This paper presents a framework for remote monitoring of unmanned aerial vehicles (UAVs) developed specifically for urban environments through crowd sensing. This system is built around a centralized control mechanism that manages UAV traffic to reduce the risks of airspace congestion and sudden accidents. The UAVs periodically broadcast their remote identifications (RIDs), which are received by ground observers on their mobile devices through dedicated applications. These RIDs contain multiple information, including the drone's ID, location, altitude, velocity, and a timestamp. The ground observers then forward the received RIDs to the surveillance station after appending their coordinates, ID, and a time stamp. A key component of our approach is the incorporation of signal quality metrics - path loss, shadowing, and received signal strength (RSS) - along with distance considerations in the activation process of ground observers for RID message forwarding. To address potential data congestion and incentive overuse in densely populated areas, our model uses a predictive strategy for dynamic observer activation, ensuring that the surveillance system processes an optimal number of RID reports efficiently. This comprehensive consideration of both signal integrity and spatial proximity significantly improves the detection and monitoring precision of UAVs, increasing the system's ability to effectively monitor UAV activities while conserving resources in urban environments.

Lecture-free teaching and learning is an instructional method that omits the traditional lecture and replaces it with self-paced learning activities in or outside the classroom. While previous research showed evidence for the effectiveness of this method, the impact of gender on students' performance and perceptions remained unexplored. This study attempts to close this gap by analyzing learning and self-report data from two lecture-free courses in a computer engineering program. Our findings, drawn from learning analytics, reveal that the performance of female students in the learning activities is comparable to that of their male counterparts, even though males complete the activities more quickly, on average. Regarding perceptions, female students report slightly lower or higher levels of interest and challenge, respectively. Moreover, the gender effect can be moderated by the complexity of the learning activity. These results suggest that engineering programs employing lecture-free instruction should consider allocating more time for female learners. Additionally, the learning activities should be designed in a way that enhances females' interest and motivation and lowers their perceived level of challenge. Further research is needed to understand the sources of interest and challenges in lecture-free learning activities and to examine the correlations between interest, challenge, and performance.

Outsourcing chip production is common among semiconductor vendors to cope with the increasing demand for integrated circuits. This has resulted in several security issues in the chip supply chain, including hardware trojans, intellectual property theft, and overproduction. The concept of zero trust –never trust, always verify– presents a promising solution for ensuring the authenticity of Integrated Circuits (ICs), particularly in critical systems where adversary attacks can cause significant losses or damage. The Security Protocol and Data Model (SPDM) is a reliable protocol that uses certificates to ensure the authenticity of ICs. Based on this protocol, the presented paper proposes a chip-to-chip zero-trust security architecture that aims to verify the authenticity of any connected peripheral before its use. The contributions include an overview of the proposed architecture, the anti-clock stretching technique, and an analysis of the challenges encountered during the implementation and execution.

Remote Identification (RID) technology provides a digital license plate for Unmanned Aerial Vehicles (UAVs) to monitor airspace and enforce lawful behavior. RID enables the detection of drones from long distances and the differentiation between legal and illegal operations. This paper proposes a low-power Add-on RID (AoRID) module as part of a comprehensive airspace monitoring system comprised of three components: the AoRID module as the transmitter, a smartphone as the receiver, and an Unmanned Traffic Management (UTM) database. The system offers advantages in its simplicity, effectiveness, and affordability.

Outsourcing chip production is common among semiconductor vendors to cope with the increasing demand for integrated circuits. This has resulted in several security issues in the chip supply chain, including hardware trojans, intellectual property theft, and overproduction. Zero-trust presents a promising solution for ensuring the authenticity of Integrated Circuits (ICs), particularly in critical systems where adversary attacks can cause significant losses or damage. The Security Protocol and Data Model (SPDM) is a reliable protocol that uses certificates to ensure the authenticity of ICs. Based on this protocol, the presented paper proposes a chip-to-chip zero-trust security architecture that aims to verify the authenticity of any connected peripheral before its use. The contributions include an overview of the proposed architecture, implementation and formal verification of the SPDM protocol, and analysis of the challenges encountered during the implementation and execution.

With the immense interest in ChatGPT worldwide, education has seen a mix of both excitement and skepticism. To properly evaluate its impact on education, it is crucial to understand how far it can help students without prior knowledge answer assessment questions. This study aims to address this question as well as the impact of the question type. We conducted multiple experiments with computer engineering students (experiment group: n =41 to 56), who were asked to use ChatGPT to answer previous test questions before learning about the related topics. Their scores were then compared with the scores of previous-term students who answered the same questions in a quiz or exam setting (control group: n =24 to 61). The results showed a wide range of effect sizes, from -2.55 to 1.23, depending on the question type and content. The experiment group performed best answering code analysis and conceptual questions but struggled with code completion and questions that involved images. However, the performance in code generation tasks was inconsistent. Overall, the ChatGPT group’s answers lagged slightly behind the control group’s answers with an effect size of -0.16. We conclude that ChatGPT, at least in the field of this study, is not yet ready to rely on by students who do not have sufficient background to evaluate generated answers. We suggest that educators try using ChatGPT and educate students on effective questioning techniques and how to assess the generated responses. This study provides insights into the capabilities and limitations of ChatGPT in education and informs future research and development.

Unmanned aerial vehicles are gaining immense attention in diverse commercial and civil applications. However, adopting this technology will rely on management services that support safe operation. UAV traffic management systems (UTM) aim to facilitate safe, efficient, and fair access to the low-altitude airspace. However, these systems are centralized and lack protocols for secure interaction between the system agents such as authorities, service providers, and end-users. Another limitation of proposed UTM architectures is the absence of efficient mechanisms to enforce airspace rules and regulations. To address these issues, we present a decentralized UTM protocol that controls access to airspace and ensures the security objectives of confidentiality, integrity, and availability. For this purpose, we exploit features of the blockchain and smart-contract technologies. In addition, we employ a mobile crowdsensing technique to seamlessly enforce airspace rules and regulations that govern UAV operations. The solution is implemented on top of the Ethereum platform and evaluated using four different tools for smart-contract verification. We also provide security and performance analysis of the solution. For reproducibility, we made our implementation publicly available on Github.

Drone flight controls and ground stations are known to be vulnerable to attacks. Besides posing a threat to integrity and confidentiality of drone data, their vulnerabilities endanger safety. Onboard continuous authentication is a vital countermeasure to hijacking attempts. Motivated by the success of Machine Learning (ML) techniques in the field of behavioral biometrics, this paper investigates the use of sensor readings generated onboard drones and of control data reaching them from the ground to feed an onboard ML model continuously authenticating pilots. We analyze fifteen inertial measurement units (IMU) and four radio control signals obtained from the drone's onboard sensors or coming from its remote controller, to identify the controlling pilot. We investigate three sequence classification schemes. In the first scheme, raw sensor sequences are directly fed to a deep Long/Short-Term Memory (LSTM) learner. In the second scheme, frequency-domain features are extracted from the data sequences and interpreted by an ensemble of random trees. In the third scheme, instantaneous sensor readings are classified using the same ensemble learning technique as in the second scheme, yet a final decision fusion method is adopted to provide a sequence-based decision. We compare the three schemes in terms of accuracy, complexity, and delay. The winning scheme is validated and tested against an unseen intruder scenario. Our tests show that an LSTM model trained with data from 19 users is able to identify the operating user at a 97% accuracy, while it can identify an unknown intruder at an average accuracy of 73%.

Unmanned aerial vehicles (UAVs) can pose a serious threat to critical infrastructure which has motivated researchers to develop solutions for early detection. Nevertheless, the problem remains unsolved due to the limitations of the current detection techniques. In this paper, a vision-based approach using deep learning and a pan-tilt-zoom camera is proposed. In addition to detecting and tracking UAVs at long distances, the approach also assesses the threat level of the intruder UAVs based on their orientation. The proposed system offers long-range coverage while being cheap and practically feasible.

Random forest is a powerful machine learning scheme which finds applications in real-time systems such as unmanned aerial vehicles. In such applications not only the classification performance is relevant but also several non-functional requirements including the classification time, the memory usage and the power consumption. This paper proposes a new approach to improve the real-time behavior of a random forest classifier. This is accomplished by reducing the number of evaluated nodes and branches as well as by reducing the branch length in the underlying binary decision trees with numerical split values. A hardware architecture is presented for the improved tree-based classification method. A proof-of-concept implementation on an FPGA platform and some preliminary results show the advantage of this approach compared to related work.

