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Research on the Validity of Network Security Model Based on WOA-BP Neural Network 15

Research on the Validity of Network Security Model Based on ...

引用

15th international conference on Communication software and Networks, ICCSN 2023

作者： Wang, Saiyu Zhu, Yanbing Xia, Handa Chen, Hongyan Geng, Rong School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350329360

In the field of network security, using machine learning to detect network anomalies and attacks is becoming increasingly popular. However, the availability of a security model based on machine learning may vary depending on the dataset and the setting of security features. In this article, we consider introducing a binary classification model for anomaly detection and a multi category classification model for various types of network attacks. machine learning algorithms used to implement the models above include Naive Bayes, Random Forest, and BP artificial neural network security models, as well as WOA optimized by BP neural network. Firstly, we conducted a series of comparative experiments by using UNSW-NB15, the most popular security data sets. On this basis, we investigated the effectiveness of the network security model. Finally, conclusions about the effectiveness of network anomaly detection are drawn, which can provide certain model ideas and data support for current network security analysis. © 2023 IEEE.

关键词： Network security

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Design of the control circuit of C523 vertical lathe on PLC 3

Design of the control circuit of C523 vertical lathe on PLC

引用

3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Wang, Yaocheng Wang, Xiaoqing Shenyang Ligong University Shenyang China

ISBN: (纸本)9781510664814

C523 vertical lathe control circuit uses a relay control method, which has some disadvantages including large size, complex wiring, high failure rate, and maintenance difficulties. To solve these problems, a C523 vertical lathe control circuit based on PLC is designed. The electronic requirements of the control circuits, basis of PLC selection, the hardware connection and software design are described. Practice has shown that the PLC control circuit is simple, reliable and can highly improve the production efficiency. © 2023 SPIE.

关键词： software design

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Multiscale Spectral–Spatial Capsule Neural Network for Hyperspectral Image Classification 1

引用

UNIfied conference of international Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, international conference on Maintenance engineering, IncoME-V 2023, international conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Wang, Weiye Xu, Yuanping Xu, Zhijie Kong, Chao Niu, Xuemei Huang, Jian School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom School of Management Chengdu University of Information Technology Chengdu China

ISBN: (数字)9783031494130

ISBN: (纸本)9783031494123

Deep learning models have recently shown good performance in the hyperspectral remote sensing image classification tasks. In particular, a capsule network (CapsNet) was introduced as a powerful alternative to convolutional neural networks (CNNs). The CapsNet adopts vector neuron and encode the spatial relationship of features in an image, which exhibits encouraging performance. Motivated by CapsNet, this paper presents a novel capsule network based on multiscale spectral–spatial features to improve the performance for hyperspectral images (HSIs) classification. First, multi-scale features are extracted from the hyperspectral data in 1D spectral, 2D spatial and 3D spatial-spectral cubes, and their primary capsule neurons are constructed separately. Then, a multi-head attention mechanism is introduced to model the association of all primary capsule neurons from multiple dimensions to efficiently extract finer-grained multi-scale spatial-spectral information. Finally, the multiscale capsule neurons are updated using a dynamic routing agreement to obtain more discriminative high-level capsule features thus improving the classification accuracy. Experimental results on two commonly used HSIs datasets (Indian Pines, Pavia University) demonstrate that the proposed model can achieve better performance compared with other state-of-the art deep-learning-based approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neurons

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Computational Thinking Education Model Through Play-Type software Teaching and learning Model 15th

Computational Thinking Education Model Through Play-Type Sof...

引用

15th international conference on Computer Science and its Applications, CSA 2023 and 17th KIPS international conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Joo, Kil Hong Seo, Ji-Hoon Park, Nam Hun Kyeongin National University of Education Anyang-Si13910 Korea Republic of Kangnam University Yongin-Si16979 Korea Republic of Anyang University Anyang-Si14028 Korea Republic of

ISBN: (纸本)9789819724468

The current trend of software education in Korea does not focus on improving computational thinking skills, but rather focuses on students consuming educational technologies developed by the private education industry, which was one step ahead of public education. In this study, we developed a play-type edutainment teaching and learning model and program by combining mathematics and software education so that lower grade students can learn with fun. To solve this research problem, qualitative research method was adopted. The qualitative study followed the techniques of design-based research to explore models for the development of research tools and to review, modify, and supplement educational programs, and to develop play-type edutainment for improving computational thinking skills developed through this study. The learning model and the effects of the board game program according to the model were analyzed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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machine learning Technique for Phishing Website Detection 8

Machine Learning Technique for Phishing Website Detection

引用

8th IEEE international conference on software engineering and Computer Systems, ICSECS 2023

作者： Zin, Nurul Amira Binti Mohd Ab Razak, Mohd Faizal Firdaus, Ahmad Ernawan, Ferda Zulkifli, Nor Saradatul Akmar Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Pahang26600 Malaysia

ISBN: (纸本)9798350310931

The Internet has emerged as an indispensable tool in both our personal and professional life in our modern day. As a direct consequence of this, the number of customers who make their purchases over the Internet is quickly increasing. Internet users may be vulnerable to a wide variety of web threats because of this fact. These threats may result in monetary loss, fraudulent use of credit cards, loss of personal data, potential damage to a brand's reputation, and customer mistrust in e-commerce and online banking. Phishing is a sort of cyber threat that may be defined as the practice of imitating a genuine website for the purpose of stealing sensitive information such as usernames, passwords, and credit card numbers. This research focuses on strategies for detecting phishing attacks. This study apply a machine learning approach to detect a phishing attack. As a result, this study able to detect phishing with accuracy 94%. © 2023 IEEE.

关键词： Websites

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Fairify: Fairness Verification of Neural Networks 23

Fairify: Fairness Verification of Neural Networks

引用

45th IEEE/ACM international conference on software engineering (ICSE)

作者： Biswas, Sumon Rajan, Hridesh Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA Iowa State Univ Dept Comp Sci Ames IA USA

ISBN: (纸本)9781665457019

Fairness of machine learning (ML) software has become a major concern in the recent past. Although recent research on testing and improving fairness have demonstrated impact on real-world software, providing fairness guarantee in practice is still lacking. Certification of ML models is challenging because of the complex decision-making process of the models. In this paper, we proposed Fairify, an SMT-based approach to verify individual fairness property in neural network (NN) models. Individual fairness ensures that any two similar individuals get similar treatment irrespective of their protected attributes e.g., race, sex, age. Verifying this fairness property is hard because of the global checking and non-linear computation nodes in NN. We proposed sound approach to make individual fairness verification tractable for the developers. The key idea is that many neurons in the NN always remain inactive when a smaller part of the input domain is considered. So, Fairify leverages white-box access to the models in production and then apply formal analysis based pruning. Our approach adopts input partitioning and then prunes the NN for each partition to provide fairness certification or counterexample. We leveraged interval arithmetic and activation heuristic of the neurons to perform the pruning as necessary. We evaluated Fairify on 25 real-world neural networks collected from four different sources, and demonstrated the effectiveness, scalability and performance over baseline and closely related work. Fairify is also configurable based on the domain and size of the NN. Our novel formulation of the problem can answer targeted verification queries with relaxations and counterexamples, which have practical implications.

关键词： fairness verification machine learning

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Applications of machine learning in Requirements Traceability: A Systematic Mapping Study 35

Applications of Machine Learning in Requirements Traceabilit...

引用

35th international conference on software engineering and Knowledge engineering, SEKE 2023

作者： Li, Xingfu Wang, Bangchao Wan, Hongyan Deng, Yang Wang, Zihan School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China Engineering Research Center of Hubei Province for Clothing Information Wuhan Textile University Wuhan China School of Mathematical & Physical Science Wuhan Textile University Wuhan China

Requirements traceability (RT) is crucial for requirement management and impact analysis of requirement change in software development. The applications of machine learning (ML) technologies to RT have received much attention. In this paper, we aim to provide the state-of-the-art progress of the studies on the intersection of ML and RT. A systematic mapping study (SMS) is conducted and 26 studies have been identified as primary studies. The results present 32 ML technologies and 7 enhancement strategies for establishing trace links. Besides, 46 datasets are utilized for validating the performance of these ML technologies. Additionally, the overall quality of these primary studies is at a good level. This study indicates that numerous studies have proved the potential of utilizing ML technologies for predicting emerging trace links in RT by utilizing existing traceability information. Moreover, open-source datasets are the most popular, which greatly improves the reproducibility of studies. However, there is still a gap between academia and industrial application because of the lack of industrial practice and guidance from practitioners. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： machine learning

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Replay-Driven Continual learning for the Industrial Internet of Things 2

Replay-Driven Continual Learning for the Industrial Internet...

