Unmanned Aerial Systems (UAS) have received a huge interest in military and civil applications. Applications of UAS are dependent on successful communications of these systems with different entities in their networks. A UAS network can include the UAS, ground control station, navigation satellite system, and automatic dependent surveillance-broadcast (ADS-B) receiver. Through these entities, a UAS is vulnerable to different cyber-attacks such as GPS spoofing. In this attack, a malicious user transmits fake signals to the GPS receiver on the UAS. The fake signals can mislead not only the aircraft but also air traffic controllers, leading to serious problems. These problems range from aircraft hijacking to collisions and human casualties. This paper proposes a supervised machine learning method based on the artificial neural network to detect GPS spoofing signals. Different features such as pseudo range, Doppler shift, and signal-to-noise ratio (SNR) are used to perform the classification of GPS signals. We examine and compare the efficiency of one-and two-hidden-layer neural networks with various numbers of hidden neurons. The results show that our proposed method provides a high probability of detection and a low probability of false alarm.

Detection of GPS Spoofing Attacks on Unmanned Aerial Systems

Cyber-attacks on unmanned aerial system networks: Detection, countermeasure, and future research directions

People experience a variety of 3D visual programs, such as 3D cinema, 3D TV and 3D games, making it necessary to deploy reliable methodologies for predicting each viewer's subjective experience. We propose a new methodology that we call multimodal interactive continuous scoring of quality (MICSQ). MICSQ is composed of a device interaction process between the 3D display and a separate device (PC, tablet, etc.) used as an assessment tool, and a human interaction process between the subject(s) and the separate device. The scoring process is multimodal, using aural and tactile cues to help engage and focus the subject(s) on their tasks by enhancing neuroplasticity. Recorded human responses to 3D visualizations obtained via MICSQ correlate highly with measurements of spatial and temporal activity in the 3D video content. We have also found that 3D quality of experience (QoE) assessment results obtained using MICSQ are more reliable over a wide dynamic range of content than obtained by the conventional single stimulus continuous quality evaluation (SSCQE) protocol. Moreover, the wireless device interaction process makes it possible for multiple subjects to assess 3D QoE simultaneously in a large space such as a movie theater, at different viewing angles and distances. We conducted a series of interesting 3D experiments showing the accuracy and versatility of the new system, while yielding new findings on visual comfort in terms of disparity, motion and an interesting relation between the naturalness and depth of field (DOF) of a stereo camera.

http://live.ece.utexas.edu/publications/2014/MISCQ%20TMM.pdf

Multimodal Interactive Continuous Scoring of Subjective 3D Video Quality of Experience

This study developed and tested a research model which examined the impact of user perceptions of self-efficacy SE and virtual environment VE efficacy on the effectiveness of VE training systems. The model distinguishes between the perceptions of one's own capability to perform trained tasks effectively and the perceptions of system performance, regarding the established parameters from literature. Specifically, the model posits that user perceptions will have positive effects on task performance and memory. Seventy-six adults participated in a VE in a controlled experiment, designed to empirically test the model. Each participant performed a series of object assembly tasks. The task involved selecting, rotating, releasing, inserting and manipulating 3D objects. Initially, the results of factor analysis demonstrated dimensionality of two user perception measures and produced a set of empirical validated factors underlining the VE efficacy. The results of regression analysis revealed that SE had a significant positive effect on perceived VE efficacy. No significant effects were found of perceptions on performance and memory. Furthermore, the study provided insights into the relationships between the perception measures and performance measures for assessing the efficacy of VE training systems. The study also addressed how well users learn, perform, adapt to and perceive the VE training, which provides valuable insight into the system efficacy. Research and practical implications are presented at the end of the paper.

The impact of self-efficacy and perceived system efficacy on effectiveness of virtual training systems

Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.

/pdf/dynamic-selection-techniques-for-detecting-gps-spoofing-3c3w1sle.pdf

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Today, the necessity of strong cyber security measures is self-evident. The proliferation of cyber attacks is causing increasing damage to companies, governments and individuals and it is not just the number of cyber security attacks that is increasing: the severity of these attacks is on the rise as well. In particular, aviation systems and Remotely Piloted Aircraft Systems are of highest importance in terms of safety and security. In this paper, we introduce a novel approach to the detection of Global Positioning System (GPS) spoofing attack to Unmanned Aerial Vehicle (UAV) based on the analysis of state estimation using the Support Vector Machine (SVM) as a tool for the anomaly detection.

A SVM-based detection approach for GPS spoofing attacks to UAV

The ideal of correct software has always been the goal of research in the field of Information Technologies. For the next years scientific communities hope for a great challenge: a complete strategy in software programming and software engineering supported by a range of analysis tools to design, develop, integrate, verify and maintain software applications with mathematical rigor. In this challenge formal methods shall play a key role. The adoption of these methodologies should be placed in the proper software engineering framework according to the software domain. In the avionic domain safety-critical software has to accomplish Federal Aviation Regulations by DO-178C or DO-278A means of compliance giving evidence that software implements its intended functions and does not perform unintended functions. DO-178B and DO-278A allowed formal methods without addressing specific process requirements. DO-178C instead is accompanied by a new RTCA Guideline DO-333 "Formal methods supplement to DO-178C and DO-278A". The paper aims to provide an overview of the above mentioned standard. It highlights key concepts about the proper adoption of formal methods to accomplish the standard and the related certification objectives and provides different cases according to the different granted verification techniques.

Formal methods in avionic software certification: the DO-178C perspective

In recent years, the drone market has had a significant expansion, with applications in various fields (surveillance, rescue operations, intelligent logistics, environmental monitoring, precision agriculture, inspection and measuring in the construction industry). Given their increasing use, the issues related to safety, security and privacy must be taken into consideration. Accordingly, the development of new concepts for countermeasures systems, able to identify and neutralize a single (or multiples) malicious drone(s) (i.e., classified as a threat), has become of primary importance. For this purpose, the paper evaluates the concept of a multiplatform counter-UAS system (CUS), based mainly on a team of mini drones acting as a cooperative defensive system. In order to provide the basis for implementing such a system, we present a review of the available technologies for sensing, mitigation and command and control systems that generally comprise a CUS, focusing on their applicability and suitability in the case of mini drones.

/pdf/a-review-of-counter-uas-technologies-for-cooperative-113drl7a.pdf

A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones

Requirements Engineering (RE) is a key discipline for the success of software projects. Consistency, completeness, and accuracy are the requirements quality properties to be guaranteed by the verification task in RE. An overview of the actual trends in RE is briefly summarized, focusing more closely on the requirements verification quality properties. Completeness results is the most difficult property to guarantee. It is hard to capture the software behavior against the whole external context. In the last years, research has focused its attention to the application of semantic Web techniques to the different tasks of RE. The adoption of ontologies seems promising to achieve the proper level of formalism and to argue on quality properties. This paper presents a survey of the main concepts that need to be accounted for requirement verification, and proposes an ontological engineering approach to demonstrate the overlapping of requirements against the external context.

A Semantic Driven Approach for Requirements Verification

Dependability of a system is commonly referred to its reliability, its availability and its maintenability (RAM), but when this concept is applied to user interfaces there is no common agreement on what aspects of user-system interaction are related to a satisfactory RAM level for the whole system. In particular, when dealing with haptic systems, interface dependability may become a crucial issue in medical and in military domains when life-critical systems are to be manipulated or where costly remote control operations are to be performed, like in industrial processes control or in aerospace/automotive engineering and manufacturing. This paper discusses the role of dependability in haptic user interfaces, aiming to the definition of a framework for the assessment of the usability and dependability properties of haptic systems and their possible correlations. The research is based on the analysis of a visual-haptic-based simulator targeted to maintenance activity training for aerospace industry which is taken as a case study. As a result, we propose a novel framework able to collect and then process relevant interaction data during the execution of haptic tasks, enabling to analyze dependability vs. usability correlations.

Gabriella Gigante

Papers

A SVM-based detection approach for GPS spoofing attacks to UAV

Formal methods in avionic software certification: the DO-178C perspective

A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones

A Semantic Driven Approach for Requirements Verification

Dependability issues in visual-haptic interfaces