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Early detection of the advanced persistent threat attack using performance analysis of deep learning

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arXiv 2020年

作者： Joloudari, Javad Hassannataj Haderbadi, Mojtaba Mashmool, Amir GhasemiGol, Mohammad Shahab, S. Mosavi, Amir Computer Engineering Department Faculty of Engineering University of Birjand Birjand Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Future Technology Research Center College of Future National Yunlin University of Science and Technology 123 University Ro ad Section 3 Douliou Yunlin64002 Taiwan Institute of Automation Obuda University Budapest1034 Hungary School of Economics and Business Norwegian University of Life Sciences Ås1430 Norway Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany Department of Informatics J. Selye University Komarno94501 Slovakia

One of the most common and important destructive attacks on the victim system is Advanced Persistent Threat (APT)-attack. The APT attacker can achieve his hostile goals by obtaining information and gaining financial benefits regarding the infrastructure of a network. One of the solutions to detect a secret APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the network may crash because of high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods such as C5.0 decision tree, Bayesian network and deep neural network are used for timely detection and classification of APT-attacks on the NSL-KDD dataset. Moreover, 10-fold cross validation method is used to experiment these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 95.64%, 88.37% and 98.85%, respectively, and also, in terms of the important criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 2.56, 10.47 and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false negative rate and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

Guest Editorial for Special Issue on Human-centered Intelligent Robots:Issues and Challenges

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 599-601页

作者： Zhijun Li C.L.Philip Chen Wei He IEEE College of Automation Science and Engineering South China University of Technology Department of Computer and Information Science Faculty of Science and Technology University of Macao School of Automation and Electrical Engineering University of Science and Technology Beijing

The special issue aims to address a broad spectrum of topics ranging from human-centered intelligent robots acting as a servant,secretary,or companion to intelligent robotic *** special issue publishes original papers of innovative ideas and concepts,new discoveries,and novel applications and business models relevant to the field of human-centered intelligent *** this special issue,modeling,intelligent control,

关键词： In Guest Editorial for Special Issue on Human-centered Intelligent Robots:Issues and Challenges

A novel approach for protection of accounts’ names against hackers combining cluster analysis and chaotic theory

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arXiv 2019年

作者： Andreev, Desislav Petrakieva, Simona Taralova, Ina Dep. Computer Science Faculty of Computer Systems and Technologies Technical University of Sofia Dep. Theory of Electrical Engineering Faculty of Automation Technical University of Sofia Laboratoire des Sciences du Numérique de Nantes LS2N UMR 6004 Ecole Centrale de Nantes

The last years of the 20th century and the beginning of the 21th mark the facilitation trend of our real life due to the big development and progress of the computers and other intelligent devices. Algorithms based on artificial intelligence are basically a part of the software. The transmitted information by Internet or LAN arises continuously and it is expected that the protection of the data has been ensured. The aim of the present paper is to reveal false names of users’ accounts as a result of hackers’ attacks. The probability a given account to be either false or actual is calculated using a novel approach combining machine learning analysis (especially clusters’ analysis) with chaos theory. The suspected account will be used as a pattern and by classification techniques clusters will be formed with a respective probability this name to be false. This investigation puts two main purposes: First, to determine if there exists a trend of appearance of the similar usernames, which arises during the creation of new accounts. Second, to detect the false usernames and to discriminate those from the real ones, independently of that if two types of accounts are generated with the same speed. These security systems are applied in different areas, where the security of the data in users’ accounts is strictly required. For example, they can be used in on-line voting for balloting, in studying the social opinion by inquiries, in protection of the information in different user accounts of given system etc. Copyright © 2019, The Authors. All rights reserved.

关键词： Cluster analysis

Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

Temperature Dependent Dielectric Spectroscopy of Muscle Tissue Phantom

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Temperature Dependent Dielectric Spectroscopy of Muscle Tiss...

European Microwave Conference in Central Europe (EuMCE)

作者： Ondrej Fiser Sebastian Ley Marko Helbig Jürgen Sachs Michaela Kantova Jan Vrba Department of Biomedical Technology CTU Prague Czech Republic Faculty of Computer Science and Automation TU Ilmenau Germany Department of Electrical Engineering and Information Technology TU Ilmenau Germany Department of Electromagnetic Field CTU Prague Czech Republic

ISBN: (纸本)9781728112404;9782874870675

The temperature dependence of the dielectric parameters of tissues and tissue mimicking phantoms is very important for the non-invasive temperature measurement in medical applications using microwaves. We investigated and proposed a procedure of the measurement of this dependence in the temperature range 25 - 50 °C. Our proposed procedure was verified using distilled water. The results were compared to the literature model in the frequency range 150 - 3000 MHz. Using this method, the temperature dependence of dielectric parameters of new tissue mimicking phantom based on agar, PE powder and TX-151 was measured in the temperature range 25 -50 °C.

