Robust Voice Liveness Detection and Speaker Verification Using Throat Microphones

While having a wide range of applications, automatic speaker verification (ASV) systems are vulnerable to spoofing attacks, in particular, replay attacks that are effective and easy to implement. Most prior work on detecting replay attacks uses audio from a single acousticmicrophone only, leading to difficulties in detecting high-end replay attacks close to indistinguishable from live human speech. In this paper, we study the use of a special body-conducted sensor, throat microphone (TM), for combined voice liveness detection (VLD) and ASV in order to improve both robustness and security of ASV against replay attacks.We first investigate the possibility and methods of attacking a TM-based ASV system, followed by a pilot data collection. Second, we study the use of spectral features for VLD using both single-channel and dualchannel ASV systems. We carry out speaker verification experiments using Gaussian mixture model with universal background model (GMM-UBM) and i-vector based systems on a dataset of 38 speakers collected by us. We have achieved considerable improvement in recognition accuracy, with the use of dual-microphone setup. In experiments with noisy test speech, the false acceptance rate (FAR) of the dual-microphone GMM-UBM based system for recorded speech reduces from 69.69% to 18.75%. The FAR of replay condition further drops to 0% when this dual-channel ASV system is integrated with the new dual-channel voice liveness detector.</p

Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing

Audio anti-spoofing for automatic speaker verification aims to safeguard users' identities from spoofing attacks. Although state-of-the-art spoofing countermeasure(CM) models perform well on specific datasets, they lack generalization when evaluated with different datasets. To address this limitation, previous studies have explored large pre-trained models, which require significant resources and time. We aim to develop a compact but well-generalizing CM model that can compete with large pre-trained models. Our approach involves multi-dataset co-training and sharpness-aware minimization, which has not been investigated in this domain. Extensive experiments reveal that proposed method yield competitive results across various datasets while utilizing 4,000 times less parameters than the large pre-trained models.Comment: Interspeech 202

Detection solution analysis for simplistic spoofing attacks in commercial mini and micro UAVs

Enamus droone kasutab lennundusest pärit GPS navigatsiooniseadmeid, millel puuduvad turvaprotokollid ning nende riskioht pahatahtlike rünnakute sihtmärgina on kasvanud hüppeliselt lähimineviku arengute ja progressi tõttu SDR ja GNSS simulatsioonitarkvara valdkonnas. See on loonud ligipääsu tehnikale amatöörkasutajatele, millel on saatja aadressi võltsimise jõudlus. Need potensiaalsed rünnakud kuuluvad lihtsakoeliste kategooriasse, kuid selle uurimustöö tulemusena selgus, et nendes rünnakute edukuses on olulised erinevused teatud GPS vastuvõtjate ja konfiguratsioonide vahel. \n\rSee uurimustöö analüüsis erinevaid saatja aadressi võltsimise avastamise meetodeid, mis olid avatud kasutajatele ning valis välja need, mis on sobilikud mini- ja mikrodroonide tehnonõuetele ja operatsioonistsenaariumitele, eesmärgiga pakkuda välja GPS aadresside rünnakute avastamiseks rakenduste tasandil avatud allikakoodiga Ground Control Station tarkvara SDK. Avastuslahenduse eesmärk on jälgida ja kinnitada äkilisi, abnormaalseid või ebaloogilisi tulemväärtusi erinevates drooni sensiorites lisaallkatest pärit lisainfoga. \n\rLäbiviidud testid kinnitavad, et olenevalt olukorrast ja tingimustest saavad saatja aadressi võltsimise rünnakud õnnestuda. Rünnakud piiravad GPS mehanismide ligipääsu, mida saab kasutada rünnakute avastuseks. Neid rünnakuid puudutav info asetseb infovoos või GPSi signaalprotsessi tasandis, kuid seda infot ei saa haarata tasandile kus SDK tarkvara haldab kõigi teiste sensorite infot.Most of UAVs are GPS navigation based aircrafts that rely on a system with lack of security, their latent risk against malicious attacks has been raised with the recent progress and development in SDRs and GNSS simulation software, facilitating to amateurs the accessibility of equipment with spoofing capabilities. The attacks which can be done with this setup belong to the category simplistic, however, during this thesis work there are validated different cases of successful results under certain GPS receivers’ state or configuration.\n\rThis work analysis several spoofing detection methods found in the open literature, and selects the ones which can be suitable for mini and micro UAV technical specifications and operational scenario, for proposing a GPS spoofing detection solution developed in the application layer of an open source code Ground Control Station software SDK. The detection solution is intended to monitor and correlate abrupt, abnormal or unreasonable values of different sensors of the UAV with data obtained from available additional sources.\n\rThe conducted tests validate the cases and circumstances where the spoofing attacks were successful. Limitations include the lack of mechanisms to access GPS values which can be useful for detection spoofing attacks, but reside in the data bit or signal processing layer of the GPS and can not be retrieve to the layer where the SDK in computing all data of other sensors

A Survey on Spoofing and Selective Forwarding Attacks on Zigbee based WSN

The main focus of WSN is to gather data from the physical world. It is often deployed for sensing, processing as well as disseminating information of the targeted physical environments. The main objective of the WSN is to collect data from the target environment using sensors as well as transmit those data to the desired place of choice. In order to achieve an efficient performance, WSN should have efficient as well as reliable networking protocols. The most popular technology behind WSN is Zigbee. In this paper a pilot study is done on important security issues on spoofing and selective forwarding attack on Zigbee based WSN. This paper identifies the security vulnerabilities of Zigbee network and gaps in the existing methodologies to address the security issues and will help the future researchers to narrow down their research in WSN.Keywords: Zigbee, WSN, Protocol Stack, Spoofing and Selective Forwarding

The development of a database taxonomy of vulnerabilities to support the study of denial of service attacks

As computer networks continue to proliferate, the world\u27s dependence on a secure communication infrastructure is of prime importance. Disruption of service through Denial of Service (DoS) attacks can result in great financial loss for Internet-based companies and major inconveniences for users of Internet services. The purpose of this two-year study was to study and understand network denial of service attacks so that methods may be developed to detect and prevent them.;Initially, the researcher constructed a database of system and network exploits that revealed the underlying vulnerabilities in the software or protocols they attack. The database was populated with exploits posted at popular reporting sites such as Rootshell, Bugtraq, Security Focus. To encourage the use of a common vulnerability taxonomy and to facilitate sharing of data, parts of the classification scheme proposed by Krsul (1998) in his research were included, as well as developing a taxonomy tree based on the current research.;Sifting through the reports and categorizing the attacks has been a challenging experience; and creating categories that are unambiguous, repeatable, and exhaustive has proven to be a difficult task. The results were two to three methods of classification that are useful for developing categories of vulnerabilities. The next phase of the project was to look for any clustering of attacks based on these vulnerability categories, and to determine if effective countermeasures can be deployed against them. Although past history is no guarantee of future exploit activity, it is hoped that the countermeasures proposed based on these 630 exploits will remain valid for future DoS attacks. Toward this goal, the research made use of data mining software packages to plot the various categories of attacks so that the interrelationships could be more easily discovered and studied. A sampling of the database plots, an interpretation of the plotted data, and the countermeasures proposed for the vulnerability categories developed as part of the database creation are presented in this research

Security Evaluation of GNSS Signal Quality Monitoring Techniques against Optimal Spoofing Attacks

GNSSs have a significant impact on everyday life and, therefore, the are increasingly becoming an attractive target for illicit exploitation. As such, anti-spoofing algorithms have become an relevant research topic within the GNSS discipline. This Thesis provides a review of recent research in the field of GNSS spoofing/anti-spoofing, designs a method to generate an energy optimal spoofing signal and evaluates the performance of the anti-spoofing signal quality monitoring techniques against it