessment of the web security of the 5G core network. In this paper, we present the first security assessment of the 5G core from a web security perspective. We use the STRIDE threat modeling approach to define a complete list of possible threat vectors and associated attacks. Thanks to a suite of security testing tools, we cover all of these threats and test the security of the 5G core. In particular, we test the three most relevant open-source 5G core implementations, i.e., Open5GS, Free5Gc, and OpenAirInterface. Our analysis shows that all these cores are vulnerable to at least two of our identified attack vectors, demanding increased security measures in the development of future 5G core networks.

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arXiv2024

A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols

The large number of Electronic Control Units (ECUs) mounted on modern cars and their expansive communication capabilities create a substantial attack surface for potential exploitation. Despite the evolution of automotive technology, the continued use of the originally insecure Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. In response to the absence of standardized authentication protocols within the automotive domain, researchers propose diverse solutions, each with unique strengths and vulnerabilities. However, the continuous influx of new protocols and potential oversights in meeting security requirements and essential operational features further complicate the implementability of these protocols. This paper comprehensively reviews and compares the 15 most prominent authentication protocols for the CAN bus. Our analysis emphasizes their strengths and weaknesses, evaluating their alignment with critical security requirements for automotive authentication. Additionally, we evaluate protocols based on essential operational criteria that contribute to ease of implementation in predefined infrastructures, enhancing overall reliability and reducing the probability of successful attacks. Our study reveals a prevalent focus on defending against external attackers in existing protocols, exposing vulnerabilities to internal threats. Notably, authentication protocols employing hash chains, Mixed Message Authentication Codes, and asymmetric encryption techniques emerge as the most effective approaches. Through our comparative study, we classify the considered protocols based on their security attributes and suitability for implementation, providing valuable insights for future developments in the field.

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arXiv2023

DynamiQS: Quantum Secure Authentication for Dynamic Charging of Electric Vehicles

Dynamic Wireless Power Transfer (DWPT) is a novel technology that allows charging an electric vehicle while driving thanks to a dedicated road infrastructure. DWPT's capabilities in automatically establishing charging sessions and billing without users' intervention make it prone to cybersecurity attacks. Hence, security is essential in preventing fraud, impersonation, and user tracking. To this aim, researchers proposed different solutions for authenticating users. However, recent advancements in quantum computing jeopardize classical public key cryptography, making currently existing solutions in DWPT authentication nonviable. To avoid the resource burden imposed by technology upgrades, it is essential to develop post-quantum-resistant solutions. In this paper, we propose DynamiQS, the first post-quantum secure authentication protocol for dynamic wireless charging. DynamiQS is privacy-preserving and secure against attacks on the DWPT. We leverage an Identity-Based Encryption with Lattices in the Ring Learning With Error framework. Furthermore, we show the possibility of using DynamiQS in a real environment, leveraging the results of cryptographic computation on real constrained devices and simulations. DynamiQS reaches a total time cost of around 281 ms, which is practicable in dynamic charging settings (car and charging infrastructure).

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arXiv2023

LoVe is in the Air -- Location Verification of ADS-B Signals using Distributed Public Sensors

The Automatic Dependant Surveillance-Broadcast (ADS-B) message scheme was designed without any authentication or encryption of messages in place. It is therefore easily possible to attack it, e.g., by injecting spoofed messages or modifying the transmitted Global Navigation Satellite System (GNSS) coordinates. In order to verify the integrity of the received information, various methods have been suggested, such as multilateration, the use of Kalman filters, group certification, and many others. However, solutions based on modifications of the standard may be difficult and too slow to be implemented due to legal and regulatory issues. A vantage far less explored is the location verification using public sensor data. In this paper, we propose LoVe, a lightweight message verification approach that uses a geospatial indexing scheme to evaluate the trustworthiness of publicly deployed sensors and the ADS-B messages they receive. With LoVe, new messages can be evaluated with respect to the plausibility of their reported coordinates in a location privacy-preserving manner, while using a data-driven and lightweight approach. By testing our approach on two open datasets, we show that LoVe achieves very low false positive rates (between 0 and 0.00106) and very low false negative rates (between 0.00065 and 0.00334) while providing a real-time compatible approach that scales well even with a large sensor set. Compared to currently existing approaches, LoVe neither requires a large number of sensors, nor for messages to be recorded by as many sensors as possible simultaneously in order to verify location claims. Furthermore, it can be directly applied to currently deployed systems thus being backward compatible.

