Arquitectura de un sistema C4ISR para pequeñas unidades

La presente tesis doctoral aborda el problema de los sistemas de mando y control, y en concreto los sistemas C4ISR. Los sistemas C4ISr (Command Control, Computers and Communications Information Surveillance and Reconaissance) engloban un amplio número de arquitecturas y sistemas informáticos y de comunicaciones. Su principal finalidad, tanto en aplicaciones civiles como militares, es la de obtener información sobre el estado del teatro de operaciones para entregársela, convenientemente formateada, a las personas al mando de una operación de forma que se construyan una adecuada visión del mismo que les permita tomar las decisiones correctas. Por otra parte, deben servir de plataforma de comunicaciones para transmitir dichas órdenes y cualquier otra información que se estime oportuna. La presente tesis doctoral se centra en identificar las necesidades existentes en mando y control a nivel táctico, tanto en la vertiente civil como en la militar, y plantear una arquitectura global para sistemas C4ISR que permita diseñar, desarrollar e implementar una solución de sistema de mando y control de pequeñas unidades (nivel de batallón e inferiores) para mejorar la conciencia situacional, tanto individual como como compartida, de los comandantes en esos niveles. Se ha promovido el planteamiento de arquitecturas y el desarrollo de sistemas que implementen los novedosos conceptos de mando y control, detectados en la literatura científica reciente, para la consecución de la efectividad en el cumplimiento de una misión, siguiendo la filosofía COTS (Commercial off-the self), enfatizando el uso de estándares en todos sus componentes y una aproximación OSS (open source software) en el desarrollo de componentes software, e integrando fluljos multimedia como una de las principales aportaciones. Para ello se ha realizado un exhaustivo y profundo análisis del estado del arte acerca de los sistemas de mando y control, desde sus comienzos hasta las últimas propuestas. Esto nos ha conducidoPérez Llopis, I. (2009). Arquitectura de un sistema C4ISR para pequeñas unidades [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/6067Palanci

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

[EN] This paper shows a Novel Low Processing Time System focused on criminal activities detection based on real-time video analysis applied to Command and Control Citizen Security Centers. This system was applied to the detection and classification of criminal events in a real-time video surveillance subsystem in the Command and Control Citizen Security Center of the Colombian National Police. It was developed using a novel application of Deep Learning, specifically a Faster Region-Based Convolutional Network (R-CNN) for the detection of criminal activities treated as "objects" to be detected in real-time video. In order to maximize the system efficiency and reduce the processing time of each video frame, the pretrained CNN (Convolutional Neural Network) model AlexNet was used and the fine training was carried out with a dataset built for this project, formed by objects commonly used in criminal activities such as short firearms and bladed weapons. In addition, the system was trained for street theft detection. The system can generate alarms when detecting street theft, short firearms and bladed weapons, improving situational awareness and facilitating strategic decision making in the Command and Control Citizen Security Center of the Colombian National Police.This work was co-funded by the European Commission as part of H2020 call SEC-12-FCT-2016-Subtopic3 under the project VICTORIA (No. 740754). This publication reflects the views only of the authors and the Commission cannot be held responsible for any use which may be made of the information contained therein.Suarez-Paez, J.; Salcedo-Gonzalez, M.; Climente, A.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE.; Pérez Llopis, I. (2019). A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers. Information. 10(12):1-19. https://doi.org/10.3390/info10120365S1191012Wang, L., Rodriguez, R. M., & Wang, Y.-M. (2018). A dynamic multi-attribute group emergency decision making method considering expertsr hesitation. International Journal of Computational Intelligence Systems, 11(1), 163. doi:10.2991/ijcis.11.1.13Esteve, M., Perez-Llopis, I., & Palau, C. E. (2013). Friendly Force Tracking COTS solution. IEEE Aerospace and Electronic Systems Magazine, 28(1), 14-21. doi:10.1109/maes.2013.6470440Senst, T., Eiselein, V., Kuhn, A., & Sikora, T. (2017). Crowd Violence Detection Using Global Motion-Compensated Lagrangian Features and Scale-Sensitive Video-Level Representation. IEEE Transactions on Information Forensics and Security, 12(12), 2945-2956. doi:10.1109/tifs.2017.2725820Shi, Y., Tian, Y., Wang, Y., & Huang, T. (2017). Sequential Deep Trajectory Descriptor for Action Recognition With Three-Stream CNN. IEEE Transactions on Multimedia, 19(7), 1510-1520. doi:10.1109/tmm.2017.2666540Arunnehru, J., Chamundeeswari, G., & Bharathi, S. P. (2018). Human Action Recognition using 3D Convolutional Neural Networks with 3D Motion Cuboids in Surveillance Videos. Procedia Computer Science, 133, 471-477. doi:10.1016/j.procs.2018.07.059Kamel, A., Sheng, B., Yang, P., Li, P., Shen, R., & Feng, D. D. (2019). Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(9), 1806-1819. doi:10.1109/tsmc.2018.2850149Zhang, B., Wang, L., Wang, Z., Qiao, Y., & Wang, H. (2018). Real-Time Action Recognition With Deeply Transferred Motion Vector CNNs. IEEE Transactions on Image Processing, 27(5), 2326-2339. doi:10.1109/tip.2018.2791180Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016). Region-Based Convolutional Networks for Accurate Object Detection and Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1), 142-158. doi:10.1109/tpami.2015.2437384Suarez-Paez, J., Salcedo-Gonzalez, M., Esteve, M., Gómez, J. A., Palau, C., & Pérez-Llopis, I. (2018). Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia. International Journal of Computational Intelligence Systems, 12(1), 123. doi:10.2991/ijcis.2018.25905186Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149. doi:10.1109/tpami.2016.2577031Hao, S., Wang, P., & Hu, Y. (2019). Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction. Information, 10(2), 81. doi:10.3390/info10020081Peng, M., Wang, C., Chen, T., & Liu, G. (2016). NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification. Information, 7(4), 61. doi:10.3390/info7040061NVIDIA CUDA® Deep Neural Network library (cuDNN)https://developer.nvidia.com/cuda-downloadsWu, X., Lu, X., & Leung, H. (2018). A Video Based Fire Smoke Detection Using Robust AdaBoost. Sensors, 18(11), 3780. doi:10.3390/s18113780Park, J. H., Lee, S., Yun, S., Kim, H., & Kim, W.-T. (2019). Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework. Sensors, 19(9), 2025. doi:10.3390/s19092025García-Retuerta, D., Bartolomé, Á., Chamoso, P., & Corchado, J. M. (2019). Counter-Terrorism Video Analysis Using Hash-Based Algorithms. Algorithms, 12(5), 110. doi:10.3390/a12050110Zhao, B., Zhao, B., Tang, L., Han, Y., & Wang, W. (2018). Deep Spatial-Temporal Joint Feature Representation for Video Object Detection. Sensors, 18(3), 774. doi:10.3390/s18030774He, Z., & He, H. (2018). Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks. Symmetry, 10(9), 375. doi:10.3390/sym10090375Muhammad, K., Hamza, R., Ahmad, J., Lloret, J., Wang, H., & Baik, S. W. (2018). Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption. IEEE Transactions on Industrial Informatics, 14(8), 3679-3689. doi:10.1109/tii.2018.2791944Barthélemy, J., Verstaevel, N., Forehead, H., & Perez, P. (2019). Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City. Sensors, 19(9), 2048. doi:10.3390/s19092048Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., & Altowaijri, S. M. (2019). Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs. Sensors, 19(9), 2206. doi:10.3390/s19092206Xu, S., Zou, S., Han, Y., & Qu, Y. (2018). Study on the Availability of 4T-APS as a Video Monitor and Radiation Detector in Nuclear Accidents. Sustainability, 10(7), 2172. doi:10.3390/su10072172Plageras, A. P., Psannis, K. E., Stergiou, C., Wang, H., & Gupta, B. B. (2018). Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings. Future Generation Computer Systems, 82, 349-357. doi:10.1016/j.future.2017.09.082Jha, S., Dey, A., Kumar, R., & Kumar-Solanki, V. (2019). A Novel Approach on Visual Question Answering by Parameter Prediction using Faster Region Based Convolutional Neural Network. International Journal of Interactive Multimedia and Artificial Intelligence, 5(5), 30. doi:10.9781/ijimai.2018.08.004Cho, S., Baek, N., Kim, M., Koo, J., Kim, J., & Park, K. (2018). Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network. Sensors, 18(9), 2995. doi:10.3390/s18092995Zhang, J., Xing, W., Xing, M., & Sun, G. (2018). Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network. Sensors, 18(7), 2327. doi:10.3390/s18072327Bakheet, S., & Al-Hamadi, A. (2016). 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HYBINT: A Hybrid Intelligence System for Critical Infrastructures Protection

