Artículos de revistas: "Unsupervised anomaly detection"

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倪, 一鸣 y 松灿陈. "Continual unsupervised anomaly detection". SCIENTIA SINICA Informationis 52, n.º 1 (1 de enero de 2022): 75. http://dx.doi.org/10.1360/ssi-2021-0192.

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Shi, Chengming, Bo Luo, Hongqi Li, Bin Li, Xinyong Mao y Fangyu Peng. "Anomaly Detection via Unsupervised Learning for Tool Breakage Monitoring". International Journal of Machine Learning and Computing 6, n.º 5 (octubre de 2016): 256–59. http://dx.doi.org/10.18178/ijmlc.2016.6.5.607.

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Farzad, Amir y T. Aaron Gulliver. "Unsupervised log message anomaly detection". ICT Express 6, n.º 3 (septiembre de 2020): 229–37. http://dx.doi.org/10.1016/j.icte.2020.06.003.

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Goernitz, N., M. Kloft, K. Rieck y U. Brefeld. "Toward Supervised Anomaly Detection". Journal of Artificial Intelligence Research 46 (20 de febrero de 2013): 235–62. http://dx.doi.org/10.1613/jair.3623.

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Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to ground on the unsupervised learning paradigm and devise a novel algorithm that meets this requirement. Although being intrinsically non-convex, we further show that the optimization problem has a convex equivalent under relatively mild assumptions. Additionally, we propose an active learning strategy to automatically filter candidates for labeling. In an empirical study on network intrusion detection data, we observe that the proposed learning methodology requires much less labeled data than the state-of-the-art, while achieving higher detection accuracies.

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Almalawi, Abdulmohsen, Adil Fahad, Zahir Tari, Asif Irshad Khan, Nouf Alzahrani, Sheikh Tahir Bakhsh, Madini O. Alassafi, Abdulrahman Alshdadi y Sana Qaiyum. "Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data". Electronics 9, n.º 6 (18 de junio de 2020): 1017. http://dx.doi.org/10.3390/electronics9061017.

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Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. Hence, the security of the information systems of critical infrastructures cannot be overstated. The effectiveness of unsupervised anomaly detection approaches is sensitive to parameter choices, especially when the boundaries between normal and abnormal behaviours are not clearly distinguishable. Therefore, the current approach in detecting anomaly for SCADA is based on the assumptions by which anomalies are defined; these assumptions are controlled by a parameter choice. This paper proposes an add-on anomaly threshold technique to identify the observations whose anomaly scores are extreme and significantly deviate from others, and then such observations are assumed to be ”abnormal”. The observations whose anomaly scores are significantly distant from ”abnormal” ones will be assumed as ”normal”. Then, the ensemble-based supervised learning is proposed to find a global and efficient anomaly threshold using the information of both ”normal”/”abnormal” behaviours. The proposed technique can be used for any unsupervised anomaly detection approach to mitigate the sensitivity of such parameters and improve the performance of the SCADA unsupervised anomaly detection approaches. Experimental results confirm that the proposed technique achieved a significant improvement compared to the state-of-the-art of two unsupervised anomaly detection algorithms.

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Tian, Yu, Haihua Liao, Jing Xu, Ya Wang, Shuai Yuan y Naijin Liu. "Unsupervised Spectrum Anomaly Detection Method for Unauthorized Bands". Space: Science & Technology 2022 (21 de febrero de 2022): 1–10. http://dx.doi.org/10.34133/2022/9865016.

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With the rapid development of wireless communication, spectrum plays increasingly important role in both military and civilian fields. Spectrum anomaly detection aims at detecting emerging anomaly signals and spectrum usage behavior in the environment, which is indispensable to secure safety and improve spectrum efficiency. However, spectrum anomaly detection faces many difficulties, especially for unauthorized frequency bands. In unauthorized bands, the composition of spectrum is complex and the anomaly usage patterns are unknown in prior. In this paper, a Variational Autoencoder- (VAE-) based method is proposed for spectrum anomaly detection in unauthorized bands. First of all, we theoretically prove that the anomalies in unauthorized bands will introduce Background Noise Enhancement (BNE) effect and Anomaly Signal Disappearance (ASD) effects after VAE reconstruction. Then, we introduce a novel anomaly metric termed as percentile (PER) score, which focuses on capturing the distribution variation of reconstruction error caused by ASD and BNE. In order to verify the effectiveness of our method, we developed an ISM Anomaly Detection (IAD) dataset. The proposed PER score achieves superior performance against different type of anomalies. For QPSK type anomaly, our method increases the recall rate from 80% to 93% while keeping a false alarm rate of 5%. The proposed method is beneficial to broadband spectrum sensing and massive spectrum data processing. The code will be released at :QXSLAB/vae_ism_ano.git.

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Lok, Lai Kai, Vazeerudeen Abdul Hameed y Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection". Indonesian Journal of Electrical Engineering and Computer Science 27, n.º 2 (1 de agosto de 2022): 1016. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three different datasets are used for model evaluation. The experiment is begun with 5 supervised models and 3 unsupervised models. After performance evaluation, 2 supervised models with the highest F1-Score and one unsupervised model with the best recall value are selected for hybrid model development. The variant 1 hybrid model recorded the best recall value across all the experiments, indicating that it is the best at detecting actual fraud and less likely to miss it compared to other models. The variant 2 hybrid model can improve the precision score significantly compared to the original unsupervised model, indicating that it is better in separating noise from fraud,

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Goldstein, Markus. "Special Issue on Unsupervised Anomaly Detection". Applied Sciences 13, n.º 10 (11 de mayo de 2023): 5916. http://dx.doi.org/10.3390/app13105916.

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Zhou, Wei, Yuan Gao, Jianhang Ji, Shicheng Li y Yugen Yi. "Unsupervised Anomaly Detection for Glaucoma Diagnosis". Wireless Communications and Mobile Computing 2021 (1 de octubre de 2021): 1–14. http://dx.doi.org/10.1155/2021/5978495.

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With the rapid development of high tech, Internet of Things (IoT) and artificial intelligence (AI) achieve a series of achievements in the healthcare industry. Among them, automatic glaucoma diagnosis is one of them. Glaucoma is second leading cause of blindness in the world. Although many automatic glaucoma diagnosis approaches have been proposed, they still face the following two challenges. First, the data acquisition of diseased images is extremely expensive, especially for disease with low occurrence, leading to the class imbalance. Second, large-scale labeled data are hard to obtain in medical image domain. The aforementioned challenges limit the practical application of these approaches in glaucoma diagnosis. To address these disadvantages, this paper proposes an unsupervised anomaly detection framework based on sparse principal component analysis (SPCA) for glaucoma diagnosis. In the proposed approach, we just employ the one-class normal (nonglaucoma) images for training, so the class imbalance problem can be avoided. Then, to distinguish the glaucoma (abnormal) images from the normal images, a feature set consisting of segmentation-based features and image-based features is extracted, which can capture the shape and textural changes. Next, SPCA is adopted to select the effective features from the feature set. Finally, with the usage of the extracted effective features, glaucoma diagnosis can be automatically accomplished via introducing the T 2 statistic and the control limit, overcoming the issue of insufficient labeled samples. Extensive experiments are carried out on the two public databases, and the experimental results verify the effectiveness of the proposed approach.

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Chung, Hwehee, Jongho Park, Jongsoo Keum, Hongdo Ki y Seokho Kang. "Unsupervised Anomaly Detection Using Style Distillation". IEEE Access 8 (2020): 221494–502. http://dx.doi.org/10.1109/access.2020.3043473.

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Vincent, Vercruyssen, Meert Wannes y Davis Jesse. "Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 6054–61. http://dx.doi.org/10.1609/aaai.v34i04.6068.

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Anomaly detection attempts to identify instances that deviate from expected behavior. Constructing performant anomaly detectors on real-world problems often requires some labeled data, which can be difficult and costly to obtain. However, often one considers multiple, related anomaly detection tasks. Therefore, it may be possible to transfer labeled instances from a related anomaly detection task to the problem at hand. This paper proposes a novel transfer learning algorithm for anomaly detection that selects and transfers relevant labeled instances from a source anomaly detection task to a target one. Then, it classifies target instances using a novel semi-supervised nearest-neighbors technique that considers both unlabeled target and transferred, labeled source instances. The algorithm outperforms a multitude of state-of-the-art transfer learning methods and unsupervised anomaly detection methods on a large benchmark. Furthermore, it outperforms its rivals on a real-world task of detecting anomalous water usage in retail stores.

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Amarbayasgalan, Tsatsral, Van Huy Pham, Nipon Theera-Umpon y Keun Ho Ryu. "Unsupervised Anomaly Detection Approach for Time-Series in Multi-Domains Using Deep Reconstruction Error". Symmetry 12, n.º 8 (29 de julio de 2020): 1251. http://dx.doi.org/10.3390/sym12081251.

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Automatic anomaly detection for time-series is critical in a variety of real-world domains such as fraud detection, fault diagnosis, and patient monitoring. Current anomaly detection methods detect the remarkably low proportion of the actual abnormalities correctly. Furthermore, most of the datasets do not provide data labels, and require unsupervised approaches. By focusing on these problems, we propose a novel deep learning-based unsupervised anomaly detection approach (RE-ADTS) for time-series data, which can be applicable to batch and real-time anomaly detections. RE-ADTS consists of two modules including the time-series reconstructor and anomaly detector. The time-series reconstructor module uses the autoregressive (AR) model to find an optimal window width and prepares the subsequences for further analysis according to the width. Then, it uses a deep autoencoder (AE) model to learn the data distribution, which is then used to reconstruct a time-series close to the normal. For anomalies, their reconstruction error (RE) was higher than that of the normal data. As a result of this module, RE and compressed representation of the subsequences were estimated. Later, the anomaly detector module defines the corresponding time-series as normal or an anomaly using a RE based anomaly threshold. For batch anomaly detection, the combination of the density-based clustering technique and anomaly threshold is employed. In the case of real-time anomaly detection, only the anomaly threshold is used without the clustering process. We conducted two types of experiments on a total of 52 publicly available time-series benchmark datasets for the batch and real-time anomaly detections. Experimental results show that the proposed RE-ADTS outperformed the state-of-the-art publicly available anomaly detection methods in most cases.

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Guo, Jiahao, Xiaohuo Yu y Lu Wang. "Unsupervised Anomaly Detection and Segmentation on Dirty Datasets". Future Internet 14, n.º 3 (13 de marzo de 2022): 86. http://dx.doi.org/10.3390/fi14030086.

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Industrial quality control is an important task. Most of the existing vision-based unsupervised industrial anomaly detection and segmentation methods require that the training set only consists of normal samples, which is difficult to ensure in practice. This paper proposes an unsupervised framework to solve the industrial anomaly detection and segmentation problem when the training set contains anomaly samples. Our framework uses a model pretrained on ImageNet as a feature extractor to extract patch-level features. After that, we propose a trimming method to estimate a robust Gaussian distribution based on the patch features at each position. Then, with an iterative filtering process, we can iteratively filter out the anomaly samples in the training set and re-estimate the Gaussian distribution at each position. In the prediction phase, the Mahalanobis distance between a patch feature vector and the center of the Gaussian distribution at the corresponding position is used as the anomaly score of this patch. The subsequent anomaly region segmentation is performed based on the patch anomaly score. We tested the proposed method on three datasets containing the anomaly samples and obtained state-of-the-art performance.

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Wen-Jen Ho, Wen-Jen Ho, Hsin-Yuan Hsieh Wen-Jen Ho y Chia-Wei Tsai Hsin-Yuan Hsieh. "Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning". 網際網路技術學刊 25, n.º 3 (mayo de 2024): 455–63. http://dx.doi.org/10.53106/160792642024052503011.

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In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation.

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Song, Yide. "Weakly-Supervised and Unsupervised Video Anomaly Detection". Highlights in Science, Engineering and Technology 12 (26 de agosto de 2022): 160–70. http://dx.doi.org/10.54097/hset.v12i.1444.

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As surveillance technology is continuously improving, an ever-increasing number of cameras are being deployed everywhere. Relying on manual detection of anomalies through cameras may be unreliable and untimely. Therefore, the application of deep learning in video anomaly detection is being extensively studied. Anomaly Detection (AD) refers to identifying events that deviate from the desired actions. This article discusses representative unsupervised and weakly-supervised learning methods applied to various data types. In these machine learning methods, Generative Adversarial Network, Auto Encoder, Recurrent Neural Network, etc. are broadly adopted for AD. Some renowned and new datasets are reviewed. Furthermore, we also proposed several future directions of research in video anomaly detection.

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ZHOU, JUNLIN, ALEKSANDAR LAZAREVIC, KUO-WEI HSU, JAIDEEP SRIVASTAVA, YAN FU y YUE WU. "UNSUPERVISED LEARNING BASED DISTRIBUTED DETECTION OF GLOBAL ANOMALIES". International Journal of Information Technology & Decision Making 09, n.º 06 (noviembre de 2010): 935–57. http://dx.doi.org/10.1142/s0219622010004172.

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Anomaly detection has recently become an important problem in many industrial and financial applications. Very often, the databases from which anomalies have to be found are located at multiple local sites and cannot be merged due to privacy reasons or communication overhead. In this paper, a novel general framework for distributed anomaly detection is proposed. The proposed method consists of three steps: (i) building local models for distributed data sources with unsupervised anomaly detection methods and computing quality measure of local models; (ii) transforming local unsupervised local models into sharing models; and (iii) reusing sharing models for new data and combining their results by considering both quality and diversity of them to detect anomalies in a global view. In experiments performed on synthetic and real-life large data set, the proposed distributed anomaly detection method achieved prediction performance comparable or even slightly better than the global anomaly detection algorithm applied on the data set obtained when all distributed data set were merged.

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Zhang, Shen Qi, Wei Yuan, Ran Yi y Li Chen. "DC operating circuit anomaly detection based on node voltage unsupervised time series". Journal of Physics: Conference Series 2474, n.º 1 (1 de abril de 2023): 012030. http://dx.doi.org/10.1088/1742-6596/2474/1/012030.

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Abstract With the increase in power system load, the number of switch cabinets increases rapidly, and the accidents caused by DC operating circuits become more and more obvious. Abnormal detection has become an important part of the DC operating circuit. Abnormal data, mainly voltage measurement, often contain important information. Finding and analyzing these abnormal data can give early warning for circuit failure or failure. However, abnormal data is often difficult to obtain and lacks marks, that is, the problem of sample imbalance and unsupervised problems, resulting in abnormal detection methods that can not meet the increasing accuracy and efficiency requirements. Generative adversarial network (GAN) is an unsupervised deep learning model, which has been successfully applied to unsupervised time series anomaly detection. However, GAN has technical difficulties in the anomaly detection process, and there is still room for optimization in accuracy. In this paper, the unsupervised time series anomaly detection method based on GAN is studied, and an anomaly detection method of a DC operating circuit combined with LSTM and GAN is designed.

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Yang, Zhengqiang, Junwei Tian y Ning Li. "Flow Graph Anomaly Detection Based on Unsupervised Learning". Mobile Information Systems 2022 (31 de marzo de 2022): 1–9. http://dx.doi.org/10.1155/2022/4194714.

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In this paper, a flow graph anomaly detection framework based on unsupervised learning is proposed. Compared with traditional anomaly detection, graph anomaly detection faces some problems. Firstly, the training of a reasonable network embedding is challenging. Secondly, the information data in the real world is often dynamically changing. Thirdly, due to the lack of sufficient training labeled data in most cases, anomaly detection models can only use unsupervised learning methods. In order to resolve these problems, three modules in the framework are proposed in this paper: preprocessor, controller, and optimizer. Additionally, a reasonable negative sampling strategy is applied to generate negative samples to deal with the lack of labeled data. Finally, experiments on real-world data sets are conducted, and the experimental results show that the accuracy of the proposed method reaches 87.6%.

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Liu, Jiaqi, Kai Wu, Qiang Nie, Ying Chen, Bin-Bin Gao, Yong Liu, Jinbao Wang, Chengjie Wang y Feng Zheng. "Unsupervised Continual Anomaly Detection with Contrastively-Learned Prompt". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 4 (24 de marzo de 2024): 3639–47. http://dx.doi.org/10.1609/aaai.v38i4.28153.

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Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible. However, continual learning methods primarily rely on supervised annotations, while the application in UAD is limited due to the absence of supervision. Current UAD methods train separate models for different classes sequentially, leading to catastrophic forgetting and a heavy computational burden. To address this issue, we introduce a novel Unsupervised Continual Anomaly Detection framework called UCAD, which equips the UAD with continual learning capability through contrastively-learned prompts. In the proposed UCAD, we design a Continual Prompting Module (CPM) by utilizing a concise key-prompt-knowledge memory bank to guide task-invariant 'anomaly' model predictions using task-specific 'normal' knowledge. Moreover, Structure-based Contrastive Learning (SCL) is designed with the Segment Anything Model (SAM) to improve prompt learning and anomaly segmentation results. Specifically, by treating SAM's masks as structure, we draw features within the same mask closer and push others apart for general feature representations. We conduct comprehensive experiments and set the benchmark on unsupervised continual anomaly detection and segmentation, demonstrating that our method is significantly better than anomaly detection methods, even with rehearsal training. The code will be available at https://github.com/shirowalker/UCAD.

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Nakao, Takahiro, Shouhei Hanaoka, Yukihiro Nomura, Masaki Murata, Tomomi Takenaga, Soichiro Miki, Takeyuki Watadani, Takeharu Yoshikawa, Naoto Hayashi y Osamu Abe. "Unsupervised Deep Anomaly Detection in Chest Radiographs". Journal of Digital Imaging 34, n.º 2 (8 de febrero de 2021): 418–27. http://dx.doi.org/10.1007/s10278-020-00413-2.

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AbstractThe purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial network (α-GAN) framework, which is a combination of a GAN and a variational autoencoder, as a DNN model. A total of 29,684 frontal chest radiographs from the Radiological Society of North America Pneumonia Detection Challenge dataset were used for this study (16,880 male and 12,804 female patients; average age, 47.0 years). All these images were labeled as “Normal,” “No Opacity/Not Normal,” or “Opacity” by board-certified radiologists. About 70% (6,853/9,790) of the Normal images were randomly sampled as the training dataset, and the rest were randomly split into the validation and test datasets in a ratio of 1:2 (7,610 and 15,221). Our anomaly detection system could correctly visualize various lesions including a lung mass, cardiomegaly, pleural effusion, bilateral hilar lymphadenopathy, and even dextrocardia. Our system detected the abnormal images with an area under the receiver operating characteristic curve (AUROC) of 0.752. The AUROCs for the abnormal labels Opacity and No Opacity/Not Normal were 0.838 and 0.704, respectively. Our DNN-based unsupervised anomaly detection method could successfully detect various diseases or anomalies in chest radiographs by training with only the normal images.

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Liu, Boyang, Pang-Ning Tan y Jiayu Zhou. "Unsupervised Anomaly Detection by Robust Density Estimation". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 4 (28 de junio de 2022): 4101–8. http://dx.doi.org/10.1609/aaai.v36i4.20328.

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Density estimation is a widely used method to perform unsupervised anomaly detection. By learning the density function, data points with relatively low densities are classified as anomalies. Unfortunately, the presence of anomalies in training data may significantly impact the density estimation process, thereby imposing significant challenges to the use of more sophisticated density estimation methods such as those based on deep neural networks. In this work, we propose RobustRealNVP, a deep density estimation framework that enhances the robustness of flow-based density estimation methods, enabling their application to unsupervised anomaly detection. RobustRealNVP differs from existing flow-based models from two perspectives. First, RobustRealNVP discards data points with low estimated densities during optimization to prevent them from corrupting the density estimation process. Furthermore, it imposes Lipschitz regularization to the flow-based model to enforce smoothness in the estimated density function. We demonstrate the robustness of our algorithm against anomalies in training data from both theoretical and empirical perspectives. The results show that our algorithm achieves competitive results as compared to state-of-the-art unsupervised anomaly detection methods.

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López-Vizcaíno, Manuel, Carlos Dafonte, Francisco Nóvoa, Daniel Garabato y M. Álvarez. "Network Data Unsupervised Clustering to Anomaly Detection". Proceedings 2, n.º 18 (17 de septiembre de 2018): 1173. http://dx.doi.org/10.3390/proceedings2181173.

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In these days, organizations rely on the availability and security of their communication networks to perform daily operations. As a result, network data must be analyzed in order to provide an adequate level of security and to detect anomalies or malfunctions in the systems. Due to the increase of devices connected to these networks, the complexity to analyze data related to its communications also grows. We propose a method, based on Self-Organized Maps, which combine numerical and categorical features, to ease communication network data analysis. Also, we have explored the possibility of using different sources of data.

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Li, H., A. Achim y D. Bull. "Unsupervised video anomaly detection using feature clustering". IET Signal Processing 6, n.º 5 (2012): 521. http://dx.doi.org/10.1049/iet-spr.2011.0074.

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Khan, Samir, Chun Fui Liew, Takehisa Yairi y Richard McWilliam. "Unsupervised anomaly detection in unmanned aerial vehicles". Applied Soft Computing 83 (octubre de 2019): 105650. http://dx.doi.org/10.1016/j.asoc.2019.105650.

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Olson, C. C., K. P. Judd y J. M. Nichols. "Manifold learning techniques for unsupervised anomaly detection". Expert Systems with Applications 91 (enero de 2018): 374–85. http://dx.doi.org/10.1016/j.eswa.2017.08.005.

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Ergen, Tolga y Suleyman Serdar Kozat. "Unsupervised Anomaly Detection With LSTM Neural Networks". IEEE Transactions on Neural Networks and Learning Systems 31, n.º 8 (agosto de 2020): 3127–41. http://dx.doi.org/10.1109/tnnls.2019.2935975.

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Wang, Pei, Wei Zhai y Yang Cao. "Robustness Benchmark for Unsupervised Anomaly Detection Models". JUSTC 53 (2023): 1. http://dx.doi.org/10.52396/justc-2022-0165.

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Due to the complexity and diversity of production environments, it is essential to understand the robustness of unsupervised anomaly detection models to common corruptions. To explore this issue systematically, we propose a dataset named MVTec-C to evaluate the robustness of unsupervised anomaly detection models. Based on this dataset, we explore the robustness of approaches in five paradigms, including reconstruction-based, representation similarity-based, normalizing flow-based, self-supervised representation learning-based, and knowledge distillation-based. Further, we explore the impact of different modules in two optimal methods on both robustness and accuracy, including multi-scale features, neighborhood size, sampling ratio of PatchCore and multi-scale features, MMF module, OCE module, multi-scale distillation of Reverse Distillation. Finally, we propose a Feature Alignment Module (FAM) to reduce the feature drift caused by corruptions and combine PatchCore and FAM to obtain a model with both high performance and high accuracy. We hope this work will serve as an evaluation method and provide experience in building robust anomaly detection models in the future.

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Xu, Haohao, Shuchang Xu y Wenzhen Yang. "Unsupervised industrial anomaly detection with diffusion models". Journal of Visual Communication and Image Representation 97 (diciembre de 2023): 103983. http://dx.doi.org/10.1016/j.jvcir.2023.103983.

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Bulut, Okan, Guher Gorgun y Surina He. "Unsupervised Anomaly Detection in Sequential Process Data". Zeitschrift für Psychologie 232, n.º 2 (abril de 2024): 74–94. http://dx.doi.org/10.1027/2151-2604/a000558.

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Abstract: In this study, we present three types of unsupervised anomaly detection to identify anomalous test-takers based on their action sequences in problem-solving tasks. The first method relies on the use of the Isolation Forest algorithm to detect anomalous test-takers based on raw action sequences extracted from process data. The second method transforms raw action sequences into contextual embeddings using the Bidirectional Encoder Representations from Transformers (BERT) model and then applies the Isolation Forest algorithm to detect anomalous test-takers. The third method follows the same procedure as the second method, but it includes an intermediary step of dimensionality reduction for the contextual embeddings before applying the Isolation Forest algorithm for detecting anomalous cases. To compare the outcomes of the three methods, we analyze the log files from test-takers in the US sample ( n = 2,021) who completed the problem-solving in technology-rich environments (PSTRE) section of the Programme for the International Assessment of Adult Competencies (PIAAC) 2012 assessment. The results indicated that different groups of test-takers were flagged as anomalous depending on the representation (raw action sequences vs. contextual embeddings) and dimensionality of action sequences. Also, when the contextual embeddings were used, a larger number of test-takers were flagged by the Isolation Forest algorithm, indicating the sensitivity of this algorithm to the dimensionality of input data.

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Dai, Songmin, Yifan Wu, Xiaoqiang Li y Xiangyang Xue. "Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection". Proceedings of the AAAI Conference on Artificial Intelligence 38, n.º 2 (24 de marzo de 2024): 1454–62. http://dx.doi.org/10.1609/aaai.v38i2.27910.

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Recent unsupervised anomaly detection methods often rely on feature extractors pretrained with auxiliary datasets or on well-crafted anomaly-simulated samples. However, this might limit their adaptability to an increasing set of anomaly detection tasks due to the priors in the selection of auxiliary datasets or the strategy of anomaly simulation. To tackle this challenge, we first introduce a prior-less anomaly generation paradigm and subsequently develop an innovative unsupervised anomaly detection framework named GRAD, grounded in this paradigm. GRAD comprises three essential components: (1) a diffusion model (PatchDiff) to generate contrastive patterns by preserving the local structures while disregarding the global structures present in normal images, (2) a self-supervised reweighting mechanism to handle the challenge of long-tailed and unlabeled contrastive patterns generated by PatchDiff, and (3) a lightweight patch-level detector to efficiently distinguish the normal patterns and reweighted contrastive patterns. The generation results of PatchDiff effectively expose various types of anomaly patterns, e.g. structural and logical anomaly patterns. In addition, extensive experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation and demonstrate that GRAD achieves competitive anomaly detection accuracy and superior inference speed.

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Hu, Jingtao, En Zhu, Siqi Wang, Xinwang Liu, Xifeng Guo y Jianping Yin. "An Efficient and Robust Unsupervised Anomaly Detection Method Using Ensemble Random Projection in Surveillance Videos". Sensors 19, n.º 19 (24 de septiembre de 2019): 4145. http://dx.doi.org/10.3390/s19194145.

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Video anomaly detection is widely applied in modern society, which is achieved by sensors such as surveillance cameras. This paper learns anomalies by exploiting videos under the fully unsupervised setting. To avoid massive computation caused by back-prorogation in existing methods, we propose a novel efficient three-stage unsupervised anomaly detection method. In the first stage, we adopt random projection instead of autoencoder or its variants in previous works. Then we formulate the optimization goal as a least-square regression problem which has a closed-form solution, leading to less computational cost. The discriminative reconstruction losses of normal and abnormal events encourage us to roughly estimate normality that can be further sifted in the second stage with one-class support vector machine. In the third stage, to eliminate the instability caused by random parameter initializations, ensemble technology is performed to combine multiple anomaly detectors’ scores. To the best of our knowledge, it is the first time that unsupervised ensemble technology is introduced to video anomaly detection research. As demonstrated by the experimental results on several video anomaly detection benchmark datasets, our algorithm robustly surpasses the recent unsupervised methods and performs even better than some supervised approaches. In addition, we achieve comparable performance contrast with the state-of-the-art unsupervised method with much less running time, indicating the effectiveness, efficiency, and robustness of our proposed approach.

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Hang, Feilu, Wei Guo, Hexiong Chen, Linjiang Xie, Xiaoyu Bai y Yao Liu. "Network Intrusion Anomaly Detection Model Based on Multiclassifier Fusion Technology". Mobile Information Systems 2023 (8 de abril de 2023): 1–11. http://dx.doi.org/10.1155/2023/1594622.

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With the increasing development of the industrial Internet, network security has attracted more and more attention. Among the numerous network security technologies, anomaly detection technology based on network traffic has become an important research field. At present, a large number of methods for network anomaly detection have been proposed. Most of the better performance detection methods are based on supervised machine learning algorithms, which require a large number of labelled data for model training. However, in a real network, it is impossible to manually filter and label large-scale traffic data. Network administrators can only use unsupervised machine learning algorithms for actual detection, and the detection effects are much worse than supervised learning algorithms. To improve the accuracy of the unsupervised detection methods, this study proposes a network anomaly detection model based on multiple classifier fusion technology, which applies different fusion techniques (such as Majority Vote, Weighted Majority Vote, and Naive Bayes) to fuse the detection results of the five best performing unsupervised anomaly detection algorithms. Comparative experiments are carried out on three public datasets. Experimental results show that, in terms of RECALL and AUC score, the fusion model proposed in this study achieves better performance than the five separate anomaly detection baseline algorithms, and it has better robustness and stability, which can be effectively applied to a wide range of network anomaly detection scenarios.

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Shao, Yingzhao, Yunsong Li, Li Li, Yuanle Wang, Yuchen Yang, Yueli Ding, Mingming Zhang, Yang Liu y Xiangqiang Gao. "RANet: Relationship Attention for Hyperspectral Anomaly Detection". Remote Sensing 15, n.º 23 (30 de noviembre de 2023): 5570. http://dx.doi.org/10.3390/rs15235570.

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Hyperspectral anomaly detection (HAD) is of great interest for unknown exploration. Existing methods only focus on local similarity, which may show limitations in detection performance. To cope with this problem, we propose a relationship attention-guided unsupervised learning with convolutional autoencoders (CAEs) for HAD, called RANet. First, instead of only focusing on the local similarity, RANet, for the first time, pays attention to topological similarity by leveraging the graph attention network (GAT) to capture deep topological relationships embedded in a customized incidence matrix from absolutely unlabeled data mixed with anomalies. Notably, the attention intensity of GAT is self-adaptively controlled by adjacency reconstruction ability, which can effectively reduce human intervention. Next, we adopt an unsupervised CAE to jointly learn with the topological relationship attention to achieve satisfactory model performance. Finally, on the basis of background reconstruction, we detect anomalies by the reconstruction error. Extensive experiments on hyperspectral images (HSIs) demonstrate that our proposed RANet outperforms existing fully unsupervised methods.

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Rashid, A. N. M. Bazlur, Mohiuddin Ahmed, Leslie F. Sikos y Paul Haskell-Dowland. "Anomaly Detection in Cybersecurity Datasets via Cooperative Co-evolution-based Feature Selection". ACM Transactions on Management Information Systems 13, n.º 3 (30 de septiembre de 2022): 1–39. http://dx.doi.org/10.1145/3495165.

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Anomaly detection from Big Cybersecurity Datasets is very important; however, this is a very challenging and computationally expensive task. Feature selection (FS) is an approach to remove irrelevant and redundant features and select a subset of features, which can improve the machine learning algorithms’ performance. In fact, FS is an effective preprocessing step of anomaly detection techniques. This article’s main objective is to improve and quantify the accuracy and scalability of both supervised and unsupervised anomaly detection techniques. In this effort, a novel anomaly detection approach using FS, called Anomaly Detection Using Feature Selection (ADUFS), has been introduced. Experimental analysis was performed on five different benchmark cybersecurity datasets with and without feature selection and the performance of both supervised and unsupervised anomaly detection techniques were investigated. The experimental results indicate that instead of using the original dataset, a dataset with a reduced number of features yields better performance in terms of true positive rate (TPR) and false positive rate (FPR) than the existing techniques for anomaly detection. For example, with FS, a supervised anomaly detection technique, multilayer perception increased the TPR by over 200% and decreased the FPR by about 97% for the KDD99 dataset. Similarly, with FS, an unsupervised anomaly detection technique, local outlier factor increased the TPR by more than 40% and decreased the FPR by 15% and 36% for Windows 7 and NSL-KDD datasets, respectively. In addition, all anomaly detection techniques require less computational time when using datasets with a suitable subset of features rather than entire datasets. Furthermore, the performance results have been compared with six other state-of-the-art techniques based on a decision tree (J48).

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Klarák, Jaromír, Robert Andok, Peter Malík, Ivan Kuric, Mário Ritomský, Ivana Klačková y Hung-Yin Tsai. "From Anomaly Detection to Defect Classification". Sensors 24, n.º 2 (10 de enero de 2024): 429. http://dx.doi.org/10.3390/s24020429.

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This paper proposes a new approach to defect detection system design focused on exact damaged areas demonstrated through visual data containing gear wheel images. The main advantage of the system is the capability to detect a wide range of patterns of defects occurring in datasets. The methodology is built on three processes that combine different approaches from unsupervised and supervised methods. The first step is a search for anomalies, which is performed by defining the correct areas on the controlled object by using the autoencoder approach. As a result, the differences between the original and autoencoder-generated images are obtained. These are divided into clusters using the clustering method (DBSCAN). Based on the clusters, the regions of interest are subsequently defined and classified using the pre-trained Xception network classifier. The main result is a system capable of focusing on exact defect areas using the sequence of unsupervised learning (autoencoder)–unsupervised learning (clustering)–supervised learning (classification) methods (U2S-CNN). The outcome with tested samples was 177 detected regions and 205 occurring damaged areas. There were 108 regions detected correctly, and 69 regions were labeled incorrectly. This paper describes a proof of concept for defect detection by highlighting exact defect areas. It can be thus an alternative to using detectors such as YOLO methods, reconstructors, autoencoders, transformers, etc.

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Qu, YanZe, HaiLong Ma y YiMing Jiang. "CRND: An Unsupervised Learning Method to Detect Network Anomaly". Security and Communication Networks 2022 (28 de octubre de 2022): 1–9. http://dx.doi.org/10.1155/2022/9509417.

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Network anomaly detection system (NADS) is one of the most important methods to maintain network system security. At present, network anomaly detection models based on deep learning have become a research hotspot in the area because of their advantage in processing high-dimensional data and excellent performance on detecting anomaly. However, most of the related research studies are based on supervised learning, which has strict requirements for dataset such as labels with high accuracy. However, there are some difficulties in obtaining a large amount of data with complete label message, thus seriously hindering the development and deployment of NADS based on DL. In this paper, we propose an unsupervised learning method to detect network anomaly, contrastive representation for network data (CRND). Based on contrastive learning, without label message, a qualified model is trained, providing more possibilities for the field. On CICIDS2018, the evaluation experiment proves that CRND can achieve 96.13% accuracy with only 200 items, and its F1-score reaches 0.96, which is far higher than that of other existing unsupervised learning methods. As fine-tuning is carried out, F1-score can reach a convergence level of 0.99, and the detection performance is the same as that of the detection model based on supervised learning.

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Liu, Wenqiang, Li Yan, Ningning Ma, Gaozhou Wang, Xiaolong Ma, Peishun Liu y Ruichun Tang. "Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data". Applied Sciences 14, n.º 2 (16 de enero de 2024): 774. http://dx.doi.org/10.3390/app14020774.

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With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model—the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost.

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Katser, Iurii, Viacheslav Kozitsin, Victor Lobachev y Ivan Maksimov. "Unsupervised Offline Changepoint Detection Ensembles". Applied Sciences 11, n.º 9 (9 de mayo de 2021): 4280. http://dx.doi.org/10.3390/app11094280.

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Offline changepoint detection (CPD) algorithms are used for signal segmentation in an optimal way. Generally, these algorithms are based on the assumption that signal’s changed statistical properties are known, and the appropriate models (metrics, cost functions) for changepoint detection are used. Otherwise, the process of proper model selection can become laborious and time-consuming with uncertain results. Although an ensemble approach is well known for increasing the robustness of the individual algorithms and dealing with mentioned challenges, it is weakly formalized and much less highlighted for CPD problems than for outlier detection or classification problems. This paper proposes an unsupervised CPD ensemble (CPDE) procedure with the pseudocode of the particular proposed ensemble algorithms and the link to their Python realization. The approach’s novelty is in aggregating several cost functions before the changepoint search procedure running during the offline analysis. The numerical experiment showed that the proposed CPDE outperforms non-ensemble CPD procedures. Additionally, we focused on analyzing common CPD algorithms, scaling, and aggregation functions, comparing them during the numerical experiment. The results were obtained on the two anomaly benchmarks that contain industrial faults and failures—Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB). One of the possible applications of our research is the estimation of the failure time for fault identification and isolation problems of the technical diagnostics.

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Zoppi, Tommaso, Andrea Ceccarelli, Tommaso Capecchi y Andrea Bondavalli. "Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape". ACM/IMS Transactions on Data Science 2, n.º 2 (2 de abril de 2021): 1–26. http://dx.doi.org/10.1145/3441140.

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Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a given system. It is acknowledged as a reliable answer to the identification of zero-day attacks to such extent, several ML algorithms that suit for binary classification have been proposed throughout years. However, the experimental comparison of a wide pool of unsupervised algorithms for anomaly-based intrusion detection against a comprehensive set of attacks datasets was not investigated yet. To fill such gap, we exercise 17 unsupervised anomaly detection algorithms on 11 attack datasets. Results allow elaborating on a wide range of arguments, from the behavior of the individual algorithm to the suitability of the datasets to anomaly detection. We conclude that algorithms as Isolation Forests, One-Class Support Vector Machines, and Self-Organizing Maps are more effective than their counterparts for intrusion detection, while clustering algorithms represent a good alternative due to their low computational complexity. Further, we detail how attacks with unstable, distributed, or non-repeatable behavior such as Fuzzing, Worms, and Botnets are more difficult to detect. Ultimately, we digress on capabilities of algorithms in detecting anomalies generated by a wide pool of unknown attacks, showing that achieved metric scores do not vary with respect to identifying single attacks.

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Du, Yan, Yuanyuan Huang, Guogen Wan y Peilin He. "Deep Learning-Based Cyber–Physical Feature Fusion for Anomaly Detection in Industrial Control Systems". Mathematics 10, n.º 22 (20 de noviembre de 2022): 4373. http://dx.doi.org/10.3390/math10224373.

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In this paper, we propose an unsupervised anomaly detection method based on the Autoencoder with Long Short-Term Memory (LSTM-Autoencoder) network and Generative Adversarial Network (GAN) to detect anomalies in industrial control system (ICS) using cyber–physical fusion features. This method improves the recall of anomaly detection and overcomes the challenges of unbalanced datasets and insufficient labeled samples in ICS. As a first step, additional network features are extracted and fused with physical features to create a cyber–physical dataset. Following this, the model is trained using normal data to ensure that it can properly reconstruct the normal data. In the testing phase, samples with unknown labels are used as inputs to the model. The model will output an anomaly score for each sample, and whether a sample is anomalous depends on whether the anomaly score exceeds the threshold. Whether using supervised or unsupervised algorithms, experimentation has shown that (1) cyber–physical fusion features can significantly improve the performance of anomaly detection algorithms; (2) the proposed method outperforms several other unsupervised anomaly detection methods in terms of accuracy, recall, and F1 score; (3) the proposed method can detect the majority of anomalous events with a low false negative rate.

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Apostol, Ioana, Marius Preda, Constantin Nila y Ion Bica. "IoT Botnet Anomaly Detection Using Unsupervised Deep Learning". Electronics 10, n.º 16 (4 de agosto de 2021): 1876. http://dx.doi.org/10.3390/electronics10161876.

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The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

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Arjunan, Tamilselvan. "A Comparative Study of Deep Neural Networks and Support Vector Machines for Unsupervised Anomaly Detection in Cloud Computing Environments". International Journal for Research in Applied Science and Engineering Technology 12, n.º 2 (29 de febrero de 2024): 983–90. http://dx.doi.org/10.22214/ijraset.2024.58496.

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Abstract: Cloud computing is now ubiquitous and provides convenient access to computing resources on demand. Cloud environments are complex and prone to faults, which can have a negative impact on service quality. Cloud providers must be able to detect issues in a proactive manner using unsupervised anomaly detection. This does not require labeled data. This paper presents a comparison of deep neural networks and support vector machine (SVMs), both used for unsupervised anomaly identification in cloud environments. On benchmark datasets provided by cloud providers, we evaluate the performance Autoencoders with LSTM models, One Class SVMs, and Isolation Forests. Our results show that shallow Autoencoders do not capture workload patterns well, but LSTMs or Convolutional Autoencoders can. SVMs are as good or better than Autoencoders. One-Class SVMs show robust performance in all workloads. Isolation Forests perform poorly on cloud data that is seasonal. One-Class SVMs are the most accurate and low latency option for anomaly detection. Our findings offer cloud providers guidance on how to select suitable unsupervised models based upon their performance, interpretability, and computational overhead. The results and comparative methodology will be used to inform future research into adapting unsupervised-learning for cloud anomaly detection.

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Le, Duc C. y Nur Zincir-Heywood. "Anomaly Detection for Insider Threats Using Unsupervised Ensembles". IEEE Transactions on Network and Service Management 18, n.º 2 (junio de 2021): 1152–64. http://dx.doi.org/10.1109/tnsm.2021.3071928.

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Spiekermann, Daniel y Jörg Keller. "Unsupervised packet-based anomaly detection in virtual networks". Computer Networks 192 (junio de 2021): 108017. http://dx.doi.org/10.1016/j.comnet.2021.108017.

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Anitha Kumari, K., Avinash Sharma, R. Barani Priyanga y A. Kevin Paul. "ENERGY DATA ANOMALY DETECTION USING UNSUPERVISED LEARNING TECHNIQUES". Advances in Mathematics: Scientific Journal 9, n.º 9 (25 de agosto de 2020): 6687–98. http://dx.doi.org/10.37418/amsj.9.9.26.

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Jeong, Woodon, Mohammed S. Almubarak y Constantinos Tsingas. "Seismic erratic noise attenuation using unsupervised anomaly detection". Geophysical Prospecting 69, n.º 7 (11 de junio de 2021): 1473–86. http://dx.doi.org/10.1111/1365-2478.13123.

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Wang, Jin, Changqing Zhao, Shiming He, Yu Gu, Osama Alfarraj y Ahed Abugabah. "LogUAD: Log Unsupervised Anomaly Detection Based on Word2Vec". Computer Systems Science and Engineering 41, n.º 3 (2022): 1207–22. http://dx.doi.org/10.32604/csse.2022.022365.

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Kandanaarachchi, Sevvandi. "Unsupervised anomaly detection ensembles using item response theory". Information Sciences 587 (marzo de 2022): 142–63. http://dx.doi.org/10.1016/j.ins.2021.12.042.

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Wang, Zhipeng, Chunping Hou, Bangbang Ge, Yang Liu, Zhicheng Dong y Zhiqiang Wu. "Unsupervised anomaly detection via dual transformation‐aware embeddings". IET Image Processing 16, n.º 6 (8 de febrero de 2022): 1657–68. http://dx.doi.org/10.1049/ipr2.12438.

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Son, Jonghwan, Chayoung Kim y Minjoong Jeong. "Unsupervised Learning for Anomaly Detection of Electric Motors". International Journal of Precision Engineering and Manufacturing 23, n.º 4 (10 de marzo de 2022): 421–27. http://dx.doi.org/10.1007/s12541-022-00635-0.

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