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Automatic Detection Method of Website Vulnerabilities Based on an Associated Data Drive

JOURNAL OF WEB ENGINEERING 2025年第2期24卷 217-242页

作者： Li, Xiaoli Zhao, Ling Shen, Haobin Du, Hanlin Guo, Zhida Guangdong Power Grid Co Ltd Huizhou Power Supply Bur Huizhou 516000 Peoples R China

In order to reduce the probability of website users being attacked and maintain the safety of website operation, this study proposes an automatic vulnerability detection method of websites based on associated data. We use plug-ins to scan the website in all directions, establish a scanning database, and classify and store the scanned web data. By applying optimized an a priori association rule algorithm, key features are extracted from web scan data, which are then transformed into input samples for a K-means clustering algorithm. The aim is to efficiently extract feature attributes of website vulnerability data and ultimately construct a text vectorized representation of vulnerability data. Convolutional neural networks can automatically detect website vulnerabilities by using the constructed text vector as input. Experimental verification shows that this method demonstrates comprehensive data coverage, efficient processing speed, and high-precision recognition perforensures the accuracy and timeliness of vulnerability detection.

关键词： A priori algorithm website website vulnerability automated detection clustering algorithm convolutional neural network

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Optimal Load Shedding Under Extreme Weather Events

Optimal Load Shedding Under Extreme Weather Events

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Bernabeu, Emanuel Chen, Y. Militello, N. Furtaw, N. Martin, A. Wharton, M. PJM Interconnect Phoenixville PA 19460 USA

ISBN: (纸本)9798350381849;9798350381832

On June 13-14, 2022 a derecho storm caused numerous forced outages in the Columbus Ohio area;including fifteen 138 kV lines and one 345 kV line. PJM, in coordination with American Electric Power (AEP), implemented corrective actions (generation re-dispatch, topology control, and demand response) in an attempt to restore the grid to an N-1 secure state. Ultimately, 500 MWs of firm load were shed to mitigate the risk of cascading outages. PJM's post-event analysis revealed that, due to the numerous potential cascading failures, operators had a difficult time developing a load shed plan. The purpose of this paper is to provide a methodology that minimizes the amount of load shed needed and jointly mitigates all potential cascading outages. The proposed methodology consists of two steps: (1) linear optimization using post-contingency power flow distribution factors, and (2) a machine-learning based clustering algorithm (DBSCAN). The methodology was evaluated using Real Time saved State Estimator (SE) Energy Management System (EMS) cases from the event. Results show that the proposed method enhances situational awareness, simplifies the risk-based decision making process, and potentially reduces the amount of load shed needed by 20%.

关键词： cascading outages clustering algorithm extreme weather load shedding machine learning optimization

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Accelerating DNN-Based Detection by MADDNESS Approximation 99

Accelerating DNN-Based Detection by MADDNESS Approximation

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IEEE 99th Vehicular Technology Conference (VTC-Spring)

作者： Gong, Shuaicong Qian, Shuangyi Zhao, Chunming Jiang, Ming Zhang, Yifan Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (纸本)9798350387414

Non-rectangular QAM can achieve better bit error rate performance in the face of different channel characteristics, and finding the constellation distribution that best matches the channel characteristics has always been a topic of discussion. The NVIDIA/Sionna open-source library provides a scheme of end-to-end transmission comprised of trainable constellation and deep neural network detection to achieve accurate information transfer. However, this application has a high computational complexity in the detection part, which contains dense layers that involve intensive and massive matrix multiplication operations. This study considers using an approximate matrix multiplication (AMM) algorithm based on table lookup, Multiply-ADDitioN-lESS (MADDNESS), to replace the accurate multiplication calculations in the DNN-Based detection and achieves a significant reduction in overall computational complexity at the cost of certain storage.

关键词： DNN detection Deep Learning clustering algorithm Approximate Multiplication Vector Quantization

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Research on K-medoids algorithm with Probabilistic-based Expressions and Its Applications

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APPLIED INTELLIGENCE 2022年第10期52卷 12016-12033页

作者： He, Yue Xu, Zeshui Liu, Nana Sichuan Univ West China Second Univ Hosp Chengdu 610041 Sichuan Peoples R China Sichuan Univ Sch Business Chengdu 610064 Peoples R China

Nowadays, the decision environment is becoming more and more complicated due to the development of society and science, and people receiving different types of information every day and gradually form their unique cognitions and knowledge backgrounds. In this situation, people tend to provide their preferences or evaluations through multiple information expression formats, such as the probabilistic hesitant fuzzy sets, the probabilistic linguistic term sets and the probabilistic interval preference ordering sets. In this paper, we deeply investigate the relationships among these three probabilistic-based expressions and introduce two transformation functions for them in order to make the information formats unified. Besides, for the probabilistic hesitant fuzzy sets, three novel distance measures are proposed, i.e., the minimal distance, the central distance, and the improved distance, which are useful tools to measure the difference between the probabilistic hesitant fuzzy elements. In order to fuse the different formats of information and get valuable results from it, the K-medoids algorithm for the probabilistic-based expressions is developed. The algorithm is applied to classify the merchants on the website of , so that the results can be provided to customers to help them make decisions.

关键词： K-medoids clustering algorithm Probabilistic-based expression Distance measure

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Stakeholder-Centric clustering Methods for Conflict Resolution in the Requirements Engineering Process 16th

Stakeholder-Centric Clustering Methods for Conflict Resoluti...

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16th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： Gambo, Ishaya Taveter, Kuldar Univ Tartu Inst Comp Sci Tartu Estonia Obafemi Awolowo Univ Dept Comp Sci & Engn Ife Nigeria

ISBN: (纸本)9783030966485;9783030966478

This paper describes the development and evaluation of a framework that combines expert-based and clustering methods for resolving conflicts in requirements elicited from stakeholders. The purpose of the framework was to identify and resolve conflicts among expectations by multiple stakeholders that often arise during the requirements elicitation phase. By means of qualitative and quantitative research approaches, face-to-face oral interviews, quantitative surveys, brainstorming sessions, and focus groups, scenarios were generated with stakeholders of a given problem domain. Our approach was implemented within an interactive system that empirically supports the adequacy of our framework with the involvements of experts and other stakeholders of the chosen problem domain. In addition, we presented a dataset of requirements with their weight scales that formed the basis for resolving conflicting views by stakeholders by applying scientific criteria. The framework was validated in a real-life case study. The results demonstrated 85.71% of correctly clustered instances of requirements, based on which the experts agreed that the interactive system was good enough for resolving conflicting subjective views in requirements analysis. The research performed has the two-fold threat to validity, which suggests (i) the need to adequately capture and harmoniously represent the views by different stakeholders in a multicultural and multidisciplinary domain and (ii) the need to validate the framework in other real life case studies in different domains. The research performed has a high potential for reducing software development costs and saving time at the early stage of the development of software products.

关键词： Requirements engineering Stakeholders Conflict resolution clustering algorithm Delphi method Software engineering

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Analysis of Operational Data and Self-learning Control Rules for Mining Mill Equipment 14

Analysis of Operational Data and Self-learning Control Rules...

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14th Asian Control Conference (ASCC)

作者： Wang, Guicheng Yuan, Liang Bai, Zhicheng Cai, Guoliang Hua, Ning Li, Xiaobin Shanghai Inst Technol Sch Elect & Elect Engn Shanghai Peoples R China State Key Lab Intelligent Optimized Mfg Min & Met Beijing Peoples R China Beijing Key Lab Proc Automat Min & Met Beijing Peoples R China Zijin Intelligent Control Xiamen Technol Co Ltd Xiamen Peoples R China

ISBN: (纸本)9798331540845;9789887581598

The engineering application under industrial big-data analysis is of great significance for improving the optimization effect of enterprise production process control processes. By analyzing the operating principle of the semi-automatic mill and studying its control optimization strategy, based on the operational data of the SAG mill, industrial big-data analysis is adopted to self-learning its control optimization rules. Firstly, SPSS is used to preprocess the high-dimensional process data generated during the operation of the semi-automatic mill. Four parameters, namely power, axial pressure, grinding sound, and grinding concentration, are preliminarily selected. Then, PCA is used to screen and reduce the dimensionality of multidimensional data, and K-Means clustering algorithm is used to classify and identify the reduced dataset. After simulation research, the results show that PCA can reduce the redundancy of SAG mill operation data, and the control optimization rules formed by self-learning can serve as operational guidance for improving the efficiency and energy-saving of SAG mill operation. The effectiveness can be further verified in practical engineering applications in the later stage.

关键词： SAG mill industrial big-data PCA clustering algorithm control optimization

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Analysis of Customer Energy Consumption Patterns using an Online Fuzzy clustering Technique

Analysis of Customer Energy Consumption Patterns using an On...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Aguilar, Jose Quintero Gull, Carlos R-Moreno, Maria D. Viera, Juan Univ Alcalci Escuela Politecn Super ISG Alcala De Henares 28805 Spain Univ Los Andes CEMISID Merida 5101 Venezuela Univ EAFIT GIDITIC Medellin 50022 Colombia Univ Los Andes Fac Ingn Ciencias Aplicadas Dpto Ciencias Aplicadas Humanist Merida 5101 Venezuela Intelligent Autonomous Syst Grp IAS TNO The Hague Netherlands

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

Currently, there is a high rate of generation of new information about the Energy Consumption of customers. It is important the traceability of its consumption pattern evolution to determine in real-time the services of a smart energy management system. This paper analyses the evolution of the Energy Consumption Pattern of customers using the Learning algorithm for Multivariable Data Analysis (LAMDA). LAMDA is a fuzzy approach for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class/cluster, through the contributions of all its descriptors. LAMDA can create new classes/clusters after the training stage (online learning). If an individual does not have enough similarity to the preexisting classes/clusters, it is evaluated with respect to a threshold called the Non-Informative Class (MC) to define if it is a new class/cluster. Particularly, the algorithm of the LAMDA family used in this paper is LAMDA-RD (Robust Distance). In the paper is analyzed the patterns of the initial grouping of the data, as well as, the patterns through their evolution (traceability). For the analysis of the patterns different metrics are considers: Calinski- Harabasz Index and Silhouette Score.

关键词： clustering algorithm fuzzy systems LAMDA Online Learning pattern evolution

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Optimizing Future Predictions in Children's Health: Implementing OGPA-enhanced Deep Learning for Precise Child Height Forecasting in the Social Media Age 27

Optimizing Future Predictions in Children's Health: Implemen...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Shao, Yantao He, Tianxiang Mao, Yan Fang, Kai Wang, Wei Mao, Keji Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou Peoples R China Zhejiang A&F Univ Sch Math & Comp Sci Hangzhou Peoples R China Shenzhen MSU BIT Univ AI Res Inst Guangdong HK Macao Jt Lab Emot Intell & Pervas Co Shenzhen Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In recent years, the issue of children's height has garnered widespread attention on social media. Social media platforms serve as pivotal communication channels for parents, doctors, educators, and researchers, with children's height, a crucial health indicator, becoming one of the hot topics. Inaccurate prediction methods might mislead the public, resulting in parents harboring erroneous expectations regarding their children's future height. Such misplaced expectations may culminate in unwarranted worries or pressure. In pursuit of devising an accurate height prediction model, this paper thoroughly leverages a substantial data sample obtained from the physical health examinations of primary and secondary school students in Zhejiang Province, as well as continuous observation samples provided by the Zhejiang Provincial Bone Age Research Center, to delve deeply into the issue of height prediction in children and adolescents. In this research, we have developed a lightweight neural net-work model suitable for particle swarm optimization to predict children's stage-wise height. When the difference between the actual and predicted values is within +/- 2cm, the prediction accuracy for boys reached 86.67%, and for girls, it was 85.32%, with an RMSE of 1.3503.

关键词： Social Media Height Prediction clustering algorithm Neural Network Particle Swarm Optimization algorithm

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Adaptive Load Balancing Routing algorithm For Low Earth Orbit Satellite Cluster Networks 9

Adaptive Load Balancing Routing Algorithm For Low Earth Orbi...

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9th International Conference on Computer and Communication Systems (ICCCS)

作者： Wang, Liming Xu, Zhan Zhi, Ruxin Wang, Jiahui Beijing Informat Sci & Technol Univ Sch Elect & Commun Engn Beijing Peoples R China Beijing Informat Sci & Technol Univ Key Lab Informat & Commun Syst Minist Informat Ind Beijing Peoples R China Beijing Informat Sci & Technol Univ Key Lab Modern Measurement & Control Technol Minist Educ Beijing Peoples R China

ISBN: (纸本)9798350350227;9798350350210

Large-scale satellite networks are a crucial component of future communication systems. However, they pose several challenges to satellite routing design and link recovery. This paper proposes an Adaptive Load Balancing routing algorithm for Low Earth Orbit (LEO) Satellite Cluster Networks (ALBSCN) to meet the needs of large-scale Low Earth Orbit LEO satellite networks. The constellation is divided into clusters, with each cluster maintaining the intra-cluster link state by electing a cluster head. The cluster head computes intra-cluster and inter-cluster routes. Messages are forwarded using inter-cluster multipath routing and intra-cluster routing techniques. We validated the algorithm's performance and compared it with other algorithms in terms of packet delivery delay using simulation. Simulation results indicate that the proposed clustering routing scheme effectively reduced packet loss and end-to-end delay, while also increasing network throughput.

关键词： clustering algorithm load balancing LEO routing

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Deep code search efficiency based on clustering

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第13期36卷 e8027-e8027页

作者： Liu, Kun Liu, Jianxun Hu, Haize Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China

The deep-learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering-based code search model (C-DCS). C-DCS uses the K-Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C-DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C-DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C-DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C-DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K-Means algorithm to improve the code search efficiency of C-DCS further, reducing the search time to 93.8% of the baseline model. Hence, C-DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development.

关键词： clustering algorithm code search code search efficiency deep learning

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