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arXiv2023

QEVSEC: Quick Electric Vehicle SEcure Charging via Dynamic Wireless Power Transfer

Dynamic Wireless Power Transfer (DWPT) can be used for on-demand recharging of Electric Vehicles (EV) while driving. However, DWPT raises numerous security and privacy concerns. Recently, researchers demonstrated that DWPT systems are vulnerable to adversarial attacks. In an EV charging scenario, an attacker can prevent the authorized customer from charging, obtain a free charge by billing a victim user and track a target vehicle. State-of-the-art authentication schemes relying on centralized solutions are either vulnerable to various attacks or have high computational complexity, making them unsuitable for a dynamic scenario. In this paper, we propose Quick Electric Vehicle SEcure Charging (QEVSEC), a novel, secure, and efficient authentication protocol for the dynamic charging of EVs. Our idea for QEVSEC originates from multiple vulnerabilities we found in the state-of-the-art protocol that allows tracking of user activity and is susceptible to replay attacks. Based on these observations, the proposed protocol solves these issues and achieves lower computational complexity by using only primitive cryptographic operations in a very short message exchange. QEVSEC provides scalability and a reduced cost in each iteration, thus lowering the impact on the power needed from the grid.

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arXiv2023

EPUF: A Novel Scheme Based on Entropy Features of Latency-based DRAM PUFs Providing Lightweight Authentication in IoT Networks

Physical unclonable functions (PUFs) are hardware-oriented primitives that exploit manufacturing variations to generate a unique identity for a physical system. Recent advancements showed how DRAM can be exploited to implement PUFs. DRAM PUFs require no additional circuits for PUF operations and can be used in most of the applications with resource-constrained nodes such as Internet of Things (IoT) networks. However, the existing DRAM PUF solutions either require to interrupt other functions in the host system, or provide unreliable responses due to their sensitiveness to the environmental conditions. In this paper, we propose EPUF, a novel strategy to extract random and unique features from DRAM cells to generate reliable PUF responses. In particular, we use the bitmap images of the binary DRAM values and their entropy features. We show via real device experiments that EPUF is approximately $1.7$ times faster than other state of the art solutions, achieves $100\%$ reliability, generates features with $47.79\%$ uniqueness, and supports a large set of CRP that leads to new potentials for DRAM PUF-based authentication. We also propose a lightweight authentication protocol based on EPUF, which not only provides far better security guarantees but also outperforms the state-of-the-art in terms of communication overhead and computational cost.

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arXiv2023

Beware of Pickpockets: A Practical Attack against Blocking Cards

Today, we rely on contactless smart cards to perform several critical operations (e.g., payments and accessing buildings). Attacking smart cards can have severe consequences, such as losing money or leaking sensitive information. Although the security protections embedded in smart cards have evolved over the years, those with weak security properties are still commonly used. Among the different solutions, blocking cards are affordable devices to protect smart cards. These devices are placed close to the smart cards, generating a noisy jamming signal or shielding them. Whereas vendors claim the reliability of their blocking cards, no previous study has ever focused on evaluating their effectiveness. In this paper, we shed light on the security threats on smart cards in the presence of blocking cards, showing the possibility of being bypassed by an attacker. We analyze blocking cards by inspecting their emitted signal and assessing a vulnerability in their internal design. We propose a novel attack that bypasses the jamming signal emitted by a blocking card and reads the content of the smart card. We evaluate the effectiveness of 11 blocking cards when protecting a MIFARE Ultralight smart card and a MIFARE Classic card. Of these 11 cards, we managed to bypass 8 of them and successfully dump the content of a smart card despite the presence of the blocking card. Our findings highlight that the noise type implemented by the blocking cards highly affects the protection level achieved by them. Based on this observation, we propose a countermeasure that may lead to the design of effective blocking cards. To further improve security, we released the tool we developed to inspect the spectrum emitted by blocking cards and set up our attack.

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arXiv2023

Electric Vehicles Security and Privacy: Challenges, Solutions, and Future Needs

Electric Vehicles (EVs) share common technologies with classical fossil-fueled cars, but they also employ novel technologies and components (e.g., Charging System and Battery Management System) that create an unexplored attack surface for malicious users. Although multiple contributions in the literature explored cybersecurity aspects of particular components of the EV ecosystem (e.g., charging infrastructure), there is still no contribution to the holistic cybersecurity of EVs and their related technologies from a cyber-physical system perspective. In this paper, we provide the first in-depth study of the security and privacy threats associated with the EVs ecosystem. We analyze the threats associated with both the EV and the different charging solutions. Focusing on the Cyber-Physical Systems (CPS) paradigm, we provide a detailed analysis of all the processes that an attacker might exploit to affect the security and privacy of both drivers and the infrastructure. To address the highlighted threats, we present possible solutions that might be implemented. We also provide an overview of possible future directions to guarantee the security and privacy of the EVs ecosystem. Based on our analysis, we stress the need for EV-specific cybersecurity solutions.

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arXiv2022

Identity-Based Authentication for On-Demand Charging of Electric Vehicles

Dynamic wireless power transfer provides means for charging Electric Vehicles (EVs) while driving, avoiding stopping for charging and hence fostering their widespread adoption. Researchers devoted much effort over the last decade to provide a reliable infrastructure for potential users to improve comfort and time management. Due to the severe security and performance system requirements, the different scheme proposed in last years lack of a unified protocol involving the modern architecture model with merged authentication and billing processes. Furthermore, they require the continuous interaction of the trusted entity during the process, increasing the delay for the communication and reducing security due to the large number of message exchanges. In this paper, we propose a secure, computationally lightweight, unified protocol for fast authentication and billing that provides on-demand dynamic charging to comprehensively deal with all the computational and security constraints. The protocol employs an ID-based public encryption scheme to manage mutual authentication and pseudonyms to preserve the user's identity across multiple charging processes. Compared to state-of-the-art authentication protocols, our proposal overcomes the problem of overwhelming interactions and provides public scheme security against the use of simple operations in wide open communications without impacting on performance.

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arXiv2022

VLC Physical Layer Security through RIS-aided Jamming Receiver for 6G Wireless Networks

Visible Light Communication (VLC) is one the most promising enabling technology for future 6G networks to overcome Radio-Frequency (RF)-based communication limitations thanks to a broader bandwidth, higher data rate, and greater efficiency. However, from the security perspective, VLCs suffer from all known wireless communication security threats (e.g., eavesdropping and integrity attacks). For this reason, security researchers are proposing innovative Physical Layer Security (PLS) solutions to protect such communication. Among the different solutions, the novel Reflective Intelligent Surface (RIS) technology coupled with VLCs has been successfully demonstrated in recent work to improve the VLC communication capacity. However, to date, the literature still lacks analysis and solutions to show the PLS capability of RIS-based VLC communication. In this paper, we combine watermarking and jamming primitives through the Watermark Blind Physical Layer Security (WBPLSec) algorithm to secure VLC communication at the physical layer. Our solution leverages RIS technology to improve the security properties of the communication. By using an optimization framework, we can calculate RIS phases to maximize the WBPLSec jamming interference schema over a predefined area in the room. In particular, compared to a scenario without RIS, our solution improves the performance in terms of secrecy capacity without any assumption about the adversary's location. We validate through numerical evaluations the positive impact of RIS-aided solution to increase the secrecy capacity of the legitimate jamming receiver in a VLC indoor scenario. Our results show that the introduction of RIS technology extends the area where secure communication occurs and that by increasing the number of RIS elements the outage probability decreases.

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arXiv2022

Hide and Seek -- Preserving Location Privacy and Utility in the Remote Identification of Unmanned Aerial Vehicles

Due to the frequent unauthorized access by commercial drones to Critical Infrastructures (CIs) such as airports and oil refineries, the US-based Federal Avionics Administration (FAA) recently published a new specification, namely RemoteID. The aforementioned rule mandates that all Unmanned Aerial Vehicles (UAVs) have to broadcast information about their identity and location wirelessly to allow for immediate invasion attribution. However, the enforcement of such a rule poses severe concerns on UAV operators, especially in terms of location privacy and tracking threats, to name a few. Indeed, by simply eavesdropping on the wireless channel, an adversary could know the precise location of the UAV and track it, as well as obtaining sensitive information on path source and destination of the UAV. In this paper, we investigate the trade-off between location privacy and data utility that can be provided to UAVs when obfuscating the broadcasted location through differential privacy techniques. Leveraging the concept of Geo-Indistinguishability (Geo-Ind), already adopted in the context of Location-Based Services (LBS), we show that it is possible to enhance the privacy of the UAVs without preventing CI operators to timely detect unauthorized invasions. In particular, our experiments showed that when the location of an UAV is obfuscated with an average distance of 1.959 km, a carefully designed UAV detection system can detect 97.9% of invasions, with an average detection delay of 303.97 msec. The UAVs have to trade-off such enhanced location privacy with a non-negligible probability of false positives, i.e., being detected as invading while not really invading the no-fly zone. UAVs and CI operators can solve such ambiguous situations later on through the help of the FAA, being this latter the only one that can unveil the actual location of the UAV.

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arXiv2022

FOLPETTI: A Novel Multi-Armed Bandit Smart Attack for Wireless Networks

Channel hopping provides a defense mechanism against jamming attacks in large scale \ac{iot} networks.} However, a sufficiently powerful attacker may be able to learn the channel hopping pattern and efficiently predict the channel to jam. In this paper, we present FOLPETTI, a MAB-based attack to dynamically follow the victim's channel selection in real-time. Compared to previous attacks implemented via DRL, FOLPETTI does not require recurrent training phases to capture the victim's behavior, allowing hence a continuous attack. We assess the validity of FOLPETTI by implementing it to launch a jamming attack. We evaluate its performance against a victim performing random channel selection and a victim implementing a MAB defence strategy. We assume that the victim detects an attack when more than $20\%$ of the transmitted packets are not received, therefore this represents the limit for the attack to be stealthy. In this scenario, FOLPETTI achieves a $15\%$ success rate for the victim's random channel selection strategy, close to the $17.5\%$ obtained with a genie-aided approach. Conversely, the DRL-based approach reaches a success rate of $12.5\%$, which is $5.5\%$ less than FOLPETTI. We also confirm the results by confronting FOLPETTI with a MAB based channel hopping method. Finally, we show that FOLPETTI creates an additional energy demand independently from its success rate, therefore decreasing the lifetime of IoT devices.

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arXiv2022

Unmanned Aerial Vehicles Meet Reflective Intelligent Surfaces to Improve Coverage and Secrecy

The high configurability and low cost of Reflective Intelligent Surfaces (RISs) made them a promising solution for enhancing the capabilities of Beyond Fifth-Generation (B5G) networks. Recent works proposed to mount RISs on Unmanned Aerial Vehicles (UAVs), combining the high network configurability provided by RIS with the mobility brought by UAVs. However, the RIS represents an additional weight that impacts the battery lifetime of the UAV. Furthermore, the practicality of the resulting link in terms of communication channel quality and security have not been assessed in detail. In this paper, we highlight all the essential features that need to be considered for the practical deployment of RIS-enabled UAVs. We are the first to show how the RIS size and its power consumption impact the UAV flight time. We then assess how the RIS size, carrier frequency, and UAV flying altitude affects the path loss. Lastly, we propose a novel particle swarm-based approach to maximize coverage and improve the confidentiality of transmissions in a cellular scenario with the support of RISs carried by UAVs.

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