In data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. (Wikipedia)
"Isolation Forest" is a brilliant algorithm for anomaly detection born in 2009 (here is the original paper). It has since become very popular: it is also implemented in Scikit-learn (see the documentation). In this article, we will appreciate the beauty in the intuition behind this algorithm and understand how exactly it works under the hood, with the aid of some examples. Anomaly (or outlier) detection is the task of identifying data points that are "very strange" compared to the majority of observations. This is useful in a range of applications, from fault detection to discovery of financial frauds, from finding health issues to identifying unsatisfied customers. Moreover, it can also be beneficial for machine learning pipelines, since it has been proven that removing outliers leads to an increase in model accuracy.
Organizations' attack surfaces are exponentially expanding, contributing to an unprecedented growth in cybersecurity risks. The internet of things, 5G, Wi-Fi 6, and other networking advances are driving an increase in network-connected devices that can be exploited by cybercriminals. For many employees, remote work is expected to remain the rule, not the exception, providing cybercriminals with many new opportunities. And as more organizations integrate data with third-party applications, APIs are a growing area of security concern. Expanding attack surfaces and the escalating severity and complexity of cyberthreats are exacerbated by a chronic shortage of cybersecurity talent.
Secure access service edge, or SASE, combines networking and security into a cloud-based service, and it's growing fast. According to Gartner projections, enterprise spending on SASE will hit almost $7 billion this year, up from under $5 billion in 2021. Gartner also predicts that more than 50% of organizations will have strategies to adopt SASE by 2025, up from less than 5% in 2020. The five core components of the SASE stack are SD-WAN, firewall-as-a-service (FWaaS), secure web gateway (SWG), cloud access security broker (CASB), and zero trust network access (ZTNA). "It's something that most, if not all, SASE vendors are working on," says Gartner analyst Joe Skorupa.
Nobody wants outliers in their data -- especially when they have come from the likes of false entries due to fat thumbs. A couple of zeros can throw off an algorithm and can destroy summary statistics. So this is how you use machine learning to remove those pesky outliers. Historically, the first step to anomaly detection is to try and understand what's "normal", and then find examples of "not normal". These "not normal" points are what we would classify as outliers -- they didn't fit our expected distribution even at the furthest ends of it.
This experiment aimed to better represent string fields encountered in a large stream of event data. The focus was on two such fields: "command line" and "image file name." The first field was discussed extensively in the previous blog -- it consists of an instruction that starts a process that is then recorded as an event and sent to the CrowdStrike Security Cloud. The second field is the name for the executable starting the process, which is a substring of the corresponding command line. Two main factors dictated the pursuit of such an embedding model: First, we aimed to improve the string processing in our models, and second, we wanted to benefit from the significant developments achieved in the area of natural language processing (NLP) in the last few years.
Thus, anomaly detection, a technology that relies on Artificial Intelligence to identify abnormal behavior within the pool of collected data, has become one of the main objectives of the Industrial IoT. Anomaly detection can be a key for solving such intrusions, as while detecting anomalies, perturbations of normal behavior indicate a presence of intended or unintended induced attacks, defects, faults, and such. Quality data in the form of product or process measurements are obtained in real-time during the manufacturing process and plotted on a graph with predetermined control limits that reflect the capability of the process. By monitoring and controlling a process, we can assure that it operates at its fullest potential and detect anomalies at early stages. Thus, anomaly detection, a technology that relies on Artificial Intelligence to identify abnormal behavior within the pool of collected data, has become one of the main objectives of the Industrial IoT.
These 101 algorithms are equipped with cheat sheets, tutorials, and explanations. Think of this as the one-stop shop/dictionary/directory for machine learning algorithms. The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you'll find 101 machine learning algorithms with useful Python tutorials, R tutorials, and cheat sheets from Microsoft Azure ML, SAS, and Scikit-Learn to help you know when to use each one (if available). At Data Science Dojo, our mission is to make data science (machine learning in this case) available to everyone.
There is no longer any doubt that artificial intelligence (AI) is advancing biological discovery and biomanufacturing operations. In biological discovery, AI systems such as AlphaFold and the Atomic Rotationally Equivariant Scorer are celebrated for their uncanny ability to predict tertiary structures for proteins and RNA molecules. In biomanufacturing, AI systems usually enjoy less fanfare. Yet they can provide valuable functions such as pattern recognition, real-time assessment of batch quality, multivariable control for continuous manufacturing, prediction/optimization of critical process parameters, and anomaly detection. Such functions are critical to the success of gene and cell therapy operations.
Outliers are patterns in data that do not confirm to the expected behavior. While detecting such patterns are of prime importance in Credit Card Fraud, Stock Trading etc. Detecting anomaly or outlier observations are also of importance when training any of the supervised machine learning models. This brings us to two very important questions: concept of a local outlier, and why a local outlier? In a multivariate dataset where the rows are generated independently from a probability distribution, only using centroid of the data might not alone be sufficient to tag all the outliers. Measures like Mahalanobis distance might be able to identify extreme observations but won't be able to label all possible outlier observations.
Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial automation, autonomous vehicles, and automated checkouts, require ML models that run on devices at the edge so predictions can be made in real time when new data is available. Another common challenge you may face when dealing with computing applications at the edge is how to efficiently manage the fleet of devices at scale.