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Laser-induced breakdown spectroscopy in space applications: Review and prospects
(Elsevier, 2024-12-01) Saeidfirouzeh, Homa; Kubelík, Petr; Laitl, Vojtěch; Křivková, Anna; Vrábel, Jakub; Rammelkamp, Kristin; Schröder, Susanne; Gornushkin, Igor; Képeš, Erik; Žabka, Ján; Ferus, Martin; Pořízka, Pavel; Kaiser, Jozef
This review describes the principles and summarizes the challenges of analytical methods based on optical emission spectroscopy (OES) in space applications, with a particular focus on Laser-Induced Breakdown Spectroscopy (LIBS). Over the past decade, LIBS has emerged as a powerful analytical technique for space exploration and In-Situ Resource Utilization (ISRU) of celestial bodies. Its implementation has been suggested for various segments of the Space Resources Value Chain, including prospecting, mining, and beneficiation. Current missions to Mars, including the ChemCam instrument on the Curiosity rover, the SuperCam on the Perseverance rover, and the MarSCoDe on the Zhurong rover, are considered flagship applications of LIBS. Despite neither the Pragyan rover nor the Vikram lander waking from the lunar night, the success of the Chandrayaan-3 mission marks another milestone in the development of LIBS instruments, with further missions, including commercial ones, anticipated. This paper reviews the deployment of LIBS payloads on Mars rovers, upcoming missions prospecting the Moon and asteroids, and LIBS analysis of meteorites. Additionally, it highlights the importance of data processing specific to space applications, emphasizing recent trends in transfer learning. Furthermore, LIBS combined with other spectroscopic techniques (e.g., Raman Spectroscopy, Mass Spectrometry, and Fourier-Transform Infrared Spectroscopy) represents an intriguing platform with comprehensive analytical capabilities. The review concludes by emphasizing the significance of LIBS-based contributions in advancing our understanding of celestial bodies and paving the way for future space exploration endeavors.
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Lattice-based Multisignature Optimization for RAM Constrained Devices
(Association for Computing Machinery, 2024-07-30) Ricci, Sara; Shapoval, Vladyslav; Dzurenda, Petr; Roenne, Peter; Oupicky, Jan; Malina, Lukáš
In the era of growing threats posed by the development of quantum computers, ensuring the security of electronic services has become fundamental. The ongoing standardization process led by the National Institute of Standards and Technology (NIST) emphasizes the necessity for quantum-resistant security measures. However, the implementation of Post-Quantum Cryptographic (PQC) schemes, including advanced schemes such as threshold signatures, faces challenges due to their large key sizes and high computational complexity, particularly on constrained devices. This paper introduces two microcontroller-tailored optimization approaches, focusing on enhancing the DS2 threshold signature scheme. These optimizations aim to reduce memory consumption while maintaining security strength, specifically enabling the implementation of DS2 on microcontrollers with only 192 KB of RAM. Experimental results and security analysis demonstrate the efficacy and practicality of our solution, facilitating the deployment of DS2 threshold signatures on resource-constrained microcontrollers.
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Quantum-Resistant and Secure MQTT Communication
(Association for Computing Machinery, 2024-07-30) Malina, Lukáš; Dobiáš, Patrik; Dzurenda, Petr; Srivastava, Gautam
In this paper, we deal with the deployment of Post-Quantum Cryptography (PQC) in Internet of Things (IoT). Concretely, we focus on the MQTT (Message Queuing Telemetry Transport) protocol that is widely used in IoT services. The paper presents our novel quantum-resistant security proposal for the MQTT protocol that supports secure broadcast. Our solution omits using TLS with the handshake causing delay and is suitable for sending irregular short messages. Finally, we show how our solution can practically affect concrete use cases by the performance results of the proposed solution.
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Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks
(Association for Computing Machinery, 2024-07-30) Phan, Viet Anh; Jeřábek, Jan; Malina, Lukáš
The Internet of Things (IoT) has become increasingly practical in applications such as smart homes, autonomous vehicles, and environmental monitoring. However, this rapid expansion has led to significant cybersecurity threats. Detecting these threats is critical, and while machine learning techniques are valuable, they struggle with high-dimensional data. Feature selection helps by reducing computational costs while maintaining model generalization. Selecting the most effective feature selection method is a crucial task. This research addresses this gap by testing five feature selection methods: Random Forest (RF), Recursive Feature Elimination (RFE), Logistic Regression (LR), XGBoost Regression (XGBoost), and Information Gain (IG) using the CIC-IoT 2023 dataset. It evaluates these methods when being used with five machine learning models: Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (k-NN), Gradient Boosting (GB), and Multi-layer Perceptron (MLP) using metrics like accuracy, precision, recall, and F1-score across three datasets. The results show that RFE, especially with the RF model, achieves the highest accuracy (99.57%) with 30 features. RF is the most stable, with accuracy from 83% to 99.56%. Additionally, the 5-feature scheme is best for implementing IDS on resource-limited IoT devices, with RFE paired with the k-NN model being the optimal combination.
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Enhancing Cybersecurity Curriculum Development: AI-Driven Mapping and Optimization Techniques
(Association for Computing Machinery, 2024-07-30) Dzurenda, Petr; Ricci, Sara; Sikora, Marek; Stejskal, Michal; Lendak, Imre; Adao, Pedro
Cybersecurity has become important, especially during the last decade. The significant growth of information technologies, internet of things, and digitalization in general, increased the interest in cybersecurity professionals significantly. While the demand for cybersecurity professionals is high, there is a significant shortage of these professionals due to the very diverse landscape of knowledge and the complex curriculum accreditation process. In this article, we introduce a novel AI-driven mapping and optimization solution enabling cybersecurity curriculum development. Our solution leverages machine learning and integer linear programming optimization, offering an automated, intuitive, and user-friendly approach. It is designed to align with the European Cybersecurity Skills Framework (ECSF) released by the European Union Agency for Cybersecurity (ENISA) in 2022. Notably, our innovative mapping methodology enables the seamless adaptation of ECSF to existing curricula and addresses evolving industry needs and trend. We conduct a case study using the university curriculum from Brno University of Technology in the Czech Republic to showcase the efficacy of our approach. The results demonstrate the extent of curriculum coverage according to ECSF profiles and the optimization progress achieved through our methodology.