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115 results on '"Martinez del Rincon, Jesus"'

4. LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection

Author

Doriguzzi-Corin, Roberto, Millar, Stuart, Scott-Hayward, Sandra, Martinez-del-Rincon, Jesus, and Siracusa, Domenico

Subjects
Computer Science - Cryptography and Security, Computer Science - Networking and Internet Architecture
Abstract

Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's Internet, disrupting the availability of essential services. The challenge of DDoS detection is the combination of attack approaches coupled with the volume of live traffic to be analysed. In this paper, we present a practical, lightweight deep learning DDoS detection system called LUCID, which exploits the properties of Convolutional Neural Networks (CNNs) to classify traffic flows as either malicious or benign. We make four main contributions; (1) an innovative application of a CNN to detect DDoS traffic with low processing overhead, (2) a dataset-agnostic preprocessing mechanism to produce traffic observations for online attack detection, (3) an activation analysis to explain LUCID's DDoS classification, and (4) an empirical validation of the solution on a resource-constrained hardware platform. Using the latest datasets, LUCID matches existing state-of-the-art detection accuracy whilst presenting a 40x reduction in processing time, as compared to the state-of-the-art. With our evaluation results, we prove that the proposed approach is suitable for effective DDoS detection in resource-constrained operational environments., Comment: Accepted for publication in the IEEE Transactions on Network and Service Management

Published
2020
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5. Least Privilege Learning for Attribute Obfuscation

Author

Brown, Glen, Martinez-del-Rincon, Jesus, Miller, Paul, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wallraven, Christian, editor, Liu, Qingshan, editor, and Nagahara, Hajime, editor

Published
2022
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10. Refining the Pose: Training and Use of Deep Recurrent Autoencoders for Improving Human Pose Estimation

Author

McLaughlin, Niall, Martinez del Rincon, Jesus, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, and Perales, Francisco José, editor

Published
2018
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11. Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis.

Author

Jia, Wenyang, Georgouli, Konstantia, Martinez-Del Rincon, Jesus, and Koidis, Anastasios

Subjects
FOOD chemistry, RAPID tooling, INDUSTRIAL research, FOOD supply, SUPPLY chains
Abstract

Routine, remote, and process analysis for foodstuffs is gaining attention and can provide more confidence for the food supply chain. A new generation of rapid methods is emerging both in the literature and in industry based on spectroscopy coupled with AI-driven modelling methods. Current published studies using these advanced methods are plagued by weaknesses, including sample size, abuse of advanced modelling techniques, and the process of validation for both the acquisition method and modelling. This paper aims to give a comprehensive overview of the analytical challenges faced in research and industrial settings where screening analysis is performed while providing practical solutions in the form of guidelines for a range of scenarios. After extended literature analysis, we conclude that there is no easy way to enhance the accuracy of the methods by using state-of-the-art modelling methods and the key remains that capturing good quality raw data from authentic samples in sufficient volume is very important along with robust validation. A comprehensive methodology involving suitable analytical techniques and interpretive modelling methods needs to be considered under a tailored experimental design whenever conducting rapid food analysis. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Kinect and Episodic Reasoning for Human Action Recognition

Author

Cantarero, Ruben, Santofimia, Maria J., Villa, David, Requena, Roberto, Campos, Maria, Florez-Revuelta, Francisco, Nebel, Jean-Christophe, Martinez-del-Rincon, Jesus, Lopez, Juan C., Kacprzyk, Janusz, Series editor, Omatu, Sigeru, editor, Semalat, Ali, editor, Bocewicz, Grzegorz, editor, Sitek, Paweł, editor, Nielsen, Izabela E., editor, García García, Julián A., editor, and Bajo, Javier, editor

Published
2016
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23. Common-Sense Knowledge for a Computer Vision System for Human Action Recognition

Author

Santofimia, Maria J., Martinez-del-Rincon, Jesus, Nebel, Jean-Christophe, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bravo, José, editor, Hervás, Ramón, editor, and Rodríguez, Marcela, editor

Published
2012
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24. Gaussian Mixture Background Modelling Optimisation for Micro-controllers

Author

Salvadori, Claudio, Makris, Dimitrios, Petracca, Matteo, Martinez-del-Rincon, Jesus, Velastin, Sergio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bebis, George, editor, Boyle, Richard, editor, Parvin, Bahram, editor, Koracin, Darko, editor, Fowlkes, Charless, editor, Wang, Sen, editor, Choi, Min-Hyung, editor, Mantler, Stephan, editor, Schulze, Jürgen, editor, Acevedo, Daniel, editor, Mueller, Klaus, editor, and Papka, Michael, editor

Published
2012
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25. AdaBoost Multiple Feature Selection and Combination for Face Recognition

Author

Martínez-Contreras, Francisco, Orrite-Uruñuela, Carlos, Martínez-del-Rincón, Jesús, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Araujo, Helder, editor, Mendonça, Ana Maria, editor, Pinho, Armando J., editor, and Torres, María Inés, editor

Published
2009
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26. Rao-Blackwellized Particle Filter for Human Appearance and Position Tracking

Author

Martínez-del-Rincón, Jesús, Orrite-Uruñuela, Carlos, Rogez, Grégory, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Martí, Joan, editor, Benedí, José Miguel, editor, Mendonça, Ana Maria, editor, and Serrat, Joan, editor

Published
2007
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27. Dealing with Non-linearity in Shape Modelling of Articulated Objects

