Fast Anomaly Detection in Micro Data Centers Using Machine Learning Techniques

2020 ◽  
Author(s):  
Negin Piran Nanekaran ◽  
Mohammad Esmalifalak ◽  
Mehdi Narimani
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Data Centers ◽  
Machine Learning Techniques ◽  
Micro Data ◽  
Learning Techniques

scholarly journals IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2857
Author(s):  
Laura Vigoya ◽  
Diego Fernandez ◽  
Victor Carneiro ◽  
Francisco Nóvoa
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
High Detection Rate ◽  
Security Vulnerabilities ◽  
Smart Systems ◽  
Learning Techniques ◽  
Positive Rate

With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

A Review of Machine Learning Techniques for Anomaly Detection in Static Graphs

2020 ◽  
pp. 146-162
Author(s):  
Hesham M. Al-Ammal
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Anomaly Detection ◽  
Real Life ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Methods ◽  
Data Set ◽  
Learning Techniques ◽  
Vector Machines

Detection of anomalies in a given data set is a vital step in several applications in cybersecurity; including intrusion detection, fraud, and social network analysis. Many of these techniques detect anomalies by examining graph-based data. Analyzing graphs makes it possible to capture relationships, communities, as well as anomalies. The advantage of using graphs is that many real-life situations can be easily modeled by a graph that captures their structure and inter-dependencies. Although anomaly detection in graphs dates back to the 1990s, recent advances in research utilized machine learning methods for anomaly detection over graphs. This chapter will concentrate on static graphs (both labeled and unlabeled), and the chapter summarizes some of these recent studies in machine learning for anomaly detection in graphs. This includes methods such as support vector machines, neural networks, generative neural networks, and deep learning methods. The chapter will reflect the success and challenges of using these methods in the context of graph-based anomaly detection.

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

Anomaly Detection in Drone-Captured Images Using Machine Learning Techniques and Deep Learning Architectures

2021 ◽  
pp. 783-791
Author(s):  
Kartik Joshi ◽  
G. Vidya ◽  
Soumya Shaw ◽  
Abitha K. Thyagarajan ◽  
Akhil Pathak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Learning Architectures

scholarly journals Anomaly detection using Unsupervised Machine Learning for Grid computing site operation

2020 ◽  
Vol 245 ◽  
pp. 07016
Author(s):  
Tomoe Kishimoto ◽  
Junichi Tnaka ◽  
Tetsuro Mashimo ◽  
Ryu Sawada ◽  
Koji Terashi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Grid Computing ◽  
Machine Learning Techniques ◽  
Storage Element ◽  
Site Administrators ◽  
Grid Middleware ◽  
Learning Techniques ◽  
The Status ◽  
Computing Grid

A Grid computing site is composed of various services including Grid middleware, such as Computing Element and Storage Element. Text logs produced by the services provide useful information for understanding the status of the services. However, it is a time-consuming task for site administrators to monitor and analyze the service logs every day. Therefore, a support framework has been developed to ease the site administrator’s work. The framework detects anomaly logs using Machine Learning techniques and alerts site administrators. The framework has been examined using real service logs at the Tokyo Tier2 site, which is one of the Worldwide LHC Computing Grid sites. In this paper, a method of the anomaly detection in the framework and its performances at the Tokyo Tier2 site are reported.

Predicting mental disorder from noisy questionnaires: an anomaly detection approach based on keyword extraction and machine learning techniques

2021 ◽  
pp. 1-13
Author(s):  
Qing Zhou ◽  
Xi Shi ◽  
Liang Ge
Keyword(s):  
Machine Learning ◽  
Mental Disorders ◽  
Anomaly Detection ◽  
Well Being ◽  
Machine Learning Techniques ◽  
Keyword Extraction ◽  
Learning Techniques ◽  
Detection Approach ◽  
Scaling Methods ◽  
Machine Learning Models

The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation of question significance. Experiments demonstrate the effectiveness of our proposed methods.

Sign in / Sign up

Export Citation Format

Share Document