arXiv subjects

Gabriele Oligeri

Publications and source records attributed to Gabriele Oligeri.

27 records · Page 2Linked to original sources

arXiv2021

Long-Term Noise Characterization of Narrowband Power Line Communications

Noise modeling in power line communications has recently drawn the attention of researchers. However, when characterizing the noise process in narrowband communications, previous works have only focused on small-scale phenomena involving fine-grained details. Nevertheless, the communication link's reliability is also affected by long-term noise phenomena that might affect transfer rates at higher layers as well. This paper addresses the problem of long-term noise characterization for narrowband power line communications and provides a statistical analysis of the long-term trends affecting the noise levels. We present a statistical description of the noise process in the time and frequency domains based on real field measurements in the FCC band (10 kHz - 490 kHz). The collected data comprises more than 1.8 billion samples taken from three different locations over a time period of approximately 10 days. The noise samples have been statistically analyzed by considering stationarity, autocorrelation, and independence. Although our results -- being unprecedented -- are interesting per se, they improve the noise pattern knowledge, thus paving the way for the design and implementation of more robust PLC protocols.

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arXiv2020

KaFHCa: Key-establishment via Frequency Hopping Collisions

The massive deployment of IoT devices being utilized by home automation, industrial and military scenarios demands for high security and privacy standards to be achieved through innovative solutions. This paper proposes KaFHCa, a crypto-less protocol that generates shared secret keys by combining random frequency hopping collisions and source indistinguishability independently of the radio channel status. While other solutions tie the secret bit rate generation to the current radio channel conditions, thus becoming unpractical in static environments, KaFHCa guarantees almost the same secret bitrate independently of the channel conditions. KaFHCa generates shared secrets through random collisions of the transmitter and the receiver in the radio spectrum, and leverages on the fading phenomena to achieve source indistinguishability, thus preventing unauthorized eavesdroppers from inferring the key. The proposed solution is (almost) independent of the adversary position, works under the conservative assumption of channel fading (σ = 8dB), and is capable of generating a secret key of 128 bits with less than 564 transmissions.

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arXiv2020

PAST-AI: Physical-layer Authentication of Satellite Transmitters via Deep Learning

Physical-layer security is regaining traction in the research community, due to the performance boost introduced by deep learning classification algorithms. This is particularly true for sender authentication in wireless communications via radio fingerprinting. However, previous research efforts mainly focused on terrestrial wireless devices while, to the best of our knowledge, none of the previous work took into consideration satellite transmitters. The satellite scenario is generally challenging because, among others, satellite radio transducers feature non-standard electronics (usually aged and specifically designed for harsh conditions). Moreover, the fingerprinting task is specifically difficult for Low-Earth Orbit (LEO) satellites (like the ones we focus in this paper) since they orbit at about 800Km from the Earth, at a speed of around 25,000Km/h, thus making the receiver experiencing a down-link with unique attenuation and fading characteristics. In this paper, we propose PAST-AI, a methodology tailored to authenticate LEO satellites through fingerprinting of their IQ samples, using advanced AI solutions. Our methodology is tested on real data -- more than 100M I/Q samples -- collected from an extensive measurements campaign on the IRIDIUM LEO satellites constellation, lasting 589 hours. Results are striking: we prove that Convolutional Neural Networks (CNN) and autoencoders (if properly calibrated) can be successfully adopted to authenticate the satellite transducers, with an accuracy spanning between 0.8 and 1, depending on prior assumptions. The proposed methodology, the achieved results, and the provided insights, other than being interesting on their own, when associated to the dataset that we made publicly available, will also pave the way for future research in the area.

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arXiv2020

MAGNETO: Fingerprinting USB Flash Drives via Unintentional Magnetic Emissions

Universal Serial Bus (USB) Flash Drives are nowadays one of the most convenient and diffused means to transfer files, especially when no Internet connection is available. However, USB flash drives are also one of the most common attack vectors used to gain unauthorized access to host devices. For instance, it is possible to replace a USB drive so that when the USB key is connected, it would install passwords stealing tools, root-kit software, and other disrupting malware. In such a way, an attacker can steal sensitive information via the USB-connected devices, as well as inject any kind of malicious software into the host. To thwart the above-cited raising threats, we propose MAGNETO, an efficient, non-interactive, and privacy-preserving framework to verify the authenticity of a USB flash drive, rooted in the analysis of its unintentional magnetic emissions. We show that the magnetic emissions radiated during boot operations on a specific host are unique for each device, and sufficient to uniquely fingerprint both the brand and the model of the USB flash drive, or the specific USB device, depending on the used equipment. Our investigation on 59 different USB flash drives---belonging to 17 brands, including the top brands purchased on Amazon in mid-2019---, reveals a minimum classification accuracy of 98.2% in the identification of both brand and model, accompanied by a negligible time and computational overhead. MAGNETO can also identify the specific USB Flash drive, with a minimum classification accuracy of 91.2%. Overall, MAGNETO proves that unintentional magnetic emissions can be considered as a viable and reliable means to fingerprint read-only USB flash drives. Finally, future research directions in this domain are also discussed.

