The Texas Spoofing Test Battery: Toward a Standard for Evaluating GPS Signal Authentication Techniques

A battery of recorded spoofing scenarios has been compiled for evaluating civil Global Positioning System (GPS) signal authentication techniques. The battery can be considered the data component of an evolving standard meant to define the notion of spoof resistance for commercial GPS receivers. The setup used to record the scenarios is described. A detailed description of each scenario reveals readily detectable anomalies that spoofing detectors could target to improve GPS securityAerospace Engineering and Engineering Mechanic

Development of an Improved Fingerprint Feature Extraction Algorithm for Personal Verification

New and sophisticated technologies are regularly developed to counter every new wave of breaches in data security. At the heart of some of these technologies is the personal verification system that rests on the oars of biometrics. Biometric systems use unique physical and behavioral traits for identification or verification. In this paper, an improved fingerprint feature extraction algorithm for personal verification is proposed. The improved fingerprint feature extraction algorithm is capable of recognizing authorized individuals and differentiating them from fraudulent imposters. The input images were preprocessed before extracting robust features for matching. Euclidean distance was used for classification. The proposed system was tested using the fingerprint images of fifty registered individuals and thirty imposters. The results obtained were a False Acceptance Rate and False Rejection Rate of 16% and 24% respectively. It is also faster than other feature extraction algorithms by forty (40) seconds Keywords: Fingerprint, biometrics, robust features, division into blocks, ridge pattern, euclidean distance, personal verification, feature extraction, classification

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors

Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD.1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD.1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication

Technical challenges for identification in mobile environments

This report describes technical challenges and requirements for identification of individuals in mobile (i.e. non-stationary) environments as e.g. required by the ¿European Mobile Identification Interoperability Group¿ (MOBIDIG). It is intended to support relevant stakeholders as law enforcement agencies or immigration offices, active in the area of identification of individuals in mobile environments. It offers some guidance for future technical work at the MOBIDIG to be respected in their work plan. Furthermore, it may be used as a first orientation for the general future work for identification in mobile environments using digital or electronically stored data. After the introduction and some background of MOBIDIG and its policy context, the document presents the intention, main objectives and some information about the scope of work of the group. The following proposals, suggestions and recommendations presented are explicitly focusing on technology. Organizational and procedural issues are out of focus of this document and need to be addressed separately in further documents.JRC.DG.G.6-Security technology assessmen

Secure ADS-B: Towards Airborne Communications Security in the Federal Aviation Administration\u27s Next Generation Air Transportation System

The U.S. Congress has mandated that all aircraft operating within the National Airspace System, military or civilian, be equipped with ADS-B transponders by the year 2020. The ADS-B aircraft tracking system, part of the Federal Aviation Administration\u27s NextGen overhaul of the Air Transportation System, replaces Radar-based surveillance with a more accurate satellite-based surveillance system. However, the unencrypted nature of ADS-B communication poses an operational security risk to military and law enforcement aircraft conducting sensitive missions. The non-standard format of its message and the legacy communication channels used by its transponders make the ADS-B system unsuitable for traditional encryption mechanisms. FPE, a recent development in cryptography, provides the ability to encrypt arbitrarily formatted data without padding or truncation. Indeed, three new algorithms recommended by the NIST, may be suitable for encryption of ADS-B messages. This research assesses the security and hardware performance characteristics of the FF1, FF2, and FF3 algorithms, in terms of entropy of ciphertext, operational latency and resource utilization when implemented on a Field-Programmable Gate Array. While all of the algorithms inherit the security characteristics of the underlying AES block cipher, they exhibit differences in their performance profiles. Findings demonstrate that a Bump-in-the-Wire FPE cryptographic engine is a suitable solution for retrofitting encryption to ADS-B communication

An Assessment of GPS Spoofing Detection Via Radio Power and Signal Quality Monitoring for Aviation Safety Operations

the ever growing threat of Global Positioning necessary for the aviation &mdash; System (GP sector to develop an effective means of detection. This paper focuses on two complementary spoofing detection techniques that are available on commercial GPS receivers and thus require no additional hardware to operate. The primary methodology for detection is using this combination of: Radio Power Monitoring (RPM) metrics, levering both Automatic Gain Control (AGC) and C/N0 measurements, along with multiple correlations for signal distortion to provide a best practices spoofing detection algorithm which is able to distinguish between interference and spoofing. The paper first assess nominal statistics for both metrics compiled from over 250 hours of nominal data collected from multiple Wide Area Augmentation System (WAAS) stations. This data is compared to previous collections to validate the thresholds and false alarms rates and establish a complete testing methodology. These test and thresholds are then assessed with the Texas Spoofing Test Battery (TEXBAT) series of GPS spoofing data sets to confirm detection capabilities. Finally, these test and thresholds are applied to assess the GPS signal of six extended flights&nbsp;&nbsp; r the United States to assess the performance on an aircraft.</p

Biometric Spoofing: A JRC Case Study in 3D Face Recognition

Based on newly available and affordable off-the-shelf 3D sensing, processing and printing technologies, the JRC has conducted a comprehensive study on the feasibility of spoofing 3D and 2.5D face recognition systems with low-cost self-manufactured models and presents in this report a systematic and rigorous evaluation of the real risk posed by such attacking approach which has been complemented by a test campaign. The work accomplished and presented in this report, covers theories, methodologies, state of the art techniques, evaluation databases and also aims at providing an outlook into the future of this extremely active field of research.JRC.G.6-Digital Citizen Securit

TESTS Functional Description CDRL A002

Report is a functional description for the Tactical Electronics Simulation Test System and written to provide hardware and software development requirements that must be satisfied to achieve a simulation based test system that can be used to conduct development tests and evaluation of advanced identification friend or foe (IFF) systems, and information on the performance requirements, preliminary design, and user impacts for the defined approach.; Contents: 50514 01 General -- System summary -- Detailed characteristics -- Design details -- Environment -- System development plan

Facial image encryption for secure face recognition system

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks