[PDF] Top 20 A new statistical approach to network anomaly detection
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A new statistical approach to network anomaly detection
... increasing. To face this issue, the use of Intrusion Detection Systems (IDSs) has emerged as a key element in network ...considering a novel statistical technique for detecting ... Voir le document complet
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Event detection, tracking and visualization in Twitter A mention-anomaly-based approach
... on statistical information derived from Wikipedia and the Microsoft Web N-Gram ...order to avoid merging distinct events that happen ...using a k-nearest neighbor ...again, statistical ... Voir le document complet
18
A Multi-phase Iterative Approach for Anomaly Detection and its Agnostic Evaluation
... able to detect the outliers in the left and right green areas that correspond to the two big ...due to the fact that the iterations stopped one step too ...of statistical metrics in a ... Voir le document complet
13
A Multivariate Extreme Value Theory Approach to Anomaly Clustering and Visualization
... only a few such groups of variables can be exhibited (compared to 2 d − 1) and/or when these groups involve a small number of variables (with respect to ...order to propose a ... Voir le document complet
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How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?
... refer to ( Hodge & Austin , 2004 ; Chandola et ...than a binary label, normal/abnormal. They first compute a scoring function, which is converted to a binary prediction, typically ... Voir le document complet
14
A multivariate extreme value theory approach to anomaly clustering and visualization
... only a few such groups of variables can be exhibited (compared to 2 d − 1) and/or when these groups involve a small number of variables (with respect to ...order to propose a ... Voir le document complet
26
Joint Optimization of Monitor Location and Network Anomaly Detection
... pacities to handle monitoring flows should be considered while selecting monitor ...proposed a multi-round monitoring scheme that reduces the complexity by a factor of the number of ...an ... Voir le document complet
5
GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection
... learning a normalcy model, ii) detecting deviations from the ...applied to cluster the critical points of AIS tracks into so-called Waypoints (WPs): ENs—where vessels enter the Region of Interest (ROI), ... Voir le document complet
14
Tejo: A Supervised Anomaly Detection Scheme for NewSQL Databases
... to theirs. Alerts from Tejo about anomalies in network, memory, disk and CPU of VMs, can contribute to enhance the efficiency of such scheduling ...mechanisms. Anomaly detection with ... Voir le document complet
14
Real-time anomaly detection with in-flight data : streaming anomaly detection with heterogeneous communicating agents
... want to detail is Active Learning. Active Learning is a field of semi-supervised learning where a third-party called an oracle can provide missing labels on ...considered a sub-field of ... Voir le document complet
112
Online and Scalable Unsupervised Network Anomaly Detection Method
... Abstract—Nowadays, network intrusion detectors mainly rely on knowledge databases to detect suspicious ...have to be continuously updated which requires impor- tant human resources and ... Voir le document complet
16
EXAD: A System for Explainable Anomaly Detection on Big Data Traces
... adapting to dynamic environments: Most deep learning algorithms are designed for offline pro- cessing, especially for image processing, hence not good for dynamic ...environments. Network/system ... Voir le document complet
7
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection - Application to Network Intrusion Detection
... [30] to improve the selection of attributes and their split values when constructing the ...(EIF) approach [30], a separation hyperplane with a random orientation is selected instead of ... Voir le document complet
17
Anomaly-based network intrusion detection using machine learning
... on a chip, which are a lot more computationally ...same network, one probe could train a new neural network model and propagate it to the others to continuously ... Voir le document complet
123
Anomaly Detection Based on Indicators Aggregation
... This approach has numerous advantages over using clas- sification or statistical tests ...among a large number of simple features can lead to very high classification accuracy in complex ... Voir le document complet
9
A Statistical Approach to the Matching of Local Features
... which a is also the nearest neighbor of ...but to the best of our knowledge, no generic pro- cedure for the matching of local, SIFT-like features has been proposed beyond the aforementioned thresholds on ... Voir le document complet
14
Anomaly Detection for Bivariate Signals
... describe a complete methodology, based upon clustering, curve realignment and empirical quantiles computation, that allows one to detect abnormal ele- ments in a large sample of time series with ... Voir le document complet
14
Anomaly detection in brain connectivity structure : an application to epilepsy
... When applied to data from epilepsy patients, we observe that using the geometrically defined fine parcelation instead of the anatomically defined coarse parcelation improv[r] ... Voir le document complet
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A neural network approach to selectional preference acquisition
... need to ade- quately deal with the consequences of Zipf’s law: language is inherently sparse, and the majority of language utterances occur very ...As a consequence, models that are based on corpus data ... Voir le document complet
11
A statistical mechanics approach to mixing in stratified fluids
... the statistical theory makes possible predictions for global, cumulative mixing efficiency in decaying turbulence whatever the Richardson number, and whatever the background buoyancy ...predicts a bell ... Voir le document complet
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