For over 60 years, textbooks on digital logic design have followed a semi-unified way to introduce the concept of digital memory. This method starts from a simple gate-based SR latch and applies several rectifications on it to obtain the D flip-flop. This method seems to have been influenced by the historical evolution of the SR latch from its relay-based variant in the 19th century, through its vacuum-tube version, to the transistor-based implementation. I argue that introducing the digital memory using this approach is problematic for learning as it implies several technical and pedagogical issues. This paper highlights these issues and proposes an alternative method which is based on an intuitive specification of the memory concept and a careful reasoning of related ideas including edge-sensitivity, synchronization, and the clock. I recommend adopting this method in textbooks and other learning resources.

The design of combinatorial and sequential circuits relies on multiple logical transformations that can show different levels of complexity. This paper investigates the intrinsic complexity of eight logical transformation problems: (1)-from human-language statement into formal function, (2)-from formal function into truth table, (3)-from truth table into formal function, (4)-from formal function into k-map, (5)-from truth table into k-map, (6)-from k-map into minimized formal function, (7)-from formal function into minimized formal function using Boolean algebra, and (8)-from formal function into digital circuit. 27 potential complexity variables were first identified and specified and a total of 303 test items/problems were generated and solved by up to 43 students each with time recording. The level of intrinsic complexity was defined based on average solving time and error ratio. Regression models were generated to establish a predictive relationship between the intrinsic complexity level and the complexity variables for each transformation problem. Apart from Transformation 7, the regression models showed adjusted R-square values between 81% and 94%. These models can be used to predict the solving time of new problems towards more reliable test design.

Analysis of interactions with remotely controlled devices has been used to detect the onset of hijacking attacks, as well as for forensics analysis, e.g., to identify the human controller. Its effectiveness is known to depend on the remote device type as well as on the properties of the remote control signal. This paper shows that the radio control signal sent to an unmanned aerial vehicle (UAV) using a typical transmitter can be captured and analyzed to identify the controlling pilot using machine learning techniques. Twenty trained pilots have been asked to fly a high-end research drone through three different trajectories. Control data have been collected and used to train multiple classifiers. Best performance has been achieved by a random forest classifier that achieved accuracy around 90% using simple time-domain features. Extensive tests have shown that the classification accuracy depends on the flight trajectory and that the pitch, roll, yaw, and thrust control signals show different levels of significance for pilot identification. This result paves the way to a number of security and forensics applications, including continuous identification of UAV pilots to mitigate the risk of hijacking.

Any exam problem shows a specific level of intrinsic complexity that affects its solving time. This paper investigates the factors that affect the intrinsic complexity of determining the total resistance of a resistive circuit. A sample of 46 circuits was generated and solved by 27 students with time recording. Regression analysis showed that the solving time is significantly affected by the number of arithmetic operations required to solve the problem, by the value range of the resistors, as well as by the inconsistency of the given resistor units. Other factors that reflect the circuit topology including the number of circuit nodes and branches are either correlated with the number of arithmetic operations or insignificant in the regression analysis. The outcome of the study is a complexity model that can be used to predict the solving time of new problems of the same class which allows to develop more reliable exam problems. Different ways to control the complexity level are discussed in depth.

The authentication of flight data in Unmanned Aerial Vehicles (UAVs) is highly critical because processing fake commands by the on-board flight controller can cause fatal consequences. Depending on the criticality level of the UAV mission, multi-layer authentication techniques can be useful to assure higher security levels. This paper proposes a technique for continuous authentication of flight data based on the behavior of the UAV operator, who flies the vehicle in a manual mode. In contrast to one-time authentication, this technique allows for an on-the-fly identification of malicious commands aiming at manipulating, hijacking, or crashing the UAV. The operator behavior is defined by the sequence of flight commands sent to the drone using a standard radio control transmitter. This is based on our assumption that every UAV operator has a distinctive pattern when it comes to controlling a UAV using transmitter's levers or joysticks. To verify this assumption, we captured 22,402 commands from five different operators, who flew a small multicopter UAV using a standard flight transmitter. Machine learning was applied to train a random forest classifier. The results show that the UAV operators can be identified with accuracies between 76% and 88% in a 10-tree configuration. These promising results pave the way for a comprehensive study towards implementing a real-time classifier on the UAV embedded system.