关键词： Temperature measurement Temperature dependence Dielectrics Calibration Phantoms Permittivity measurement

Realizing learned quadruped locomotion behaviors through kinematic motion primitives

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arXiv 2018年

作者： Singla, Abhik Bhattacharya, Shounak Dholakiya, Dhaivat Bhatnagar, Shalabh Ghosal, Ashitava Amrutur, Bharadwaj Kolathaya, Shishir Faculty of Mechanical Engineering Faculty of Electrical & Computer Engineering Robert Bosch Center for Cyber Physical Systems IISc Bengaluru India Faculty of Computer Science and Automation

Humans and animals are believed to use a very minimal set of trajectories to perform a wide variety of tasks including walking. Our main objective in this paper is two fold 1) Obtain an effective tool to realize these basic motion patterns for quadrupedal walking, called the kinematic motion primitives (kMPs), via trajectories learned from deep reinforcement learning (D-RL) and 2) Realize a set of behaviors, namely trot, walk, gallop and bound from these kinematic motion primitives in our custom four legged robot, called the "Stoch". D-RL is a data driven approach, which has been shown to be very effective for realizing all kinds of robust locomotion behaviors, both in simulation and in experiment. On the other hand, kMPs are known to capture the underlying structure of walking and yield a set of derived behaviors. We first generate walking gaits from D-RL, which uses policy gradient based approaches. We then analyze the resulting walking by using principal component analysis. We observe that the kMPs extracted from PCA followed a similar pattern irrespective of the type of gaits generated. Leveraging on this underlying structure, we then realize walking in Stoch by a straightforward reconstruction of joint trajectories from kMPs. This type of methodology improves the transferability of these gaits to real hardware, lowers the computational overhead on-board, and also avoids multiple training iterations by generating a set of derived behaviors from a single learned gait. Copyright © 2018, The Authors. All rights reserved.

关键词： Kinematics

Hierarchic neighbors embedding

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arXiv 2019年

作者： Liu, Shenglan Yu, Yang Liu, Yang Qiao, Hong Feng, Lin Feng, Jiashi School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian Liaoning116024 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

Occupational health and safety risk management

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Occupational health and safety risk management

2018 Annual Session of Scientific Papers IMT ORADEA 2018

作者： Oarga, Ioan-Florin Rat Iu, Mariana Oarga, Ioan-Tudor Technical University of Cluj-Napoca Faculty of Machine Building Cluj-Napoca400641 Romania University of Oradea Department of Mechanical Engineering and Automotive Oradea410087 Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca400027 Romania

The ongoing industrial revolution also includes the health and safety domain, which assists this development process. The participants and beneficiaries of this revolution don't have to endure a lowering of the protection levels from the beginning of it. Consequently, the health and safety domain needs to play its role in this historical stage. The risks of injury and illness are managed from the beginning, when the technological processes are being developed, on each production stage, until the end of every technological process. In order to achieve this, the professional risks must be identified, quantified, ranked and evaluated such that, the elaborated measures will eliminate or decrease the risks from the source. This control process of occupational health and safety risks of injury or illness uses documents which are applicable to any organization. These documents are legally required in each state, being instruments of the organization management, working like a system along with all other documents. © The Authors, published by EDP sciences, 2018.

关键词： Risk management

Cross-ethnicity face anti-spoofing recognition challenge: A review

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arXiv 2020年

作者： Liu, Ajian Li, Xuan Wan, Jun Liang, Yanyan Escalera, Sergio Escalante, Hugo Jair Madadi, Meysam Jin, Yi Wu, Zhuoyuan Yu, Xiaogang Tan, Zichang Yuan, Qi Yang, Ruikun Zhou, Benjia Guo, Guodong Li, Stan Z. Faculty of Information Technology Avenida WaiLong Taipa Macau China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China Universitat de Barcelona and Computer Vision Center Barcelona Instituto Nacional de Astrofísica Óptica y Electrónica Puebla Mexico School of Software Beihang University Beijing China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Beijing Westlake University Hangzhou China

Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1;607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face *** organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset