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Enhance density peak clustering algorithm for anomaly intrusion detection system
2021
Periodicals of Engineering and Natural Sciences (PEN)
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The Anomaly Intrusion Detection System (AIDS) by using original density peak clustering algorithm shows the stable in result to be applied to data-mining module of the intrusion detection system. ...
In this paper proposed new model of Density Peak Clustering algorithm to enhance clustering of intrusion attacks. ...
To handle these limitations of supervised anomaly intrusion detection approaches by using unsupervised learning. Unsupervised anomaly detection approaches do not require labelled training data. ...
doi:10.21533/pen.v9i2.1927
fatcat:ehqq4i6xync4vkcyzjstekmima
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
2019
IEEE Access
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anomaly detection, Internet traffic classification, and quality of service optimization. ...
In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine ...
Another approach [71] discusses the use of grid-based and density-based clustering for anomaly and intrusion detection using unsupervised learning. ...
doi:10.1109/access.2019.2916648
fatcat:xutxh3neynh4bgcsmugxsclkna
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
[article]
2017
arXiv
pre-print
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Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly ...
Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of ...
[64] Density & Grid Based Clustering Applied an unsupervised clustering strategy in density and grid based clustering algorithms to detect anomalies. Chimphlee et al. ...
arXiv:1709.06599v1
fatcat:llcg6gxgpjahha6bkhsitglrsm
Latent Clustering Models for Outlier Identification in Telecom Data
2016
Mobile Information Systems
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Clustering models can help to identify issues by showing patterns in network data, which can quickly catch anomalies and highlight previously unseen outliers. ...
We perform computation on both sample and telecom traffic data to show that the efficiency and robustness of GPLSA make it the superior method to detect outliers and provide results automatically with ...
anomalies using unsupervised algorithms. ...
doi:10.1155/2016/1542540
fatcat:66br4nt7yrgdrkjvjhjpxf755a
Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection
2018
International Journal of Data Science and Analytics
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Anomaly detection problems can be classified into three categories: point anomaly detection, collective anomaly detection and contextual anomaly detection [10] . ...
Accordingly, we propose an unsupervised backward elimination feature selection algorithm BAHSIC-AD, utilizing Hilbert-Schmidt Independence Critirion (HSIC) in identifying the data instances present as ...
In Section 4.3, we present an unsupervised feature selection scheme based on HSIC that is useful for detecting contextual anomalies. ...
doi:10.1007/s41060-018-0161-7
fatcat:xzbxwr3ujfckbnbi2bza2a4mqq
Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering
2020
IEEE Access
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After that, in the abnormal detection step, a density-based clustering algorithm, in which the best clustering parameters are selected through iteration and evaluation, combined with unsupervised clustering ...
Then, the optimal feature set, which reflects the customers' electricity consumption behavior, is obtained by features selected based on the variance and similarity between them. ...
CLUSTERING ALGORITHM Density-based spatial clustering of applications with noise (DBSCAN) is a classical clustering algorithm based on density that is widely used in anomaly detection [49] . ...
doi:10.1109/access.2020.2980079
fatcat:ldz2dbszdnetdgyixuvff7vc5a
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
2020
IEEE Access
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Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. ...
Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies ...
LDBSCAN algorithm [32] , created by the merge of DBSCAN and LOF, is a density-based algorithm for unsupervised anomaly detection problems in spatial databases with noise. ...
doi:10.1109/access.2020.3022366
pmid:34931155
pmcid:PMC8668158
fatcat:m23b5ijr25ahfko2ktxqhx2fsa
Unsupervised Word Segmentation using K Nearest Neighbors
[article]
2022
arXiv
pre-print
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This is in contrast to current methods that use a two-stage approach; first detecting the phonemes in the utterance and then detecting word-boundaries according to statistics calculated on phoneme patterns ...
In this paper, we propose an unsupervised kNN-based approach for word segmentation in speech utterances. ...