Many undergraduate engineering programs require students to take non-major courses to enhance their multidisciplinary competence, interdisciplinary competence, or both. Motivating non-major students is often challenging, especially when they feel that such courses do not really relate to their major, i.e., when they miss the interdisciplinary aspects. This situation is often caused by adopting a teaching approach that treats the course subjects in a self-contained manner, i.e., without sufficient links to the students' major. A case study is sometimes used in engineering education to bridge the gap between theoretical knowledge and application. However, this approach is typically used after presenting the theoretical material and, thus, cannot easily contribute to an early improvement of students' motivation. If used as an introduction to the course, in contrast, we claim that a case study can considerably highlight the value of the course and motivate students to learn. For this purpose, the case study's level of details should be kept appropriate and the case study must include clear links to the course content. We tested this approach at Khalifa University. A case study on the anti-lock brake system was presented to mechanical and aerospace engineering students taking a course on the fundamentals of electronic systems. Students reported a higher motivation to study the course after presenting the case study.

DLD-VISU is web-based tools for the visualization and animation of digital logic design. Combinatorial circuits can be built using logic gates, multiplexers, decoders, and look-up tables. Various configurations of finite state machines can be selected to define the machine type, the state code, and the flip-flop type. Logic minimization with the K-map approach and the Quine McCluskey scheme is also supported. The tools help students practice related topics in digital logic design courses. Also, instructors can use the tools to efficiently generate and verify examples for lecture notes or for homework problems and assignments. The tools support self-assessment and reflect the student learning process using learning curves. DLD-VISU that was developed in collaboration between Khalifa University and Technische Universität Darmstadt, has been used in teaching digital logic design since Fall 2013 with a positive impact on student's motivation and learning. The paper outlines the main features of DLD-VISU with focus on the pedagogical concepts in the design of these tools.

In this demo we will present DLD-VISU which is a Web-based tool for the visualization and animation of digital logic design [1]. Combinatorial circuits can be built using logic gates, multiplexers, decoders, and look-up tables. Various configurations of finite state machines can be selected to define the machine type, the state code, and the flip-flop type. Logic minimization with the K-map approach and the Quine McCluskey scheme is also supported. The tools help students practice related topics in digital logic design courses. Also, instructors can use the tools to efficiently generate and verify examples for lecture notes or for homework problems and assignments. The tools support self-assessment and reflect the student learning process using learning curves. DLD-VISU, that was developed in collaboration between Khalifa University and Technische Universität Darmstadt, has been used in teaching digital logic design since Fall 2013 with a positive impact on students' motivation and learning.

In this paper we analyze the security requirements of civil microdrones and propose a hardware architecture to meet these requirements. While hardware solutions are usually used to accelerate cryptographic operations and reduce their power consumption, we show that the latter aspect needs to be reviewed in the context of civil drones. Specifcally, adding cryptgraphic hardware to a flying device increases its weight and, thus, the power needed to fly this device. Depending on the relative weight of the added cryptographic processor, the computational power advantage of the hardware solution may be undone by the additional hadrware weight. This aspect is analyzed for the proposed hardware solution.

We put forward a notion of forward security for stateful public-key encryption against state exposure and chosen ciphertext attack. This new notion is important in mobile applications in which small devices that perform ‘stateful encryptions’ are vulnerable to attacks which can result in the compromise of internal states. We precisely formulate a security definition and propose two efficient schemes which are provably secure under standard computational assumptions.

We propose an efficient generic construction of forward-secure identity-based signature (FSIBS) that ensures unforgeability of past signatures in spite of the exposure of the current signing key. Our construction, supported by formal security analysis, brings about concrete FSIBS schemes which are more efficient than existing schemes in the literature. Especially, one of our instantiations of FSIBS based on discrete-log primitive turns out to be the most efficient among existing ones. As a secondary contribution, we refine the definition of security of FSIBS in such a way that users in the system can freely specify time periods over which their signing keys evolve.