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arXiv2020

GNSS Spoofing Detection via Opportunistic IRIDIUM Signals

In this paper, we study the privately-own IRIDIUM satellite constellation, to provide a location service that is independent of the GNSS. In particular, we apply our findings to propose a new GNSS spoofing detection solution, exploiting unencrypted IRIDIUM Ring Alert (IRA) messages that are broadcast by IRIDIUM satellites. We firstly reverse-engineer many parameters of the IRIDIUM satellite constellation, such as the satellites speed, packet interarrival times, maximum satellite coverage, satellite pass duration, and the satellite beam constellation, to name a few. Later, we adopt the aforementioned statistics to create a detailed model of the satellite network. Subsequently, we propose a solution to detect unintended deviations of a target user from his path, due to GNSS spoofing attacks. We show that our solution can be used efficiently and effectively to verify the position estimated from standard GNSS satellite constellation, and we provide constraints and parameters to fit several application scenarios. All the results reported in this paper, while showing the quality and viability of our proposal, are supported by real data. In particular, we have collected and analyzed hundreds of thousands of IRA messages, thanks to a measurement campaign lasting several days. All the collected data ($1000+$ hours) have been made available to the research community. Our solution is particularly suitable for unattended scenarios such as deserts, rural areas, or open seas, where standard spoofing detection techniques resorting to crowd-sourcing cannot be used due to deployment limitations. Moreover, contrary to competing solutions, our approach does not resort to physical-layer information, dedicated hardware, or multiple receiving stations, while exploiting only a single receiving antenna and publicly-available IRIDIUM transmissions. Finally, novel research directions are also highlighted.

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arXiv2020

BrokenStrokes: On the (in)Security of Wireless Keyboards

Wireless devices resorting to event-triggered communications have been proved to suffer critical privacy issues, due to the intrinsic leakage associated with radio-frequency (RF) emissions. In this paper, we move the attack frontier forward by proposing BrokenStrokes: an inexpensive, easy to implement, efficient, and effective attack able to detect the typing of a pre-defined keyword by only eavesdropping the communication channel used by the wireless keyboard. BrokenStrokes proves itself to be a particularly dreadful attack: it achieves its goal when the eavesdropping antenna is up to 15 meters from the target keyboard, regardless of the encryption scheme, the communication protocol, the presence of radio noise, and the presence of physical obstacles. While we detail the attack in three current scenarios and discuss its striking performance--its success probability exceeds 90% in normal operating conditions--, we also provide some suggestions on how to mitigate it. The data utilized in this paper have been released as open-source to allow practitioners, industries, and academia to verify our claims and use them as a basis for further developments.

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arXiv2020

Cryptomining Makes Noise: a Machine Learning Approach for Cryptojacking Detection

A new cybersecurity attack,where an adversary illicitly runs crypto-mining software over the devices of unaware users, is emerging in both the literature and in the wild . This attack, known as cryptojacking, has proved to be very effective given the simplicity of running a crypto-client into a target device. Several countermeasures have recently been proposed, with different features and performance, but all characterized by a host-based architecture. This kind of solutions, designed to protect the individual user, are not suitable for efficiently protecting a corporate network, especially against insiders. In this paper, we propose a network-based approach to detect and identify crypto-clients activities by solely relying on the network traffic, even when encrypted. First, we provide a detailed analysis of the real network traces generated by three major cryptocurrencies, Bitcoin, Monero, and Bytecoin, considering both the normal traffic and the one shaped by a VPN. Then, we propose Crypto-Aegis, a Machine Learning (ML) based framework built over the results of our investigation, aimed at detecting cryptocurrencies related activities, e.g., pool mining, solo mining, and active full nodes. Our solution achieves a striking 0.96 of F1-score and 0.99 of AUC for the ROC, while enjoying a few other properties, such as device and infrastructure independence. Given the extent and novelty of the addressed threat we believe that our approach, supported by its excellent results, pave the way for further research in this area.

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arXiv2019

PiNcH: an Effective, Efficient, and Robust Solution to Drone Detection via Network Traffic Analysis

We propose PiNcH, a methodology to detect the presence of a drone, its current status, and its movements by leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). PiNcH is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. PiNcH is fully passive and it requires just cheap and general-purpose hardware. To evaluate the effectiveness of our solution, we collected real communication traces originated by a drone running the widespread ArduCopter open-source firmware, currently mounted on-board of a wide range (30+) of commercial amateur drones. We tested our solution against different publicly available wireless traces. The results prove that PiNcH can efficiently and effectively: (i) identify the presence of the drone in several heterogeneous scenarios; (ii) identify the current state of a powered-on drone, i.e., flying or lying on the ground; (iii) discriminate the movements of the drone; and, finally, (iv) enjoy a reduced upper bound on the time required to identify a drone with the requested level of assurance. The effectiveness of PiNcH has been also evaluated in the presence of both heavy packet loss and evasion attacks. In this latter case, the adversary modifies on purpose the profile of the traffic of the drone-RC link to avoid the detection. In both the cited cases, PiNcH continues enjoying a remarkable performance. Further, the comparison against state of the art solution confirms the superior performance of PiNcH in several scenarios. Note that all the drone-controller generated data traces have been released as open-source, to allow replicability and foster follow-up. Finally, the quality and viability of our solution, do prove that network traffic analysis can be successfully adopted for drone identification and status discrimination.

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arXiv2013

Silence is Golden: exploiting jamming and radio silence to communicate

Jamming techniques require just moderate resources to be deployed, while their effectiveness in disrupting communications is unprecedented. In this paper we introduce several contributions to jamming mitigation. In particular, we introduce a novel adversary model that has both (unlimited) jamming reactive capabilities as well as powerful (but limited) proactive jamming capabilities. Under this powerful but yet realistic adversary model, the communication bandwidth provided by current anti-jamming solutions drops to zero. We then present Silence is Golden (SiG): a novel anti jamming protocol that, introducing a tunable, asymmetric communication channel, is able to mitigate the adversary capabilities, enabling the parties to communicate. For instance, with SiG it is possible to deliver a 128 bits long message with a probability greater than 99% in 4096 time slots in the presence of a jammer that jams all the on-the-fly communications and the 74% of the silent radio spectrum---while competing proposals simply fail. The provided solution enjoys a thorough theoretical analysis and is supported by extensive experimental results, showing the viability of our proposal.

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