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Deep Security Detection Framework Based on ATT&CK

International Journal of Network Security 2023年第1期25卷 161-170页

作者： Qiu, Rixuan Fu, Yu Le, Jian Zheng, Fuyong Qi, Gan Peng, Chao Zhang, Yi Li, Yuancheng Liu, Yan Dispatch Monitoring Center State Grid Jiangxi Information & Telecommunication Company Nanchang China School of Electrical Engineering and Automation Wuhan University Wuhan430072 China School of Control and Computer Engineering & North China Electric Power University Beijing102206 China

It is urgent to establish an efficient and comprehensive security system architecture facing the increasingly severe network threat. Aiming at the security problems in information systems, a deep security detection framework based on ATT&CK is proposed. There are three modules: information collection, analysis engine, and security response. The anomaly detection results of the technical layer based on ATT&CK knowledge base association analysis are mapped to the attack links of the tactical layer, thus realizing the bottom-up system security detection. An improved transformer anomaly detection method based on FLOATER positional coding is applied in the security detection framework to realize the early detection of network threats. Based on the continuous dynamic system, the positional coding function is learned to capture the temporal features of the sequence. Then multi-head attention is used to encode the input sequence. Moreover, the model training is optimized by the adversarial method. The experimental results demonstrated that the method applied in this work outperforms traditional deep learning models in terms of detection accuracy and F1 score. © 2022,International Journal of Network Security. All Rights Reserved.

关键词： Anomaly detection

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Robust control for Discrete-Time T-S Fuzzy Singular Systems

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Journal of Systems Science & Complexity 2021年第4期34卷 1345-1363页

作者： CHEN Jian YU Jinpeng School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520China School of Automation Qingdao UniversityQingdao 266071China

This paper deals with the robust admissibility and state feedback stabilization problems for discrete-time T-S fuzzy singular systems with norm-bounded *** introducing a new approximation technique,the initial membership functions are conveniently expressed in piecewiselinear functions with the consideration of the approximation *** utilizing the piecewise-linear membership functions,the fuzzy weighting-based Lyapunov function and the use of auxiliary matrices,the admissibility of the systems is determined by examining the conditions at some sample *** conditions can be reduced into the normal parallel distributed compensation ones by choosing special values of some slack ***,the authors design the robust state feedback controller to guarantee the closed-loop system to be *** examples are provided to illustrate the advantage and effectiveness of the proposed method.

关键词： Admissible discrete-time singular systems piecewise-linear membership functions state feedback T-S fuzzy models

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Category-Level and Open-Set Object Pose Estimation for Robotics

arXiv

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arXiv 2025年

作者： Hönig, Peter Hirschmanner, Matthias Vincze, Markus Automation and Control Institute Faculty of Electrical Engineering TU Wien Vienna1040 Austria

Object pose estimation enables a variety of tasks in computer vision and robotics, including scene understanding and robotic grasping. The complexity of a pose estimation task depends on the unknown variables related to the target object. While instance-level methods already excel for opaque and Lambertian objects, category-level and open-set methods, where texture, shape, and size are partially or entirely unknown, still struggle with these basic material properties. Since texture is unknown in these scenarios, it cannot be used for disambiguating object symmetries, another core challenge of 6D object pose estimation. The complexity of estimating 6D poses with such a manifold of unknowns led to various datasets, accuracy metrics, and algorithmic solutions. This paper compares datasets, accuracy metrics, and algorithms for solving 6D pose estimation on the category-level. Based on this comparison, we analyze how to bridge category-level and open-set object pose estimation to reach generalization and provide actionable recommendations. Copyright © 2025, The Authors. All rights reserved.

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Based on an improved Zero-DCE++ low-light enhanced object detection algorithm

Based on an improved Zero-DCE++ low-light enhanced object de...

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2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Bai, Xuecheng Gao, Hongwei Zhao, Zhanpeng Yan, Zhiwen School of Automation and Electrical Engineering Shenyang Ligong University Liaoning Shenyang110159 China Institute of International Engineering Shenyang Ligong University Liaoning Shenyang110159 China China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110159 China Xi'an Modern Control Technology Research Institute Xi'an710100 China

ISBN: (纸本)9781510689176

Low-light image quality often suffers from noise, color distortion, and reduced contrast, challenging accurate object detection. Traditional enhancement methods can lead to over-processing and semantic information loss. This study presents an enhanced low-light image detection model based on an improved Zero-DCE++ algorithm, integrated with YOLOv8 for high-speed, accurate detection. Key improvements include the CBAM attention mechanism to enhance feature extraction in low-light areas, DCNv2 for capturing complex object details, and a Local Region Brightness Curve Enhancement Module to prevent overexposure in well-lit areas. The proposed model was tested on the Night Drone dataset, where it achieved superior performance in image quality and detection accuracy compared to state-of-the-art methods. This approach demonstrates significant potential for applications requiring reliable object detection in challenging low-light conditions. © 2025 SPIE.

关键词： Semantic Segmentation

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Distributed control Algorithm for Workload Partition of Multi-agent Systems with Time Delay 41

Distributed Control Algorithm for Workload Partition of Mult...

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第41届中国控制会议

作者： Pengyang Fan Chao Zhai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In the problem of multi-agent coverage,a group of agents are required to cover a given region or clean the workload as soon as *** workload partition can improve the work efficiency of multi-agent *** addition,the communication delays play a key role in the stability of multi-agent ***,a distributed control algorithm of workload partition with time delay is proposed in this ***,an approach based on nonlinear matrix inequality is presented to find the maximum delay of multi-agent ***,experiments are carried out to investigate the effect of delay on system stability on the ZigBee platform.

关键词： Multi-agents systems Time delay Nonlinear matrix inequality Distributed algorithm Workload partition

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Autoregressive Minimum Entropy Deconvolution Enhanced Resonance Features for Fault Diagnosis of Wind Turbine Gearbox

Autoregressive Minimum Entropy Deconvolution Enhanced Resona...

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2023 International Conference on automation, control and Electronics engineering, CACEE 2023

作者： Shi, Xiaoyu Sun, Kang Henan Polytechnic University School of Electrical Engineering and Automation Jiaozuo China Henan Polytechnic University Dianrong Intelligent Technology Co. Ltd. School of Electrical Engineering and Automation Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Jiaozuo China

ISBN: (纸本)9798350309201

Addressing the problem that the pulse component often submerges in other frequency components during the failure of the wind turbine gearbox, it is difficult to extract effectively the early fault characteristics. This paper proposed autoregressive minimum entropy deconvolution enhanced resonance features for wind turbine gearbox fault diagnosis. The method is combined with 1DCNN to achieve high-accuracy fault diagnosis. Firstly, the resonance-based sparse signal decomposition was utilized to decompose the vibration signals into a high resonance component containing noise and harmonic components and a low resonance component containing fault impulse components. Secondly, the AR-MED was employed to enhance the weak impulse features in the low resonance component, thus further enhancing the periodic impulse components of the gearbox. Finally, a feature enhanced 1DCNN was constructed to fuse the decomposed harmonic and periodic impulse components for feature integration, targeted training, and classification. The outcomes of the experiment indicated the efficacy and supremacy of the suggested approach in extracting information about fault characteristics and enhancing the accuracy of fault diagnosis in wind turbine gearbox systems in contrast to current fault diagnosis models. © 2023 IEEE.

关键词： Failure analysis

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A Deep Learning Approach for Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants 28

A Deep Learning Approach for Faults Recognition of Dissolved...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ghinea, Liliana Maria Miron, Mihaela Ratnaweera, Harsha Electrical Engineering and Electronics Faculty of Automation Computers Department of Automatic Control and Electrical Engineering Department Galaţi Romania Computers Electrical Engineering and Electronics Faculty of Automation Department of Computer Science and Information Technology Galaţi Romania Norwegian University of Life Sciences Faculty of Science and Technology Oslo Norway

ISBN: (纸本)9798350339918

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing. © 2023 IEEE.

关键词： Wastewater treatment

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Artificial Intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process

Artificial Intelligence-based Fault Detection and Diagnosis:...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Liu, Yiqi Li, Zhi Chen, Hongtian The School of Automation Science & Engineering Key Laboratory of Autonomous Systems and Networked Control Ministry of Education South China University of Technology Guangzhou510640 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350337754

As the chemical processes become more and more complicated, it is crucial but also difficult to detect and diagnose faults in the system than ever. With the development of machine learning algorithms and cloud computing, to monitor chemical processes completely with the help of big data become true. However, most machine learning methods suffer from unexplainable problem. By employing Explainable Artificial Intelligence(XAI) techniques, this paper applies two kinds of unsupervised machine learning methods, One-Class Support Vector Machine and Depth-based Isolation Forest Feature Importance, to detect and diagnosis faults in a chemical process. By combining two different fault explanation methodologies, accuracy and reliability of the fault diagnosis results can be achieved by the validation in a simulated continuous stirred tank reactor. © 2023 IEEE.

关键词： Support vector machines

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Design of Intelligent Monitoring System for Blast Furnace Hearth Erosion 41

Design of Intelligent Monitoring System for Blast Furnace He...

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第41届中国控制会议

作者： Shuqiao Wang Yan Yuan Jingjie Liang Zhuofu Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper takes the iron-making blast furnace in operation of a steel plant as the specific object,analyzes the erosion status and characteristics of different parts of the furnace hearth and bottom combined with iron-making process and erosion mechanism of the blast furnace,designs an intelligent monitoring system for erosion of the furnace hearth lining,visualizes the temperature measurement *** erosion calculation model is established by using the heat transfer mechanism to quickly solve the erosion boundary for the area with low erosion degree and rate,and performs a fast solution of the erosion *** areas with high erosion degree and rate,the particle swarm optimization algorithm is selected by using its adaptive *** solves the erosion boundary accurately from the rough solution of the mechanism model,and the residual thickness of furnace lining is output and displayed,which helps ironmakers optimize the blast furnace production operation,maintenance strategy,improve production efficiency and reduce energy consumption.

关键词： Blast furnace lining erosion Particle swarm optimization algorithm Intelligent monitoring system

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