A Structured Sparse Subspace Learning Algorithm for Anomaly Detection in UAV Flight Data

2018 ◽  
Vol 67 (1) ◽  
pp. 90-100 ◽  
Author(s):  
Yongfu He ◽  
Yu Peng ◽  
Shaojun Wang ◽  
Datong Liu ◽  
Philip H. W. Leong
Keyword(s):  
Anomaly Detection ◽  
Learning Algorithm ◽  
Subspace Learning ◽  
Flight Data

QLLog: A log anomaly detection method based on Q-learning algorithm

2021 ◽  
Vol 58 (3) ◽  
pp. 102540
Author(s):  
Xiaoyu Duan ◽  
Shi Ying ◽  
Wanli Yuan ◽  
Hailong Cheng ◽  
Xiang Yin
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Learning Algorithm ◽  
Q Learning

scholarly journals Multiview Discriminative Geometry Preserving Projection for Image Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ziqiang Wang ◽  
Xia Sun ◽  
Lijun Sun ◽  
Yuchun Huang
Keyword(s):  
Image Classification ◽  
Learning Algorithm ◽  
Subspace Learning ◽  
Visual Features ◽  
Expression Recognition ◽  
Single View ◽  
Discrimination Information ◽  
New Feature ◽  
Low Dimensional ◽  
Conventional Solution

In many image classification applications, it is common to extract multiple visual features from different views to describe an image. Since different visual features have their own specific statistical properties and discriminative powers for image classification, the conventional solution for multiple view data is to concatenate these feature vectors as a new feature vector. However, this simple concatenation strategy not only ignores the complementary nature of different views, but also ends up with “curse of dimensionality.” To address this problem, we propose a novel multiview subspace learning algorithm in this paper, named multiview discriminative geometry preserving projection (MDGPP) for feature extraction and classification. MDGPP can not only preserve the intraclass geometry and interclass discrimination information under a single view, but also explore the complementary property of different views to obtain a low-dimensional optimal consensus embedding by using an alternating-optimization-based iterative algorithm. Experimental results on face recognition and facial expression recognition demonstrate the effectiveness of the proposed algorithm.

Anomaly Detection on ADS-B Flight Data Using Machine Learning Techniques

2021 ◽  
pp. 771-783
Author(s):  
Osman Taşdelen ◽  
Levent Çarkacioglu ◽  
Behçet Uğur Töreyin
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Machine Learning Techniques ◽  
Flight Data ◽  
Learning Techniques

scholarly journals Valid Probabilistic Anomaly Detection Models for System Logs

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Chunbo Liu ◽  
Lanlan Pan ◽  
Zhaojun Gu ◽  
Jialiang Wang ◽  
Yitong Ren ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Learning Algorithm ◽  
Recall Rate ◽  
Support Vector ◽  
Fusion Algorithm ◽  
Flexible Tool ◽  
System Logs ◽  
Output Only ◽  
Better Than

System logs can record the system status and important events during system operation in detail. Detecting anomalies in the system logs is a common method for modern large-scale distributed systems. Yet threshold-based classification models used for anomaly detection output only two values: normal or abnormal, which lacks probability of estimating whether the prediction results are correct. In this paper, a statistical learning algorithm Venn-Abers predictor is adopted to evaluate the confidence of prediction results in the field of system log anomaly detection. It is able to calculate the probability distribution of labels for a set of samples and provide a quality assessment of predictive labels to some extent. Two Venn-Abers predictors LR-VA and SVM-VA have been implemented based on Logistic Regression and Support Vector Machine, respectively. Then, the differences among different algorithms are considered so as to build a multimodel fusion algorithm by Stacking. And then a Venn-Abers predictor based on the Stacking algorithm called Stacking-VA is implemented. The performances of four types of algorithms (unimodel, Venn-Abers predictor based on unimodel, multimodel, and Venn-Abers predictor based on multimodel) are compared in terms of validity and accuracy. Experiments are carried out on a log dataset of the Hadoop Distributed File System (HDFS). For the comparative experiments on unimodels, the results show that the validities of LR-VA and SVM-VA are better than those of the two corresponding underlying models. Compared with the underlying model, the accuracy of the SVM-VA predictor is better than that of LR-VA predictor, and more significantly, the recall rate increases from 81% to 94%. In the case of experiments on multiple models, the algorithm based on Stacking multimodel fusion is significantly superior to the underlying classifier. The average accuracy of Stacking-VA is larger than 0.95, which is more stable than the prediction results of LR-VA and SVM-VA. Experimental results show that the Venn-Abers predictor is a flexible tool that can make accurate and valid probability predictions in the field of system log anomaly detection.

scholarly journals Detecting anomalies in financial statements using machine learning algorithm

2019 ◽  
Vol 4 (2) ◽  
pp. 181-201 ◽  
Author(s):  
Mark Lokanan ◽  
Vincent Tran ◽  
Nam Hoai Vuong
Keyword(s):  
Anomaly Detection ◽  
Learning Algorithm ◽  
Financial Statements ◽  
Data Set ◽  
Financial Reports ◽  
Accounting Data ◽  
Content Type ◽  
Listed Firms ◽  
Detection Model ◽  
Credit Worthiness

Purpose The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method. Design/methodology/approach The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly. Findings The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable. Research limitations/implications The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels. Practical implications The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud. Originality/value This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.

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