引用

IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Sen, Sagar Nielsen, Simon Myklebust Husom, Erik Johannes Goknil, Arda Tverdal, Simeon Pinilla, Leonardo Sastoque SINTEF Digital Sustainable Commun Technol Oslo Norway Univ Oslo Dept Informat Oslo Norway Univ Basque Country CFAA Zamudio Spain

ISBN: (纸本)9798350301137

The Industrial Internet of Things (IIoT) leverages thousands of interconnected sensors and computing devices to monitor and control large and complex industrial processes. machine learning (ML) applications in IIoT use data acquired from multiple sensors to perform tasks such as predictive maintenance. While remembering useful learning from the past, these applications need to adapt learning for evolving sensor data stemming from changes in industrial processes and environmental conditions. This paper presents a continual learning pipeline to learn from the evolving data while replaying selected parts of the old data. The pipeline is configured to produce ML experiences (e.g., training a baseline neural network model), improve the baseline model with the new data while replaying part of the old data, and infer/predict using a specific model version given a stream of IIoT sensor data. We have evaluated our approach from an AI engineering perspective using three industrial case studies, i.e., predicting tool wear, remaining useful lifetime, and anomalies from sensor data acquired from CNC machining and broaching operations. Our results show that configuring experiences for replay-driven continual learning allows dynamic maintenance of ML performance on evolving data while minimizing the excessive accumulation of legacy sensor data.

关键词： Continual learning Industrial IoT machine learning

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engineering Challenges for AI-Supported Computer Vision in Small Uncrewed Aerial Systems 2

Engineering Challenges for AI-Supported Computer Vision in S...

引用

IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Chowdhury, Muhammed Tawfiq Cleland-Huang, Jane Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9798350301137

Computer Vision (CV) is used in a broad range of Cyber-Physical Systems such as surgical and factory floor robots and autonomous vehicles including small Unmanned Aerial Systems (sUAS). It enables machines to perceive the world by detecting and classifying objects of interest, reconstructing 3D scenes, estimating motion, and maneuvering around objects. CV algorithms are developed using diverse machine learning and deep learning frameworks, which are often deployed on limited resource edge devices. As sUAS rely upon an accurate and timely perception of their environment to perform critical tasks, problems related to CV can create hazardous conditions leading to crashes or mission failure. In this paper, we perform a systematic literature review (SLR) of CV-related challenges associated with CV, hardware, and software engineering. We then group the reported challenges into five categories and fourteen sub-challenges and present existing solutions. As current literature focuses primarily on CV and hardware challenges, we close by discussing implications for software engineering, drawing examples from a CV-enhanced multi-sUAS system.

关键词： Small Uncrewed Aerial Systems Computer Vision Artificial Intelligence

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How Data can Mislead machine learning Prediction Process: Case Study of Building Cooling and Heating Loads 11

How Data can Mislead Machine Learning Prediction Process: Ca...

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11th international conference on Information Technology, ICIT 2023

作者： Masoud, Mohammad Z. Manasrah, Ahmad Jaradat, Yousef Shaban, Nabeel A. Abu Al-Zaytoonah University of Jordan Electrical Engineering Department Amman Jordan Al-Zaytoonah University of Jordan Mechanical Engineering Department Amman Jordan

ISBN: (纸本)9798350320060

Building heating and cooling power consumption optimization is an important field to reduce and optimize power consumption and to reach green building. To optimize the loads, the building can be constructed utilizing simulation software with its real materials, diminutions, glazing and orientation. Subsequently, the cooling and heating consumptions can be calculated. This method is a complex process. To reduce its complexity, machine learning (ML) has emerged to predict the cooling and heating loads of buildings. However, these algorithms require datasets to train, validate and to be tested. In this work, we attempted to work with a building dataset to predict its heating and cooling loads. Five different supervised machine learning regression algorithms are leveraged. In addition, a new hybrid multi-layer machine learning algorithm (HMLM) is proposed. The dataset utilized is a dataset constructed from simulation software. Our main purpose in this work is to show that simulation datasets may mislead the ML algorithms since their data has been extracted utilizing different assumptions and different preconfigured values. Moreover, simulation datasets should be verified with any real datasets before utilizing them. We have reduced the features in the dataset to only the building height. Our results show that with only this variable an acceptable model with accuracy exceeds 91% can be trained. This is misleading information obtained from the model. © 2023 IEEE.

关键词： Buildings

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