This paper presents a hardware architecture for secure quadrotor communication. Both, the control data sent by the ground station and the information data sent by the quadrotor are encrypted and authenticated. The system is implemented on an FPGA and integrated on an extension board. The board is embedded into a self-constructed quadrotor based on the project Next-Generation Universal Aerial Video Platform.

Trusted computing is gaining an increasing acceptance in the industry and finding its way to cloud computing. With this penetration, the question arises whether the concept of hard-wired security modules will cope with the increasing sophistication and security requirements of future IT systems and the ever expanding threats and violations. So far, embedding cryptographic hardware engines into the Trusted Platform Module (TPM) has been regarded as a security feature. However, new developments in cryptanalysis, side-channel analysis, and the emergence of novel powerful computing systems, such as quantum computers, can render this approach useless. Given that, the question arises: Do we have to throw away all TPMs and loose the data protected by them, if someday a cryptographic engine on the TPM becomes insecure? To address this question, we present a novel architecture called Sustainable Trusted Platform Module (STPM), which guarantees a secure update of the TPM cryptographic engines without compromising the system's trustworthiness. The STPM architecture has been implemented as a proof-of-concept on top of a Xilinx Virtex-5 FPGA platform, demonstrating a test case with an update of the fundamental hash engine of the TPM.

Trusted computing is gaining an increasing acceptance in the industry and finding its way to cloud computing. With this penetration, the question arises whether the concept of hard-wired security modules will cope with the increasing sophistication and security requirements of future IT systems and the ever expanding threats and violations. So far, embedding cryptographic hardware engines into the Trusted Platform Module (TPM) has been regarded as a security feature. However, new developments in cryptanalysis, side-channel analysis, and the emergence of novel powerful computing systems, such as quantum computers, can render this approach useless. Given that, the question arises: Do we have to throw away all TPMs and loose the data protected by them, if someday a cryptographic engine on the TPM becomes insecure? To address this question, we present a novel architecture called Sustainable Trusted Platform Module (STPM), which guarantees a secure update of the TPM cryptographic engines without compromising the system's trustworthiness. The STPM architecture has been implemented as a proof-of-concept on top of a Xilinx Virtex-5 FPGA platform, demonstrating a test case with an update of the fundamental hash engine of the TPM.

This paper presents a compact course on VHDL-AMS which we offer since several years for students of electrical engineering and computer science. The paper is intended for instructors looking for a concise way to introduce VHDL-AMS on a pedagogical basis.

The Merkle Signature Scheme relies on hash function and is, therefore, assumed to be resistant to attacks by quantum computers. This paper presents an efficient hardware architecture to accelerate the generation of Merkle hash trees. Timing measurements on a prototype show a considerable performance boost compared to a similar software solution.

This paper presents the VISUAL-VHDL platform for visualizing digital circuit synthesis based on the hardware description language VHDL. VISUAL-VHDL enables students to enter VHDL code and control an animation process, which shows step-by-step how the different language constructs are treated to synthesize a complete digital circuit. It also enables the visualization of the Quine-McCluskey algorithm, which is embedded in our tool to optimize the circuit resulting from synthesizing the VHDL code. The drag and drop schematic editor for entering and parameterizing digital circuits can be used by educators and students to effectively produce circuit diagrams.

In this paper we propose a new approach to enhance security and privacy in C2X communication directly on the physical layer. Instead of using a single omni-directional antenna, our approach relies on an uniform linear antenna array, which enables beam forming. By this means, our approach allows adjusting the radiation and the reception patterns of the vehicle, which is essential for excluding undesired communication parties trying to eavesdrop exchanged messages or to distribute corrupted information. To specify the antenna array configuration appropriate to some important use cases, a simulation-based approach is deployed. A dedicated simulator is exploited, which produces field patterns and visualizes them according to a mobility model and on user settings. Based on the visual simulation results the antenna array has been specified and evaluated.

Beamforming is an approach for enhancing security and privacy in C2X communication. In this demo paper we describe a simulation tool for adaptive beamforming in the context of car-to-car and car-to-infrastructure communication. Besides verification, this simulator helps a system designer in specifying the antenna array in terms of element number, spacing, and phasing. The simulator determines the appropriate radiation pattern based on an embedded mobility model, a road map, and user settings.