For peak detection, we used the standard peak detection function by the scipy package. ...
arXiv:2204.13094v1
fatcat:tqv2n5gubnf3hp4oibgyjrox2u
A Weakly Supervised Gas-Path Anomaly Detection Method for Civil Aero-Engines Based on Mapping Relationship Mining of Gas-Path Parameters and Improved Density Peak Clustering
2021
Sensors
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Finally, an anomaly detection method is deployed based on improved density peak clustering and a weakly supervised clustering parameter adjustment strategy. ...
Therefore, a weakly supervised gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of gas-path parameters and improved density peak clustering is proposed. ...
Finally, anomalies are detected using an improved density peak clustering-based anomaly detection method. ...
doi:10.3390/s21134526
pmid:34282788
fatcat:uiurngphjbajnlrk5zwl44gdbq
Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
2021
Applied Sciences
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However, current periodic maintenance and inspection cannot detect anomalies at an early stage. In addition, constructing a stable and accurate anomaly detection system is a very challenging task. ...
Hence, in this work, we propose a method for detecting anomalies of BOU on metro vehicles using a one-class long short-term memory (LSTM) autoencoder. ...
[18] proposed a clustering-based unsupervised intrusion detection (CBUID) technique. They use a novel incremental clustering algorithm to group datasets into clusters with almost the same radius. ...
doi:10.3390/app11199290
fatcat:p3jhfa7vpjek3ma2xoldjjmxd4
Anomaly Detection Model Over Blockchain Electronic Transactions
2019
2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)
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We used in our proposal two machine learning algorithms, namely the One Class Support Vector Machines (OCSVM) algorithm to detect outliers and the K-Means algorithm in order to group the similar outliers ...
In this work, we propose a new model for anomaly detection over bitcoin electronic transactions. ...
Bogner [3] use an automatic learning approach based on an unsupervised learning algorithm to optimize anomaly detection. ...
doi:10.1109/iwcmc.2019.8766765
dblp:conf/iwcmc/SayadiRC19
fatcat:zmelhhqo7bbztcdgntdezdm6te
Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects
2021
SN Computer Science
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We have also listed the datasets used in various studies to detect group anomalies along with detected anomalies and the various performance measures used to validate the results. ...
Anomaly detection has evolved as a successful research subject in the areas such as bibliometrics, informatics and computer networks including security-based and social networks. ...
Moreover, an unsupervised t-partite graph [61] , graph skeleton-based (gSkeletonClu) clustering algorithm [62] and structural-based algorithm [63] detected clusters based on a node-based feature that ...
doi:10.1007/s42979-021-00603-x
fatcat:oyjzthza7vbhnakpm3t2ko6ctq
Cyber Threat Prediction with Machine Learning
2020
Information & Security An International Journal
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This work would not have been possible without the support of colleagues (past and present) across the NCI Agency who provided the environment and collected the data set used for post-exercise analysis ...
Acknowledgment The authors wish to thank Alberto Domingo and his colleagues at Allied Command Transformation for their support in applying Machine Learning techniques to the Cyber Exercise data set. ...
Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields. 4 For anomaly detection the focus shifts to the data point that cannot be ...
doi:10.11610/isij.4714
fatcat:dhzm63dqpzew5n55oe44lhg2ha
DeCorus: Hierarchical Multivariate Anomaly Detection at Cloud-Scale
[article]
2022
arXiv
pre-print
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We use real-world data sets that consist of 1.5 billion network device syslog messages and hundreds of incident tickets to characterize the performance of DeCorus and compare its ability to detect incidents ...
Multivariate anomaly detection can be used to identify outages within large volumes of telemetry data for computing systems. ...
Another feature of our UVAD is its ability to use metadata about metrics. It accepts value bounds per signal and can be configured to detect only positive or negative anomalies or both. ...
arXiv:2202.06892v1
fatcat:u45nuv2i4nfelbpliyzhc4sy6i
Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques
2021
Computers Materials & Continua
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Towards this end, an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms, fever, and ...
Current supervised Machine Learning (ML) based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography (CT) for disease detection. ...
Hayat Dino Bedru and Shirley S. T. Yeung for their extensive support during the critical review of this article. ...
doi:10.32604/cmc.2021.018098
fatcat:zwbegp7c3fcwvmn5g2foilbbhm
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