Attaque par usurpation d'identité

Dans le contexte de la sécurité de l'information, et en particulier de la sécurité des réseaux, une attaque par usurpation d'identité est une situation dans laquelle une personne ou un programme réussit à s'identifier à un autre en falsifiant des données, pour obtenir un avantage illégitime. Internet Usurpation et TCP/IP De nombreux protocoles de la suite TCP/IP ne fournissent pas de mécanismes pour authentifier la source ou la destination d'un message, ce qui les rend vulnérables aux attaques d'usurpation lorsque des précautions supplémentaires ne sont pas prises par les applications pour vérifier l'identité de l'hôte d'envoi ou de réception. L'usurpation d'adresse IP et l'usurpation d'adresse ARP en particulier peuvent être utilisées pour tirer parti des attaques de type man-in-the-middle contre des hôtes sur un réseau informatique. Les attaques par usurpation d'identité qui tirent parti des protocoles de la suite TCP/IP peuvent être atténuées par l'utilisat

Trustworthy speaker recognition with minimal prior knowledge using neural networks

Hannah Muckenhirn

The performance of speaker recognition systems has considerably improved in the last decade. This is mainly due to the development of Gaussian mixture model-based systems and in particular to the use of i-vectors. These systems handle relatively well noise and channel mismatches and yield a low error rate when confronted with zero-effort impostors, i.e. impostors using their own voice but claiming to be someone else. However, speaker verification systems are vulnerable to more sophisticated attacks, called presentation or spoofing attacks. In that case, the impostors present a fake sample to the system, which can either be generated with a speech synthesis or voice conversion algorithm or can be a previous recording of the target speaker. One way to make speaker recognition systems robust to this type of attack is to integrate a presentation attack detection system. Current methods for speaker recognition and presentation attack detection are largely based on short-term spectral processing. This has certain limitations. For instance, state-of-the-art speaker verification systems use cepstral features, which mainly capture vocal tract system characteristics, although voice source characteristics are also speaker discriminative. In the case of presentation attack detection, there is little prior knowledge that can guide us to differentiate bona fide samples from presentation attacks, as they are both speech signals that carry the same high level information, such as message, speaker identity and information about environment. This thesis focuses on developing speaker verification and presentation attack detection systems that rely on minimal assumptions. Towards that, inspired by recent advances in deep learning, we first develop speaker verification approaches where speaker discriminative information is learned from raw waveforms using convolutional neural networks (CNNs). We show that such approaches are capable of learning both voice source related and vocal tract system related speaker discriminative information and yield performance competitive to state of the art systems, namely i-vectors and x-vectors-based systems. We then develop two high performing approaches for presentation attack detection: one based on long-term spectral statistics and the other based on raw speech modeling using CNNs. We show that these two approaches are complementary and make the speaker verification systems robust to presentation attacks. Finally, we develop a visualization method inspired from the computer vision community to gain insight about the task-specific information captured by the CNNs from the raw speech signals.

EPFL2019

Evading Voltage-Based Intrusion Detection on Automotive CAN

Rohit Bhatia, Mathias Josef Payer, Duo Xu

The controller area network (CAN) is widely adopted in modern automobiles to enable communications among in-vehicle electronic control units (ECUs). Lacking mainstream network security capabilities due to resource constraints, the CAN is susceptible to the ECU masquerade attack in which a compromised (attacker) ECU impersonates an uncompromised (victim) ECU and spoofs the latter's CAN messages. A cost-effective state-of-the-art defense against such attacks is the CAN bus voltage-based intrusion detection system (VIDS), which identifies the source of each message using its voltage fingerprint on the bus. Since the voltage fingerprint emanates from an ECU's hardware characteristics, an attacker ECU by itself cannot controllably modify it. As such, VIDS has been proved effective in detecting masquerade attacks that each involve a single attacker. In this paper, we discover a novel voltage corruption tactic that leverages the capabilities of two compromised ECUs (i.e., an attacker ECU working in tandem with an accomplice ECU) to corrupt the bus voltages recorded by the VIDS. By exploiting this tactic along with the fundamental deficiencies of the CAN protocol, we propose a novel masquerade attack called DUET, which evades all existing VIDS irrespective of the features and classification algorithms employed in them. DUET follows a two-stage attack strategy to first manipulate a victim ECU's voltage fingerprint during VIDS retraining mode, and then impersonate the manipulated fingerprint during VIDS operation mode. Our evaluation of DUET on real CAN buses (including three in two real cars) demonstrates an impersonation success rate of at least 90% in evading two state-of-the-art VIDS. Finally, to mitigate ECU masquerade attacks, we advocate the development of cost-effective defenses that break away from the "attack vs. IDS" arms race. We propose a lightweight defense called RAID, which enables each ECU to make protocol-compatible modifications in its frame format generating a unique dialect (spoken by ECUs) during VIDS retraining mode. RAID prevents corruption of ECUs' voltage fingerprints, and re-enables VIDS to detect all ECU masquerade attacks including DUET.

INTERNET SOC2021

Trustworthy speaker recognition with minimal prior knowledge using neural networks

Hannah Muckenhirn

EPFL2019

Evading Voltage-Based Intrusion Detection on Automotive CAN

Rohit Bhatia, Mathias Josef Payer, Duo Xu

INTERNET SOC2021

Attaque par usurpation d'identité

Trustworthy speaker recognition with minimal prior knowledge using neural networks

On Joint Optimization of Automatic Speaker Verification and Anti-spoofing in the Embedding Space

Evading Voltage-Based Intrusion Detection on Automotive CAN

On Joint Optimization of Automatic Speaker Verification and Anti-spoofing in the Embedding Space

Trustworthy speaker recognition with minimal prior knowledge using neural networks

Evading Voltage-Based Intrusion Detection on Automotive CAN