Principal Component Analysis (PCA) is a great tool for a data analysis projects for a lot of reasons. If you have never heard of PCA, in simple words it does a linear transformation of your features using covariance or correlation. I will add a few links below if you want to know more about it. Some of the applications of PCA are dimensional reduction, feature analysis, data compression, anomaly detection, clustering and many more. The first time I learnt about PCA, it was not easy to understand and quite confusing.

Introducing Anomagram - An interactive tool that lets you train and evaluate an autoencoder for the task of anomaly detection on ECG data. Across many business use cases that generate data, it is frequently desirable to automatically identify data samples that deviate from "normal". In many cases, these deviations are indicative of issues that need to be addressed. For example, an abnormally high cash withdrawal from a previously unseen location may be indicative of fraud. An abnormally high CPU temperature may be indicative of impending hardware failure.

Learning classifiers for misuse and anomaly detection using a bag of system calls representation. Anomaly detection in health data based on deep learning. Abnormal human activity recognition using SVM based approach. Anomaly detection of gas turbines based on normal pattern extraction. Contextual anomaly detection for a critical industrial system based on logs and metrics.

Machine Learning helps to automate analysis and surface insights that are important for the day-to-day operation of certain business functions. In this talk, Elvis Saravia, Education Architect at Elastic, will focus on introducing a series of machine learning jobs, via the Machine Learning UI, that are easy to compose and can help classify new information (in the form of document classification) and help reveal an abnormal behavior in the data (in the form of anomaly detection).

In the last few months, millions of dollars have been stolen from unemployment systems during this time of immense pressure due to coronavirus-related claims. A skilled ring of international fraudsters has been submitting false unemployment claims for individuals that still have steady work. The attackers use previously acquired Personally Identifiable Information (PII) such as social security numbers, addresses, names, phone numbers, and banking account information to trick public officials into accepting the claims. Payouts to these employed people are then redirected to money laundering accomplices who pass the money around to veil the illicit nature of the cash before depositing it into their own accounts. The acquisition of the PII that enabled these attacks, and the pattern of money laundering that financial institutions failed to detect highlight the importance of renewed security.

In the last few months, millions of dollars have been stolen from unemployment systems during this time of immense pressure due to coronavirus-related claims. A skilled ring of international fraudsters has been submitting false unemployment claims for individuals that still have steady work. The attackers use previously acquired Personally Identifiable Information (PII) such as social security numbers, addresses, names, phone numbers, and banking account information to trick public officials into accepting the claims. Payouts to these employed people are then redirected to money laundering accomplices who pass the money around to veil the illicit nature of the cash before depositing it into their own accounts. The acquisition of the PII that enabled these attacks, and the pattern of money laundering that financial institutions failed to detect highlight the importance of renewed security.

Outlier Detection is also known as anomaly detection, noise detection, deviation detection, or exception mining. There is no universally accepted definition. An early definition by (Grubbs, 1969) is: An outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs. An observation which appears to be inconsistent with the remainder of that set of data. A list of applications that utilize outlier detection according to (Hodge, V.J. and Austin, J., 2014) is: This is analogous to unsupervised clustering.

According to McKinsey's study "Visualizing the uses and potential impact of AI and other analytics", 2018, the estimated impact of artificial intelligence and other analytics on all industries regarding anomaly detection is between $1.0T and $1.4T. Anomaly detection is the critical success factor in predictive maintenance, which tries to anticipate when maintenance is required. This differs from the classical preventive approach, in which activities are planned on a regularly scheduled basis, or condition-based maintenance activities, in which assets are monitored through IoT sensors. Applying anomaly detection algorithms based on machine learning, it's possible to perform prognostics to estimate the condition of a system or a component and its remaining useful life (RUL), in order to predict an incoming failure. One of the most famous algorithms is the MSET-SPRT, well-described with a use case in this blog post: "Machine Learning Use Case: Real-Time Support for Engineered Systems."

Hands-on of Machine Learning in Cybersecurity Supervised and unsupervised machine learning models for cybersecurity Description Machine learning is disrupting cybersecurity to a greater extent than almost any other industry. Many problems in cyber security are well suited to the application of machine learning as they often involve some form of anomaly detection on very large volumes of data. This course deals the most found issues in cybersecurity such as malware, anomalies detection, SQL injection, credit card fraud, bots, spams and phishing. All these problems are covered in case studies.

This project is an example demonstrating how to use Python to train two different machine learning models to detect anomalies in an electric motor. The first model relies on the classic machine learning technique of Mahalanobis distance. The second model is an autoencoder neural network created with TensorFlow and Keras. Data was captured using an ESP32 and MSA301 3-axis accelerometer taped to a ceiling fan. Each sample is about 200 samples of all 3 axes captured over the course of 1 second.