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Detecting and Identifying Insider Threats Based on Advanced clustering Methods

IEEE ACCESS 2024年 12卷 30242-30253页

作者： Nikiforova, Oksana Romanovs, Andrejs Zabiniako, Vitaly Kornienko, Jurijs Riga Tech Univ Fac Comp Sci & Informat Technol LV-1048 Riga Latvia ABC Software Ltd LV-1012 Riga Latvia

This paper explores the analysis of user behavior in information systems through audit records, creating a behavior model represented as a graph. The model captures actions over a specified period, facilitating real-time comparison to identify insider threats exploring anomalies detected in behavior models. "e-StepControl," developed by "ABC software" Ltd., incorporates this approach for monitoring user behavior in different business environments. The study proposes enhancing this solution with automatic user clustering, achieved by grouping individuals exhibiting similar behavior patterns using AI/ML algorithms. The research evaluates various clustering methods, discussing their suitability for grouping users based on their behavior. The subsequent step involves leveraging user class behavior models to identify anomalies by comparing an individual's actions with the behavior model expected in their specific user group. This extension aims to enhance the system's ability to detect potentially malicious activities, providing data security administrators with timely alerts in case of deviations from typical behavior.

关键词： Behavioral sciences Analytical models Security clustering algorithms Manuals Data models clustering methods Threat assessment Anomaly detection Information systems User experience Information security clustering algorithms data mining information system user behavior analysis information technology security insider threats detection

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Low-Rank Graph Completion-Based Incomplete Multiview clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第6期35卷 8064-8074页

作者： Cui, Jinrong Fu, Yulu Huang, Cheng Wen, Jie South China Agr Univ Coll Math & Informat Guangzhou 510620 Peoples R China Harbin Inst Technol Shenzhen Shenzhen Key Lab Visual Object Detect & Recognit Shenzhen 518055 Peoples R China

In order to reduce the negative effect of missing data on clustering, incomplete multiview clustering (IMVC) has become an important research content in machine learning. At present, graph-based methods are widely used in IMVC, but these methods still have some defects. First, some of the methods overlook potential relationships across views. Second, most of the methods depend on local structure information and ignore the global structure information. Third, most of the methods cannot use both global structure information and potential information across views to adaptively recover the incomplete relationship structure. To address the above issues, we propose a unified optimization framework to learn reasonable affinity relationships, called low-rank graph completion-based IMVC (LRGR_IMVC). 1) Our method introduces adaptive graph embedding to effectively explore the potential relationship among views;2) we append a low-rank constraint to adequately exploit the global structure information among views;and 3) this method unites related information within views, potential information across views, and global structure information to adaptively recover the incomplete graph structure and obtain complete affinity relationships. Experimental results on several commonly used datasets show that the proposed method achieves better clustering performance significantly than some of the most advanced methods.

关键词： clustering algorithms Laplace equations Tensors Kernel Periodic structures Optimization Machine learning Graph completion graph learning incomplete view low-rank representation multiview clustering (MVC)

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A Trend-Granulation-Based Fuzzy C-Means Algorithm for clustering Interval-Valued Time Series

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第3期32卷 1263-1277页

作者： Yang, Zonglin Yu, Fusheng Pedrycz, Witold Yang, Huilin Tang, Yuqing Ouyang, Chenxi Beijing Normal Univ Sch Math Sci Beijing 100875 Peoples R China Beijing Normal Univ Key Lab Math & Complex Syst Minist Educ Beijing 100875 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Fac Engn & Nat Sci Dept Comp Engn TR-34010 Istanbul Turkiye

Along with the abundant appearance of the interval-valued time series (ITS), the study on ITS clustering, especially shape-based ITS clustering, is becoming increasingly important. As an effective approach to extracting trend information in time series, fuzzy trend granulation addresses the needs of shape-based ITS clustering. However, when extracting trend information in ITS, unequal-size granules are inevitably produced, which makes ITS clustering difficult and challenging. Facing this issue, this article aims to generalize the widely used fuzzy C-means (FCM) algorithm to a fuzzy trend-granulation-based FCM algorithm for ITS clustering. To this end, a suite of algorithms, including ITS segmenting, segment merging, and granule building algorithms, are first developed for fuzzy trend-granulation of ITS, with which the given ITS is transformed into granular ITS, which consists of double linear fuzzy information granules (DLFIGs) and may be of different lengths. With the defined distance between DLFIGs, the distance between granular ITS is further developed through the dynamic time warping (DTW) algorithm. In designing the fuzzy trend-granulation-based FCM algorithm, the key step is to design the method for updating cluster prototypes to cope with the unequal lengths of granular ITS. The weighted DTW barycenter averaging method is a previously adopted prototype updating approach with the drawback of hardly changing the lengths of prototypes, which often makes prototypes less representative. Thus, a granule splitting and merging algorithm is designed to resolve this issue. Additionally, a prototype initialization method is also proposed to improve the clustering performance. The proposed fuzzy trend-granulation-based FCM algorithm for clustering ITS, being a typical shape-based clustering algorithm, exhibits superior performance, which is validated by the ablation experiments as well as the comparative experiments.

关键词： Time series analysis clustering algorithms Market research Prototypes Shape Merging Filtering Dynamic time warping (DTW) fuzzy C-means (FCM) fuzzy information granules fuzzy trend granulation interval-valued time series (ITS) clustering

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Feature Weighting-Based Deep Fuzzy C-Means for clustering Incomplete Time Series

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第12期32卷 6835-6847页

作者： Li, Yurui Du, Mingjing Zhang, Wenbin Jiang, Xiang Dong, Yongquan Jiangsu Normal Univ Sch Comp Sci & Technol Jiangsu Key Lab Educ Intelligent Technol Xuzhou 221116 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

Time-series clustering is a crucial unsupervised technique for analyzing data, commonly used in various fields, including medicine and stock analysis. However, in real-world scenarios, time-series data inevitably contain missing values, consequently reducing the efficiency of traditional clustering methods. In incomplete time series, existing clustering methods typically adopt a two-stage strategy, i.e., initially imputing missing values followed by clustering. However, this approach of separating imputation from clustering may lead to inconsistencies in the optimization objectives and increase the complexity of parameter tuning, potentially resulting in unsatisfactory clustering results. This article proposes an end-to-end deep fuzzy clustering (EEDFC) model for incomplete time series, which jointly optimizes imputation and clustering within a unified framework by integrating multiple losses. In the imputation part, an attention mechanism is integrated to tackle challenges associated with dependencies in extended sequences. In addition, an adversarial strategy is introduced to enhance the encoder's imputation and feature representation learning capability, thus reducing the error propagation from imputation to clustering. In the clustering part, EEDFC combines a feature weighting-based fuzzy clustering, which considers intracluster compactness and intercluster separateness. Furthermore, exponential distance is adopted, and feature and cluster weighting are also integrated into the Kullback-Leibler divergence loss to improve clustering performance. We conduct extensive experiments comparing our proposed model with eleven other methods across ten benchmark datasets. The experimental results demonstrate that our proposed model performs better than eleven comparative methods.

关键词： Time series analysis Imputation clustering algorithms Fuzzy systems Attention mechanisms Training Representation learning Deep learning end-to-end fuzzy clustering missing values imputation time series

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M3W: Multistep Three-Way clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第4期35卷 5627-5640页

作者： Du, Mingjing Zhao, Jingqi Sun, Jiarui Dong, Yongquan Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Jiangsu Normal Univ Sch Fine Arts Xuzhou 221116 Jiangsu Peoples R China

Three-way clustering has been an active research topic in the field of cluster analysis in recent years. Some efforts are focused on the technique due to its feasibility and rationality. We observe, however, that the existing three-way clustering algorithms struggle to obtain more information and limit the fault tolerance excessively. Moreover, although the one-step three-way allocation based on a pair of fixed, global thresholds is the most straightforward way to generate the three-way cluster representations, the clusters derived from a pair of global thresholds cannot exactly reveal the inherent clustering structure of the dataset, and the threshold values are often difficult to determine beforehand. Inspired by sequential three-way decisions, we propose an algorithm, called multistep three-way clustering (M3W), to address these issues. Specifically, we first use a progressive erosion strategy to construct a multilevel structure of data, so that lower levels (or external layers) can gather more available information from higher levels (or internal layers). Then, we further propose a multistep three-way allocation strategy, which sufficiently considers the neighborhood information of every eroded instance. We use the allocation strategy in combination with the multilevel structure to ensure that more information is gradually obtained to increase the probability of being assigned correctly, capturing adaptively the inherent clustering structure of the dataset. The proposed algorithm is compared with eight competitors using 18 benchmark datasets. Experimental results show that M3W achieves superior performance, verifying its advantages and effectiveness.

关键词： clustering algorithms Resource management Decision making clustering methods Sun Rough sets Information processing clustering fuzzy-rough set theory three-way decision theory uncertain data analysis

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A Fuzzy C-Means clustering-Based Hybrid Multivariate Time Series Prediction Framework With Feature Selection

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第8期32卷 4270-4284页

作者： Zhan, Jianming Huang, Xianfeng Qian, Yuhua Ding, Weiping Hubei Minzu Univ Sch Math & Stat Enshi 445000 Peoples R China Shanxi Univ Inst Big Data Sci & Ind Taiyuan 030006 Peoples R China Shanxi Univ Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Peoples R China Shanxi Univ Engn Res Ctr Machine Vis & Data Min Shanxi Prov Taiyuan 030006 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China City Univ Macau Fac Data Sci Macau 999078 Peoples R China

Multivariate time series prediction (MTSP) stands as a significant and challenging frontier in the data science domain, garnering considerable interest among researchers. Extreme learning machine (ELM) has emerged as a popular machine learning algorithm capable of effectively addressing MTSP challenges. However, the high-dimensional and nonlinear nature of prediction information within Big Data contexts exposes certain limitations in ELM's prediction performance. To address this issue, this article proposes a hybrid MTSP framework based on fuzzy C-means (FCM) clustering coupled with feature selection. The framework begins with a possibility distribution (PD)-based feature selection algorithm designed to evaluate information quality and describe information uncertainty via multisource information fusion. Subsequently, a robust FCM algorithm is developed, optimizing the clustering process by incorporating feature differences and neighbor information of samples while employing a multimetric hybrid strategy to determine cluster numbers. Additionally, an enhanced dual-kernel ELM (EDKELM) network is established to enhance prediction capabilities. The resulting hybrid MTSP framework with feature selection excels in autonomously discovering intrinsic feature-model connections, exhibiting superior prediction performance, and demonstrating excellent generalization ability. Experimental results using real-world datasets showcase the competitiveness of the proposed framework over existing machine learning prediction models in resolving multivariate prediction challenges.

关键词： Feature extraction Predictive models clustering algorithms Prediction algorithms Time series analysis Data models Possibility theory Extreme learning machine (ELM) feature selection fuzzy C-means (FCM) clustering multivariate time series prediction (MTSP) possibility theory

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Multi-Step clustering and Generalized Nash Bargaining-Based Planning Strategy of Community-Shared Energy Storage for Large-Scale Prosumers

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2024年第2期15卷 1013-1027页

作者： Zhang, Tianhan Chen, Changming Ma, Longyi Chen, Taowei Wei, Yuanqing Lin, Zhenzhi Srinivasan, Dipti Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Guangdong Power Grid Co Ltd Huizhou Power Supply Co Huizhou 441300 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

Inspired by economies of scale, the joint planning of community-shared energy storage (CSES) among prosumers provides a new solution to the issues of high investment costs associated with independent construction of individual energy storage. However, the cooperation among large-scale prosumers is rarely investigated and there is an urgent need to formulate an appropriate strategy. Hence, a multi-step clustering and generalized Nash bargaining (GNB)-based CSES joint planning strategy for large-scale prosumers is proposed. First, a CSES planning-oriented multi-step clustering algorithm based on nonparametric kernel density estimation (NPKDE) and a modified fuzzy C-means (FCM) algorithm is proposed to reduce the input data dimensionality of the planning model. Then, taking the optimal construction time sequence into consideration, a bi-level CSES joint planning model is established to minimize the total costs of prosumers. On this basis, a contribution rate index is designed to evaluate prosumers' bargaining power, and a GNB-based allocation strategy is formulated for realizing fair cost allocation. Finally, case studies show that the proposed strategy can help large-scale prosumers effectively deal with the joint CSES planning problem and achieve lower investment and operation costs. It also demonstrates that the proposed cost allocation approach is more efficient and practical than other cooperative game-based methods.

关键词： Planning Energy storage Costs Resource management Indexes Load modeling clustering algorithms Community-shared energy storage planning strategy energy prosumer multi-step clustering generalized Nash bargaining cost allocation

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Efficient Discrete clustering With Anchor Graph

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第10期35卷 15012-15020页

作者： Wang, Jingyu Ma, Zhenyu Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

Spectral clustering (SC) has been applied to analyze varieties of data structures over the past few decades owing to its outstanding breakthrough in graph learning. However, the time-consuming eigenvalue decomposition (EVD) and information loss during relaxation and discretization impact the efficiency and accuracy especially for large-scale data. To address above issues, this brief proposes a simple and fast method named efficient discrete clustering with anchor graph (EDCAG) to circumvent postprocessing by binary label optimization. First of all, sparse anchors are adopted to accelerate graph construction and obtain a parameter-free anchor similarity matrix. Subsequently, inspired by intraclass similarity maximization in SC, we design an intraclass similarity maximization model between anchor-sample layer to cope with anchor graph cut problem and exploit more explicit data structures. Meanwhile, a fast coordinate rising (CR) algorithm is employed to alternatively optimize discrete labels of samples and anchors in designed model. Experimental results show excellent rapidity and competitive clustering effect of EDCAG.

关键词： Optimization clustering algorithms Laplace equations Computational modeling Sparse matrices Learning systems Kernel Anchors coordinate rising (CR) algorithm discrete labels intraclass similarity maximization spectral clustering (SC)

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Unsupervised Anomaly Detection and Diagnosis in Power Electronic Networks: Informative Leverage and Multivariate Functional clustering Approaches

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IEEE TRANSACTIONS ON SMART GRID 2024年第2期15卷 2214-2225页

作者： Wu, Shushan Fang, Luyang Zhang, Jinan Sriram, T. N. Coshatt, Stephen J. Zahiri, Feraidoon Mantooth, Alan Ye, Jin Zhong, Wenxuan Ma, Ping Song, WenZhan Univ Georgia Dept Stat Athens GA 30602 USA Univ Georgia Coll Engn Athens GA 30602 USA Robins Air Force Base Warner Robins GA USA Univ Arkansas Dept Elect Engn Fayetteville AR 72701 USA

We propose a novel unsupervised anomaly detection and diagnosis algorithm in power electronic networks. Since most anomaly detection and diagnosis algorithms in the literature are based on supervised methods that can hardly be generalized to broader scenarios, we propose unsupervised algorithms. Our algorithm extracts the Time-Frequency Domain (TFD) features from the three-phase currents and three-phase voltages of the point of coupling (PCC) nodes to detect anomalies and distinguish between different types of anomalies, such as cyber-attacks and physical faults. To detect anomalies through TFD features, we propose a novel Informative Leveraging for Anomaly Detection (ILAD) algorithm. The proposed unsupervised ILAD algorithm automatically extracts noise-reduced anomalous signals, resulting in more accurate anomaly detection results than other score-based methods. To assign anomaly types for anomaly diagnosis, we apply a novel Multivariate Functional Principal Component Analysis (MFPCA) clustering method. Unlike the deep learning methods, the MFPCA clustering method does not require labels for training and provides more accurate results than other deep embedding-based clustering approaches. Furthermore, it is even comparable to supervised algorithms in both offline and online experiments. To the best of our knowledge, the proposed unsupervised framework accomplishing anomaly detection and anomaly diagnosis tasks is the first of its kind in power electronic networks.

关键词： Anomaly detection Feature extraction Power electronics Cyberattack clustering algorithms Training Data models Anomaly detection anomaly diagnosis leverage score multivariate principal component analysis based clustering power electronic networks

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Unified Framework for Faster clustering via Joint Schatten p-Norm Factorization With Optimal Mean

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 3012-3026页

作者： Zhang, Hengmin Zhao, Jiaoyan Zhang, Bob Gong, Chen Qian, Jianjun Yang, Jian Univ Macau Inst Collaborat Innovat Dept Comp & Informat Sci PAMI Res Grp Macau Peoples R China Univ Macau Inst Collaborat Innovat Ctr Artificial Intelligence & Robot Macau Peoples R China Shenzhen Polytech Univ Sch Artificial Intelligence Shenzhen 518055 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Key Lab Intelligent Percept & Syst & High Dimens I PCA LabMinist Educ Nanjing 210094 Peoples R China

To enhance the effectiveness and efficiency of subspace clustering in visual tasks, this work introduces a novel approach that automatically eliminates the optimal mean, which is embedded in the subspace clustering framework of low-rank representation (LRR) methods, along with the computationally factored formulation of Schatten p-norm. By addressing the issues related to meaningful computations involved in some LRR methods and overcoming biased estimation of the low-rank solver, we propose faster nonconvex subspace clustering methods through joint Schatten p-norm factorization with optimal mean (JSpNFOM), forming a unified framework for enhancing performance while reducing time consumption. The proposed approach employs tractable and scalable factor techniques, which effectively address the disadvantages of higher computational complexity, particularly when dealing with large-scale coefficient matrices. The resulting nonconvex minimization problems are reformulated and further iteratively optimized by multivariate weighting algorithms, eliminating the need for singular value decomposition (SVD) computations in the developed iteration procedures. Moreover, each subproblem can be guaranteed to obtain the closed-form solver, respectively. The theoretical analyses of convergence properties and computational complexity further support the applicability of the proposed methods in real-world scenarios. Finally, comprehensive experimental results demonstrate the effectiveness and efficiency of the proposed nonconvex clustering approaches compared to existing state-of-the-art methods on several publicly available databases. The demonstrated improvements highlight the practical significance of our work in subspace clustering tasks for visual data analysis. The source code for the proposed algorithms is publicly accessible at https://***/ZhangHengMin/TRANSUFFC.

关键词： Optimization Computational complexity clustering algorithms Minimization Convergence Computational modeling Learning systems Low-rank representation (LRR) matrix factorization optimal mean Schatten p-norm subspace clustering

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