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SAND: semi-automated adaptive network defense via programmable rule generation and deployment

science China(Information sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense adaptive defense automated defense programmable defense security services software-defined networking security management

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A Novel Shared Control Framework Based on Imitation Learning 25

A Novel Shared Control Framework Based on Imitation Learning

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Hu, Hao Shi, Donghao Yang, Chenguang Si, Weiyong Li, Qinchuan School Of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou China University Of The West Of England Bristol Robotics Laboratory Bristol United Kingdom School Of Computer Science And Electronic Engineering University Of Essex London United Kingdom

ISBN: (纸本)9798350340266

Learning from Demonstration (LfD) stands out as a powerful tool for swiftly deploying tasks in robotics, distinguished by its capacity to effectively leverage the task expertise of experts. However, contemporary robotic intelligence has not yet attained a level of complete autonomy. The integration of shared control methodologies allows for the assimilation of offline experiences derived from experts, concomitant with the introduction of online professional guidance during task execution. This study augments the established framework by incorporating electromyographic signals to discern expert intentions and utilising the Gaussian Mixture Model (GMM) to learn experience. The proposed approach engenders a more efficient shared control method. Validation of this methodology is conducted on the Omni and Franka Emika Panda robotic platforms. © 2024 IEEE.

关键词： Gaussian distribution

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A Comprehensive Study on the Role of Artificial Intelligence in Animal Husbandry

A Comprehensive Study on the Role of Artificial Intelligence...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Surya Deveswar Reddy, Kovvuri Uday Shaik, Ayesha Balasundaram, A. Nivedita, M. Ram Kakarla, Lakshmi Sai Nithin, Mattapally Sai School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Vellore Institute of Technology Center for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350394580

Distinguishing the breeds of animals is one of the major examination areas in animal welfare. Recognition of animal breeds demands numerous determining elements which are necessary to be explored and following classification of variety. Animals such as cows, buffaloes, sheep, goats, and other diverse creatures play a huge role in the production of milk, and sometimes meat. Every creature has different species and provide unique benefits which contribute a lot to the Indian economy. India is currently holding the first rank in milk export. Hence, it is compulsory to introduce automated methods that will differentiate breeds of various species. In addition, identifying diseases that effected to animals is also an important area related to animal protection and it is necessary to automate the detection as it would be effective for identify the diseases in primary stages. This paper carries out a full-scale study on how deep learning techniques are used in animal husbandry. © 2023 IEEE.

关键词： Animal Breeds Automate Methods Classification Deep learning Diseases Machine Learning

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Machine learning-aided scoring of synthesis difficulties for designer chromosomes

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science China(Life sciences) 2023年第7期66卷 1615-1625页

作者： Yan Zheng Kai Song Ze-Xiong Xie Ming-Zhe Han Fei Guo Ying-Jin Yuan Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering Ministry of EducationTianjin UniversityTianjin300072China School of Chemical Engineering and Technology Tianjin UniversityTianjin300072China School of Computer Science and Engineering Central South UniversityChangsha410083China

Designer chromosomes are artificially synthesized ***,these chromosomes have numerous applications ranging from medical research to the development of ***,some chromosome fragments can interfere with the chemical synthesis of designer chromosomes and eventually limit the widespread use of this *** address this issue,this study aimed to develop an interpretable machine learning framework to predict and quantify the synthesis difficulties of designer chromosomes in *** the use of this framework,six key sequence features leading to synthesis difficulties were identified,and an e Xtreme Gradient Boosting model was established to integrate these *** predictive model achieved high-quality performance with an AUC of 0.895 in cross-validation and an AUC of 0.885 on an independent test *** on these results,the synthesis difficulty index(S-index)was proposed as a means of scoring and interpreting synthesis difficulties of chromosomes from prokaryotes to *** findings of this study emphasize the significant variability in synthesis difficulties between chromosomes and demonstrate the potential of the proposed model to predict and mitigate these difficulties through the optimization of the synthesis process and genome rewriting.

关键词： synthetic biology machine learning artificial chromosome chemical synthesis

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Study on Method of Bearing Fault Detection Based on Vibration Signal Analysis

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engineering Letters 2023年第3期31卷 1009-1015页

作者： Yang, Guang Liu, Xinrong Engineering Training Center Yantai University Yantai264005 China School of Computer Science and Technology Shandong Technology and Business University Yantai264005 China

This paper introduces a complete method for bearing fault detection. By analyzing the vibration signal when a fault occurs, the type of fault can be detected. The number of high-frequency intrinsic mode functions (IMFs) is determined by calculating the cumulative mean of each IMF. In the modified ensemble empirical mode decomposition (EEMD), only the high-frequency IMFs need to be decomposed out. The scientific calculation process for the parameters of the algorithm is explained in detail. The function of the kurtosis value and the correlation coefficient is analyzed, and the IMFs with the most abundant fault information are selected to restructure the signal. The reconstructed signal is then processed by wavelet algorithm. The fault vibration signal from the bearing inner ring is used as an example for the experiment. The fault characteristic frequency of bearing inner race is calculated by formula. By analyzing the envelope spectrum of the final restructured signal, the fault information is effectively detected. The results demonstrate that the method given by this paper can detect bearing fault information more quickly and efficiently. © 2023, International Association of Engineers. All rights reserved.

关键词： Intrinsic mode functions

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CHART:Intelligent Crime Hotspot Detection and Real-Time Tracking Using Machine Learning

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computers, Materials & Continua 2024年第12期81卷 4171-4194页

作者： Rashid Ahmad Asif Nawaz Ghulam Mustafa Tariq Ali Mehdi Tlija Mohammed AEl-Meligy Zohair Ahmed University Institute of Information Technology PMAS Arid Agriculture UniversityRawalpindi46000Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia Jadara University Research Center Jadara UniversityIrbid21110Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Science and Engineering Central South UniversityChangsha410083China

Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public *** methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the *** some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal *** research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these *** proposed methodology begins with comprehensive data collection from the police *** dataset includes detailed attributes such as crime type,location,time and demographic *** key steps in the proposed framework include:Data Preprocessing,Feature engineering that leveraging domain-specific knowledge to extract and transform relevant *** Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various *** Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),*** designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime *** of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhanc

关键词： Crime hotspot heat map kernel density estimation(KDE) support vector machine(SVM)

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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

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2023 International Conference on Electrical engineering and Informatics, ICEEI 2023

作者： Medyanto, Richard Sanjaya, Nicholas Liawatimena, Suryadiputra Binus Aso School of Engineering Bina Nusantara University Automotive & Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350381290

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers. © 2023 IEEE.

关键词： Solar energy

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Simulation extractable SNARKs based on target linearly collision-resistant oracle

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science China(Technological sciences) 2024年第9期67卷 2853-2866页

作者： WANG LiGuan LI Yuan ZHANG ShuangJun CAI DongLiang KAN HaiBin Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Shanghai Engineering Research Center of Blockchain Shanghai 200433China Yiwu Research Institute of Fudan University Yiwu 322000China

The famous zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARK) was proposed by Groth in ***, the construction is based on quadratic arithmetic programs which are highly efficient concerning the proof length and the verification complexity. Since then, there has been much progress in designing zk-SNARKs, achieving stronger security,and simulated extractability, which is analogous to non-malleability and has broad applications. In this study, following Groth's pairing-based zk-SNARK, a simulation extractability zk-SNARK under the random oracle model is constructed. Our construction relies on a newly proposed property named target linearly collision-resistant, which is satisfied by random oracles under discrete logarithm assumptions. Compared to the original Groth16 zk-SNARK, in our construction, both parties are allowed to use such a random oracle, aiming to get the same random number. The resulting proof consists of 3 group elements and only 1 pairing equation needs to be verified. Compared to other related works, our construction is shorter in proof length and simpler in verification while preserving simulation extractability. The results also extend to achieve subversion zero-knowledge SNARKs.

关键词： quadratic arithmetic program simulation extractability subversion zero-knowledge succinct non-interactive arguments of knowledge target linearly collision-resistant

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Autonomous Liver Ultrasound Examination Based on Imitation Learning and Stiffness Estimation 25

Autonomous Liver Ultrasound Examination Based on Imitation L...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Wang, Zihao Shi, Donghao Yang, Chenguang Si, Weiyong Li, Qinchuan School Of Mechanical Engineering Zhejiang Sci-Tech University Hangzhou China Bristol Robotics Laboratory University Of The West Of England Bristol United Kingdom School Of Computer Science And Electronic Engineering University Of Essex London United Kingdom

ISBN: (纸本)9798350340266

In the past few decades, the demand for assistive robots in the field of healthcare has steadily increased. We conducted research on an autonomous robot system based on imitation learning to assist in liver scans guided by ultrasound. Traditional procedures for liver examination using ultrasound imaging typically require a physician to slide the ultrasound probe along the patient's lower ribs for multi-angle observation. To automate this process, we employed imitation learning to capture the physician's manipulation techniques. However, the imitation generalization system often struggles to accurately extract the starting and ending points due to the obstruction of fat and skin, and the individual variations in patient anatomy lead to inconsistencies in the positions of lower ribcage. To improve the scanning process, we introduced stiffness estimation to differentiate between the abdomen and the rib, allowing real-time adjustments for optimal alignment of lower ribcage. Furthermore, due to the need for occasional physician intervention during the detection process, we implemented mechanisms for pausing and resuming the trajectory learning system to ensure continued operation. Ultimately, the proposed approach was evaluated using the Franka Panda robot system and demonstrated autonomous liver ultrasound examination in scenarios involving multiple human subjects. © 2024 IEEE.

关键词： Stiffness

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