Author

Rogez, Grégory, Martínez-del-Rincón, Jesús, Orrite, Carlos, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Rangan, C. Pandu, editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Martí, Joan, editor, Benedí, José Miguel, editor, Mendonça, Ana Maria, editor, and Serrat, Joan, editor

Published
2007
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28. Exploiting Spatio-temporal Constraints for Robust 2D Pose Tracking

Author

Rogez, Grégory, Rius, Ignasi, Martínez-del-Rincón, Jesús, Orrite, Carlos, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Elgammal, Ahmed, editor, Rosenhahn, Bodo, editor, and Klette, Reinhard, editor

Published
2007
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30. Human Figure Segmentation Using Independent Component Analysis

Author

Rogez, Grégory, Orrite-Uruñuela, Carlos, Martínez-del-Rincón, Jesús, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Marques, Jorge S., editor, Pérez de la Blanca, Nicolás, editor, and Pina, Pedro, editor

Published
2005
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33. Kinect and Episodic Reasoning for Human Action Recognition

Author

Cantarero, Ruben, primary, Santofimia, Maria J., additional, Villa, David, additional, Requena, Roberto, additional, Campos, Maria, additional, Florez-Revuelta, Francisco, additional, Nebel, Jean-Christophe, additional, Martinez-del-Rincon, Jesus, additional, and Lopez, Juan C., additional

Published
2016
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37. 3-D Human Pose Estimation Using Iterative Conditional Squeeze and Excitation Networks.

Author

McLaughlin, Niall, Martinez-del-Rincon, Jesus, and Miller, Paul

Abstract

We propose a new method for single-camera real-world 3-D human pose estimation. Our method uses multitask training together with iterative pose refinement using a novel conditional attention mechanism. For iterative pose refinement, the output of each convolutional layer is conditioned on the latest pose estimate, using a conditioned squeeze-and-excitation network architecture that incorporates novel feedback connections. Multitask training on both an in-the-wild 2-D pose dataset and a controlled 3-D pose dataset allows for real-world 3-D pose estimation without the need for a large-scale in-the-wild 3-D pose dataset, which is unavailable. Experiments are performed on several real-world datasets, as well as the Human 3.6 Million and HumanEva-I datasets, to show that the combined attention mechanism, iterative refinement scheme, and multitask training allow us to achieve robust and competitive performance with only a simple network architecture. In addition, we show that our method is efficient enough to run on commodity hardware, producing pose estimates in real time. [ABSTRACT FROM AUTHOR]

Published
2022
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39. DANdroid

Author

Millar, Stuart, primary, McLaughlin, Niall, additional, Martinez del Rincon, Jesus, additional, Miller, Paul, additional, and Zhao, Ziming, additional

Published
2020
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41. Multi-modal object detection using unsupervised transfer learning and adaptation techniques

Author

Abbott, Rachael, Robertson, Neil, Martinez del Rincon, Jesus, and Connor, Barry

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

Deep neural networks achieve state-of-the-art performance on object detection tasks with RGB data. However, there are many advantages of detection using multi-modal imagery for defence and security operations. For example, the infrared modality offers persistent surveillance and is essential in poor lighting conditions. It is, therefore, crucial to creating an object detection system which can use IR imagery. Collecting and labelling large volumes of thermal imagery is incredibly expensive and time-consuming. Consequently, we propose to mobilise the abundance of already labelled RGB data currently available to achieve detection in the IR modality. In this paper, we present a method for multi-modal object detection using unsupervised transfer learning and adaptation techniques. We train Faster-RCNN on RGB imagery and test with a thermal imager. The images contain object classes; people and land vehicles and represent real-life urban scenes which include clutter and occlusions. We improve the baseline F1-score by up to 20% through training with an additional loss function which reduces the difference between RGB and IR feature maps. This work shows unsupervised modality adaptation is possible, and we have the opportunity to maximise the use of labelled RGB imagery for detection in multiple modalities. The novelty of this work includes; the use of the IR modality, modality adaption from RGB to IR for object detection and the ability to use real-life imagery in uncontrolled environments. The practical impact of this work to the defence and security community is an increase in performance and the saving of time and money in data collection and annotations.

Published
2019

42. Cascading Sum-Product Networks using Robustness

Author

Conaty, Diarmaid, Martinez del Rincon, Jesus, de Campos, Cassio, Sub Decision Support Systems, and Decision Support Systems

Subjects
sensitivity analysis, classification, robustness, Sum-product networks
Abstract

Sum-product networks are an increasingly popular family of probabilistic graphical models for which marginal inference can be performed in polynomial time. They have been shown to achieve state-of-the-art performance in several tasks. When learning sum-product networks from scarce data, the obtained model may be prone to robustness issues. In particular, small variations of parameters could lead to different conclusions. We discuss the characteristics of sum-product networks as classifiers and study the robustness of them with respect to their parameters. Using a robustness measure to identify (possibly) unreliable decisions, we build a hierarchical approach where the classification task is deferred to another model if the outcome is deemed unreliable. We apply this approach on benchmark classification tasks and experiments show that the robustness measure can be a meaningful manner to improve classification accuracy.

Published
2018

49. Deep Android Malware Detection

Author

McLaughlin, Niall, primary, Martinez del Rincon, Jesus, additional, Kang, BooJoong, additional, Yerima, Suleiman, additional, Miller, Paul, additional, Sezer, Sakir, additional, Safaei, Yeganeh, additional, Trickel, Erik, additional, Zhao, Ziming, additional, Doupé, Adam, additional, and Joon Ahn, Gail, additional

Published
2017
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