Overview of Anomaly Detection techniques in Machine Learning

2020 ◽  
Author(s):  
Akanksha Toshniwal ◽  
Kavi Mahesh ◽  
Jayashree R.
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Techniques

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

Machine Learning Applications for Anomaly Detection

2019 ◽  
pp. 49-83
Author(s):  
Teguh Wahyono ◽  
Yaya Heryadi
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Anomaly Detection ◽  
Research Trend ◽  
Application Development ◽  
Detection Techniques ◽  
Airline Safety ◽  
Research Areas ◽  
Machine Learning Applications ◽  
Meta Approach

The aim of this chapter is to describe and analyze the application of machine learning for anomaly detection. The study regarding the anomaly detection is a very important thing. The various phenomena often occur related to the anomaly study, such as the occurrence of an extreme climate change, the intrusion detection for the network security, the fraud detection for e-banking, the diagnosis for engines fault, the spacecraft anomaly detection, the vessel track, and the airline safety. This chapter is an attempt to provide a structured and a broad overview of extensive research on anomaly detection techniques spanning multiple research areas and application domains. Quantitative analysis meta-approach is used to see the development of the research concerned with those matters. The learning is done on the method side, the techniques utilized, the application development, the technology utilized, and the research trend, which is developed.

scholarly journals DETECTING ANOMALOUS ACTIVITIES IN SPATIO-TEMPORAL REGION

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
H Manoj T Gadiyar ◽  
Kanchana Goudar ◽  
Dr. Thyagaraju G S ◽  
Harshitha K ◽  
Megha Jeevan Kurdekar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Anomaly Detection ◽  
Unsolved Problem ◽  
Detection System ◽  
Temporal Region ◽  
Detection Techniques ◽  
Video Frames ◽  
Spatio Temporal ◽  
Anomaly Detection System

Anomaly detection system using Machine Learning helps to detect the anomalies like accidents etc. Detecting anomalies in videos is important but it is an unsolved problem. This system will automatically detect the anomaly by image analysis from the surveillance videos. This system is capable of tracking abnormal event in each frame and generates a notification of such event. The proposed K-means algorithm performs existing anomaly detection techniques, while being comparatively time efficient. The system just requires a dataset of video frames and is fed as input to the anomaly detecting module

scholarly journals An Efficient Anomaly Detection Based on Optimal Deep Belief Network in Big Data

2019 ◽  
Vol 9 (1) ◽  
pp. 708-716
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Anomaly Detection ◽  
Computer Network ◽  
Large Datasets ◽  
Support Vector ◽  
The Internet ◽  
Data Sets ◽  
Data Generation ◽  
Detection Techniques

Nowadays, the internet and network service user’s counts are increasing and the data generation speed also very high. Then again, we see greater security dangers on the internet, enterprise network, websites and the network. Anomaly has been known as one of the effective cyber threats over the internet which increasing exponentially and thus overcomes the commonly used approaches for anomaly detection and classification. Anomaly detection is used in big data analytics to recognize the unexpected behaviour. The most commonly used characteristics in network environment are size and dimensionality, which are big datasets and also impose problems in recognizing useful patterns, For example, to identify the network traffic anomalies from the large datasets. Due to the enormous increase of computer network based facilities it is a challenge to perform fast and efficient anomaly detection. The anomaly recognition in big data sets is more useful to discover fraud and abnormal action. Here, we mainly focus on the problems regarding anomaly detection, so we introduce a novel machine learning based anomaly detection technique. Machine learning approach is used to enhance the anomaly detection speed which is very much useful to detect the anomaly from the large datasets. We evaluate the proposed framework by performing experiments with larger data sets and compare to several existing techniques such as fuzzy, SVM (Support Vector Machine) and PSO (Particle swarm optimization). It has shown 98% percentage of accuracy and the false rate of 0.002 % on proposed classifier. The experimental results illuminate that better performance than existing anomaly detection techniques in big data environment.

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

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