470 research outputs found
A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification
For practical automatic speaker verification (ASV) systems, replay attack
poses a true risk. By replaying a pre-recorded speech signal of the genuine
speaker, ASV systems tend to be easily fooled. An effective replay detection
method is therefore highly desirable. In this study, we investigate a major
difficulty in replay detection: the over-fitting problem caused by variability
factors in speech signal. An F-ratio probing tool is proposed and three
variability factors are investigated using this tool: speaker identity, speech
content and playback & recording device. The analysis shows that device is the
most influential factor that contributes the highest over-fitting risk. A
frequency warping approach is studied to alleviate the over-fitting problem, as
verified on the ASV-spoof 2017 database
Detecting ADS-B Spoofing Attacks using Deep Neural Networks
The Automatic Dependent Surveillance-Broadcast (ADS-B) system is a key
component of the Next Generation Air Transportation System (NextGen) that
manages the increasingly congested airspace. It provides accurate aircraft
localization and efficient air traffic management and also improves the safety
of billions of current and future passengers. While the benefits of ADS-B are
well known, the lack of basic security measures like encryption and
authentication introduces various exploitable security vulnerabilities. One
practical threat is the ADS-B spoofing attack that targets the ADS-B ground
station, in which the ground-based or aircraft-based attacker manipulates the
International Civil Aviation Organization (ICAO) address (a unique identifier
for each aircraft) in the ADS-B messages to fake the appearance of non-existent
aircraft or masquerade as a trusted aircraft. As a result, this attack can
confuse the pilots or the air traffic control personnel and cause dangerous
maneuvers. In this paper, we introduce SODA - a two-stage Deep Neural Network
(DNN)-based spoofing detector for ADS-B that consists of a message classifier
and an aircraft classifier. It allows a ground station to examine each incoming
message based on the PHY-layer features (e.g., IQ samples and phases) and flag
suspicious messages. Our experimental results show that SODA detects
ground-based spoofing attacks with a probability of 99.34%, while having a very
small false alarm rate (i.e., 0.43%). It outperforms other machine learning
techniques such as XGBoost, Logistic Regression, and Support Vector Machine. It
further identifies individual aircraft with an average F-score of 96.68% and an
accuracy of 96.66%, with a significant improvement over the state-of-the-art
detector.Comment: Accepted to IEEE CNS 201
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A Testbed for Developing and Evaluating GNSS Signal Authentication Techniques
An experimental testbed has been created for developing
and evaluating Global Navigation Satellite System (GNSS)
signal authentication techniques. The testbed advances the state
of the art in GNSS signal authentication by subjecting candidate
techniques to the strongest publicly-acknowledged GNSS spoofing
attacks. The testbed consists of a real-time phase-coherent GNSS
signal simulator that acts as spoofer, a real-time softwaredefined
GNSS receiver that plays the role of defender, and
post-processing versions of both the spoofer and defender. Two
recently-proposed authentication techniques are analytically and
experimentally evaluated: (1) a defense based on anomalous
received power in a GNSS band, and (2) a cryptographic
defense against estimation-and-replay-type spoofing attacks. The
evaluation reveals weaknesses in both techniques; nonetheless,
both significantly complicate a successful GNSS spoofing attackAerospace Engineering and Engineering Mechanic
When the Differences in Frequency Domain are Compensated: Understanding and Defeating Modulated Replay Attacks on Automatic Speech Recognition
Automatic speech recognition (ASR) systems have been widely deployed in
modern smart devices to provide convenient and diverse voice-controlled
services. Since ASR systems are vulnerable to audio replay attacks that can
spoof and mislead ASR systems, a number of defense systems have been proposed
to identify replayed audio signals based on the speakers' unique acoustic
features in the frequency domain. In this paper, we uncover a new type of
replay attack called modulated replay attack, which can bypass the existing
frequency domain based defense systems. The basic idea is to compensate for the
frequency distortion of a given electronic speaker using an inverse filter that
is customized to the speaker's transform characteristics. Our experiments on
real smart devices confirm the modulated replay attacks can successfully escape
the existing detection mechanisms that rely on identifying suspicious features
in the frequency domain. To defeat modulated replay attacks, we design and
implement a countermeasure named DualGuard. We discover and formally prove that
no matter how the replay audio signals could be modulated, the replay attacks
will either leave ringing artifacts in the time domain or cause spectrum
distortion in the frequency domain. Therefore, by jointly checking suspicious
features in both frequency and time domains, DualGuard can successfully detect
various replay attacks including the modulated replay attacks. We implement a
prototype of DualGuard on a popular voice interactive platform, ReSpeaker Core
v2. The experimental results show DualGuard can achieve 98% accuracy on
detecting modulated replay attacks.Comment: 17 pages, 24 figures, In Proceedings of the 2020 ACM SIGSAC
Conference on Computer and Communications Security (CCS' 20
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