Incompatimic model of anomaly detection on different panoramas

2021 ◽  
Author(s):  
O.P. Arkhipov ◽  
M.V. Tsukanov
Keyword(s):  
Anomaly Detection ◽  
Singular Points ◽  
Single View ◽  
Noise Type ◽  
Automatic Methods ◽  
Algorithmic Model ◽  
Special Points ◽  
Selection Of

The development of automatic methods for comparing panoramas obtained at different times during the inspection flight of UAVs of the same area is currently an urgent and popular task. In this connection, a new algorithmic model for detecting anomalies on multi-time panoramas was proposed, based on the comparison of the found singular points and descriptors, establishing their mutual correspondence on panoramas, and highlighting the found differences in non-overlapping areas of anomalies. The strategy aimed at bringing the panoramas to a single view and their subsequent synchronization is proposed. The results of the algorithm are presented, using the example of multi-time panoramas of the selected inspected area. We managed to synchronize the panoramas at different times to minimize differences in the shooting angles and illumination. Perform a search for anomalies on multi-time panoramas, excluding the selection of anomalies of the "noise" type and minor deviations in the color and geometric coordinates of special points. Sort the found anomalies by importance groups.

Iterative Selection of Categorical Variables for Log Data Anomaly Detection

2021 ◽  
pp. 757-777
Author(s):  
Max Landauer ◽  
Georg Höld ◽  
Markus Wurzenberger ◽  
Florian Skopik ◽  
Andreas Rauber
Keyword(s):  
Anomaly Detection ◽  
Categorical Variables ◽  
Log Data ◽  
Selection Of

scholarly journals A Log-Based Anomaly Detection Method with Efficient Neighbor Searching and Automatic K Neighbor Selection

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Bingming Wang ◽  
Shi Ying ◽  
Zhe Yang
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Detection Method ◽  
Tree Model ◽  
Log Data ◽  
Neighbor Search ◽  
Different Types ◽  
Efficient Selection ◽  
Selection Of

Using the k-nearest neighbor (kNN) algorithm in the supervised learning method to detect anomalies can get more accurate results. However, when using kNN algorithm to detect anomaly, it is inefficient at finding k neighbors from large-scale log data; at the same time, log data are imbalanced in quantity, so it is a challenge to select proper k neighbors for different data distributions. In this paper, we propose a log-based anomaly detection method with efficient selection of neighbors and automatic selection of k neighbors. First, we propose a neighbor search method based on minhash and MVP-tree. The minhash algorithm is used to group similar logs into the same bucket, and MVP-tree model is built for samples in each bucket. In this way, we can reduce the effort of distance calculation and the number of neighbor samples that need to be compared, so as to improve the efficiency of finding neighbors. In the process of selecting k neighbors, we propose an automatic method based on the Silhouette Coefficient, which can select proper k neighbors to improve the accuracy of anomaly detection. Our method is verified on six different types of log data to prove its universality and feasibility.

scholarly journals Evaluation model of the companies operating within logistic network

2015 ◽  
Vol 36 (4) ◽  
pp. 21-33 ◽  
Author(s):  
Pavel Foltin ◽  
Mariusz Gontarczyk ◽  
Andrzej Świderski ◽  
Jarosław Zelkowski
Keyword(s):  
Complex Systems ◽  
Evaluation Model ◽  
Competitive Environment ◽  
Practical Realization ◽  
Logistics Systems ◽  
Evaluation Problem ◽  
Operational Activity ◽  
Algorithmic Model ◽  
Selection Of ◽  
Holistic Manner

In the paper authors presented concept of evaluation of complex systems such as logistic companies which operate in competitive environment. Authors also highlighted the importance of the evaluation problem in operational activity, particularly in the properly prepared procedures to be followed, consisted of several stages adapted to currently conducted research. Properly executed evaluation of companies enables customers to take right decisions on contracting services and stimulate the development of those companies. It should be noted that the proposed algorithmic model of assessment may be particularly useful for assessment of complex logistics systems. Characteristics of the respective phases developed procedure allowed to indicate that it is important to select of appropriate criteria and evaluation methods. It was pointed out that particularly useful for assessing of logistics companies are multi-criteria analysis methods, because they allow to examine objects in a holistic manner, taking into consideration various aspects of activities such organizations. For the practical realization of assessment of logistics companies there was proposed Bellinger method, which contains quite transparent procedures. The results obtained from that method are consistent with those carried out by means of other, more complex multi-criteria methods. Also there was presented the problem of selection of weighted factors for the criteria in the context of their impact on the end result of evaluation. It was pointed out that the choice of an appropriate procedure for determining weights depends on the nature of the evaluated problem and form of criteria.

scholarly journals A Hybrid Approach for the Analysis of Feature Selection using Information Gain and BAT Techniques on The Anomaly Detection

2021 ◽  
Vol 12 (5) ◽  
pp. 656-666
Author(s):  
Dr. Joel Sunny Deol Gosu, Dr. Pullagura Priyadarsini, Ravi Kanth Motupalli
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Anomaly Detection ◽  
Heuristic Algorithm ◽  
Analytical Data ◽  
The Internet ◽  
Classification Algorithms ◽  
Detection Systems ◽  
Modeling Power ◽  
Selection Of

Every day, millions of people in many institutions communicate with each other on the Internet. The past two decades have witnessed unprecedented levels of Internet use by people around the world. Almost alongside these rapid developments in the internet space, an ever increasing incidence of attacks carried out on the internet has been consistently reported every minute. In such a difficult environment, Anomaly Detection Systems (ADS) play an important role in monitoring and analyzing daily internet activities for security breaches and threats. However, the analytical data routinely generated from computer networks are usually of enormous size and of little use. This creates a major challenge for ADSs, who must examine all the functionality of a certain dataset to identify intrusive patterns. The selection of features is an important factor in modeling anomaly-based intrusion detection systems. An irrelevant characteristic can lead to overfitting which in turn negatively affects the modeling power of classification algorithms. The objective of this study is to analyze and select the most discriminating input characteristics for the construction of efficient and computationally efficient schemes for an ADS. In the first step, a heuristic algorithm called IG-BA is proposed for dimensionality reduction by selecting the optimal subset based on the concept of entropy. Then, the relevant and meaningful features are selected, before implementing Number of Classifiers which includes: (1) An irrelevant feature can lead to overfitting which in turn negatively affects the modeling power of the classification algorithms. Experiment was done on CICIDS-2017 dataset by applying (1) Random Forest (RF), (2) Bayes Network (BN), (3) Naive Bayes (NB), (4) J48 and (5) Random Tree (RT) with results showing better detection precision and faster execution time. The proposed heuristic algorithm outperforms the existing ones as it is more accurate in detection as well as faster. However, Random Forest algorithm emerges as the best classifier for feature selection technique and scores over others by virtue of its accuracy in optimal selection of features.

An Anomaly Detection Method for Individual Services on a Web-Based System by Selection of Dummy Variables in Multiple Regression

2014 ◽  
Vol 97 (2) ◽  
pp. 9-16 ◽  
Author(s):  
Yuki Tsuda ◽  
Masaki Samejima ◽  
Masanori Akiyoshi ◽  
Norihisa Komoda ◽  
Matsuki Yoshino
Keyword(s):  
Anomaly Detection ◽  
Multiple Regression ◽  
Detection Method ◽  
Web Based ◽  
Dummy Variables ◽  
Web Based System ◽  
Selection Of
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