[EN] Cyberattacks, which consist of exploiting security vulnerabilities of computer networks and systems for any kind of malicious purpose (e.g., extortion, data steal, assets hijacking), have been continuously increasing worldwide in recent years. Cyberspace appears today as a new battlefield, along with physical world scenarios (land, sea, air, and space), for the organizations defence and security. Besides, by the fact that attacks from the physical world may have significant implications in the cyber world and vice versa, these dimensions cannot be understood independently. However, the most common intelligence systems offer an insufficient situational awareness exclusively focused on one of these decision spaces. This article introduces HYBINT, an enhanced intelligence system that provides the necessary decision-making support for an efficient critical infrastructures protection by combining the real-time situation of the physical and cyber domains in a single visualization space. HYBINT is a real cross-platform solution which supplies, through Big Data analytical methods and advanced representation techniques, hybrid intelligence information from significant data of both physical and cyber data sources in order to bring an adequate hybrid situational awareness (HSA) of the cyber-physical environment. The proposal will be validated in a detailed scenario in which HYBINT system will be evaluated.Hingant Gómez, J.; Zambrano-Vizuete, OM.; Pérez-Carrasco, FJ.; Pérez Llopis, I.; Esteve Domingo, M. (2018). HYBINT: A Hybrid Intelligence System for Critical Infrastructures Protection. Security and Communication Networks. 2018:1-13. https://doi.org/10.1155/2018/5625860S113201

Sistema Distribuido de Detección de Sismos Usando una Red de Sensores Inalámbrica para Alerta Temprana

El detectar eventos disruptivos usando sensores COTS como los utilizados en smartphones representa un gran desafío pero también una oportunidad interesante. En este artículo se presenta una arquitectura de sistema de tiempo real crítico, jerárquica y distribuida, que hace uso de smartphones que actúan como sensores a través de una aplicación de bajo consumo de energía que convierte a sus acelerómetros en acelerógrafos. Los smartphones desplegados forman una red de sensores que detecta, analiza y notifica un pico sísmico. El sistema optimiza cálculos distribuidos y capacidades de comunicación en smartphones para proveer tiempo extra para alertas tempranas en escenarios de desastre de tipo sísmico, aunque puede ser empleada como solución a otros desastres naturales. Se propone una solución innovadora de bajo coste que realiza análisis tanto espaciales como temporales, no presentes en otros trabajos, lo cual lo hace más preciso y personalizable permitiendo adaptarse a las características geográficas de la zona, de red, y recursos tanto humanos como monetarios. La arquitectura ha sido validada mediante una extensa evaluación, consiguiendo como resultado notificaciones tempranas que adelantan en decenas de segundos el pico máximo del sismo en la zona del epicentro y aún más para zonas más alejadas; y la considerable reducción de falsas alarmas. Adicionalmente la arquitectura propuesta incluye una administración post-evento que mejora la capacidad operativa, logística y de telecomunicaciones desde un solo nivel central, y al mismo tiempo, mantiene al usuario informado de centros de refugios cercanos, mejores rutas, rutas seguras para una mejor decisión.Zambrano Vizuete, AM.; Pérez Llopis, I.; Palau Salvador, CE.; Esteve Domingo, M. (2015). Sistema Distribuido de Detección de Sismos Usando una Red de Sensores Inalámbrica para Alerta Temprana. Revista Iberoamericana de Automática e Informática Industrial RIAI. 12(3):260-269. doi:10.1016/j.riai.2015.06.00226026912

Command and Control Information Systems Applied to Large Forest Fires Response

[EN] Forest fires are one of the natural disasters that frequently occur around the world, causing irreparable human, material and environmental losses. To confront a wild forest fire, it is essential to have an accurate description of the operations environment, which allows to take decisions in line with reality and collaboration among the different agencies involved in response operations. This paper describes an architecture for implementing of a Command and Control Information System that enables to obtain an accurate situational awareness of the operations theater, communications with the deployed units inside and outside the disaster environment, and coordination and effective transmission of decisions making when responding to the presence of a wild forest fire. It focus its proposal on the establishment of a tactical network that allows adequate monitoring of the environment and facilitate mobility and deployment of response units on the hot spot.Zambrano-Vizuete, OM.; Pérez Llopis, I.; Carvajal Rodrigo, FJ.; Esteve Domingo, M.; Palau Salvador, CE. (2017). Command and Control Information Systems Applied to Large Forest Fires Response. IEEE Latin America Transactions. 15(9):1735-1741. doi:10.1109/TLA.2017.8015080S1735174115

Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia

[EN] This paper shows the implementation of a prototype of street theft detector using the deep learning technique R- CNN (Region-Based Convolutional Network), applied in the Command and Control Information System (C2IS) of National Police of Colombia, the prototype is implemented using three models of CNN (Convolutional Neural Network), AlexNet, VGG16 and VGG19 comparing their computational cost measuring the image processing time, according to the complexity (depth) of each model. Finally, we conclude which model has the lowest computational cost and is more useful for the case of the National Police of Colombia.Suarez-Paez, JE.; Salcedo-González, ML.; Esteve Domingo, M.; Gomez, J.; Palau Salvador, CE.; Pérez Llopis, I. (2018). Reduced computational cost prototype for street theft detection based on depth decrement in Convolutional Neural Network. Application to Command and Control Information Systems (C2IS) in the National Police of Colombia. International Journal of Computational Intelligence Systems. 12(1):123-130. https://doi.org/10.2991/ijcis.2018.25905186S12313012

Threat Hunting System for Protecting Critical Infrastructures Using a Machine Learning Approach

Cyberattacks are increasing in number and diversity in nature daily, and the tendency for them is to escalate dramatically in the forseeable future, with critical infrastructures (CI) assets and networks not being an exception to this trend. As time goes by, cyberattacks are more complex than before and unknown until they spawn, being very difficult to detect and remediate. To be reactive against those cyberattacks, usually defined as zero-day attacks, cyber-security specialists known as threat hunters must be in organizations’ security departments. All the data generated by the organization’s users must be processed by those threat hunters (which are mainly benign and repetitive and follow predictable patterns) in short periods to detect unusual behaviors. The application of artificial intelligence, specifically machine learning (ML) techniques (for instance NLP, C-RNN-GAN, or GNN), can remarkably impact the real-time analysis of those data and help to discriminate between harmless data and malicious data, but not every technique is helpful in every circumstance; as a consequence, those specialists must know which techniques fit the best at every specific moment. The main goal of the present work is to design a distributed and scalable system for threat hunting based on ML, and with a special focus on critical infrastructure needs and characteristics

Threat Hunting Architecture Using a Machine Learning Approach for Critical Infrastructures Protection

The number and the diversity in nature of daily cyber-attacks have increased in the last few years, and trends show that both will grow exponentially in the near future. Critical Infrastructures (CI) operators are not excluded from these issues; therefore, CIs’ Security Departments must have their own group of IT specialists to prevent and respond to cyber-attacks. To introduce more challenges in the existing cyber security landscape, many attacks are unknown until they spawn, even a long time after their initial actions, posing increasing difficulties on their detection and remediation. To be reactive against those cyber-attacks, usually defined as zero-day attacks, organizations must have Threat Hunters at their security departments that must be aware of unusual behaviors and Modus Operandi. Threat Hunters must face vast amounts of data (mainly benign and repetitive, and following predictable patterns) in short periods to detect any anomaly, with the associated cognitive overwhelming. The application of Artificial Intelligence, specifically Machine Learning (ML) techniques, can remarkably impact the real-time analysis of those data. Not only that, but providing the specialists with useful visualizations can significantly increase the Threat Hunters’ understanding of the issues that they are facing. Both of these can help to discriminate between harmless data and malicious data, alleviating analysts from the above-mentioned overload and providing means to enhance their Cyber Situational Awareness (CSA). This work aims to design a system architecture that helps Threat Hunters, using a Machine Learning approach and applying state-of-the-art visualization techniques in order to protect Critical Infrastructures based on a distributed, scalable and online configurable framework of interconnected modular components

Friendly Force Tracking COTS solution

[EN] The military community is redefining the way wars will be fought in the future, evolving toward a network-centric warfare (NCW) paradigm. In this paradigm, force is improved through the extensive use of the communications network and information sharing and fusion, with the main goal to achieve information superiority on the battlefield and gain agility and assure mission effectiveness. The key component to fuse all the information and distribute it to the appropriate destination is an adequate command and control (C2) tool, supported by a tactical communications networkWe are very grateful to the Spanish Army, especially the Signal Brigade and the 8th Cavalry Regiment for their support and collaboration during the development of the system and the execution of the trials.Esteve Domingo, M.; Pérez Llopis, I.; Palau Salvador, CE. (2013). Friendly Force Tracking COTS solution. IEEE Aerospace and Electronic Systems Magazine. 28(1):14-21. https://doi.org/10.1109/MAES.2013.6470440S142128

Distributed Sensor System for Earthquake Early Warning Based on the Massive Use of Low Cost Accelerometers

This paper presents a different and innovative proposal to detect seismic events, a solution that uses smartphones as opportunistic sensor nodes to obtain real-time knowledge of the community environment through a hierarchical architecture, taking advantage of this growing trend. A distributed low-cost network formed of smartphones capable of detect a seismic-peak with a high accuracy by means of converting accelerometers in accelerographs optimizing distributed calculations in these. A server which considers time and spatial analyses not present in another works, making it more precise and customizable, coupling it to the features of the geographical zone, network and resources. Validated by extensive evaluation, the most relevant results have been the improvement in notifications delivery about a seismic-peak 12 seconds earlier in the epicenter zone, the reduced consumption of mobile battery and the reduction in the number of false positives. In addition, this challenge becomes an great opportunity giving people as much as tens of seconds warning before an earthquake occurs in places far from the epicenter.Zambrano Vizuete, AM.; Pérez Llopis, I.; Palau Salvador, CE.; Esteve Domingo, M. (2015). Distributed Sensor System for Earthquake Early Warning Based on the Massive Use of Low Cost Accelerometers. IEEE Latin America Transactions. 13(1):291-298. doi:10.1109/TLA.2015.7040661S29129813