Reconfigurable computing is an established field in computer science. Teaching this field to computer science students demands special attention due to limited student experience in electronics and digital system design. This article presents a compact course on reconfigurable processors, which was offered at the Technische Universität Darmstadt, and is intended for instructors aiming to introduce a new course in reconfigurable computing. Therefore, a detailed description of the course structure and content is provided. In contrast to courses on digital system design, which use FPGAs as a case platform, our course places this platform at the center of its focus and highlights its features as a basis for reconfigurable computing. Both declarative knowledge and functioning knowledge are considered in defining learning outcomes based on a novel What-Why-How Model. Lab activities were designated to deepen the functioning knowledge. The written exam is aligned to learning outcomes and shows that most students acquired the intended outcomes.

The McEliece public-key cryptosystem relies on the NP-hard decoding problem, and therefore, is regarded as a solution for postquantum cryptography. Though early known, this cryptosystem was not employed so far because of efficiency questions regarding performance and communication overhead. This paper presents a novel processor architecture as a high-performance platform to execute key generation, encryption, and decryption according to this cryptosystem. A prototype of this processor is realized on a reconfigurable device and tested via a dedicated software interface. A comparison with a similar software solution highlights the performance advantage of the proposed hardware solution.

McEliece scheme represents a code-based public-key cryptosystem. So far, this cryptosystem was not employed because of efficiency questions regarding performance and communication overhead.This paper presents a novel processor architecture as a high-performance platform to execute key generation, encryption and decryption according to this cryptosystem. A prototype of this processor is realized on Virtex-5 FPGA and tested via a software API. A comparison with a similar software solution highlights the performance advantage of the proposed hardware solution.

Group key management is a critical task in secure multicast applications such as Pay-TV over the Internet. The communication group key must be updated and distributed after every change in the group membership. Many solutions have been proposed in the last years to minimize the cost of this rekeying process on the server side. Most of these solutions are tree-based approaches such as the logical key hierarchy. These approaches suffer from three problems. First, tree-based solutions aim at minimizing rekeying costs only by reducing the number of needed cryptographic operations such as encryption or secure hashing. Second, these solutions do not treat the time-consuming digital signing needed to authenticate rekeying messages. Third, tree-based approaches manage huge amounts of keys by software which compromises security. In this paper, a novel hardware/software architecture is proposed, which optimizes the rekeying performance not only by minimizing the number of cryptographic operations, but also by reducing the execution times of these operations including digital signing with the aid of hardware acceleration. All help-keys are generated, managed, and stored on hardware, which enhances the system security. To keep flexibility, control-intensive tasks such as tree management are performed as software functions on the embedded processor. The presented rekeying processor is designed based on a comprehensive security analysis with the aid of a novel illustration for security threats, requirements, and technical solutions, a so-called security Y-diagram. A performance measurement on a prototype implementation shows that the rekeying processor can join and disjoin members much faster than software solutions besides supporting much larger groups.

Symmetric encryption, secure hashing, and random number generation are essential operations for many cryptographic applications. Realizations of these functions usually aim for high computational speed. For some applications, however, low resource usage is more important. Thus, this work presents a compact design for symmetric encryption, hash function, and Cryptographically Secure Random Number Generator. Its central element is a small AES-module, which is shared by all operations, leading to a lower resource usage than aggregated stand-alone solutions in previous work.

While a vast number ofsolutions to multicast group rekeying were published in the last years, a common base to evaluate these solutions and compare them with each other is missing. This paper presents a unified way to evaluate the performance of diferent re-keying algorithms running on the server side. A rekeying simulator estimates rekeying costs from a system point of view, which allows a reliable comparison between diferent rekeying algorithms. For this purpose, new system metrics related to rekeying performance are defined: the Rekeying Quality of Service (RQoS) and the Rekeying Access Control (RAC). By means of four simulation modes, these metrics are estimated by the simulator in relation to both the group size and its dynamics. A simulator prototype, implemented in Java, demonstrates the merit of this unified assessment method by means of a comprehensive case study.