Anomaly Detection

About the speaker: Kevin Chen is currently a self-taught ML practitioner concentrating on anomaly detection, time-series, streaming data, and (later) predictive analytics. He has a diverse technology and domain background spanning both enterprise and deep-tech. Previously, Kevin was a blockchain researcher/advocate involved with projects including IOTA and Fetch.AI. Before blockchain, he was in the financial sector as a back-end developer and data analyst at Citigroup and Aristeia Capital respectively. Kevin graduated from UVA with a Bachelors in CS. https://github.com/Kevin-Chen0

Looking for a valuable way to use your data? With anomaly detection, you can use it to stop a minor issue from becoming a widespread, time-consuming problem. By proactively detecting abnormal behavior, your company can ensure the right people are alerted to unexpected changes and are able to make faster decisions about what actions need to be taken. Join us for this "ask me anything" webinar to hear from a panel of data scientists on the basics of anomaly detection, common use cases, and some key techniques to keep in mind as you get started. We've got the answers to all your questions about anomaly detection.

Wearing a mask on their faces, 2 robbers smashed the walls of the jewellery showroom and looted gold and diamond jewelleries worth INR 13 crores (USD 1.8 million). This robbery shook Tamilnadu as the store had CCTV and watchmen to prevent such mishaps. "CCTVs are like eyes without brains" says Heptagon's co-founder & C.E.O Vijayramkumar Veeraraghavan In this video,Vijay introduces A.I. based Video Analytics and Surveillance Platform - Focus Sentinel, to prevent such mishaps from happening in the future. Reach out to business@heptagon.in to know more.

We present the first application of adaptive machine learning to the identification of anomalies in a data set of non-periodic astronomical light curves. The method follows an active learning strategy where highly informative objects are selected to be labelled. This new information is subsequently used to improve the machine learning model, allowing its accuracy to evolve with the addition of every new classification. For the case of anomaly detection, the algorithm aims to maximize the number of real anomalies presented to the expert by slightly modifying the decision boundary of a traditional isolation forest in each iteration. As a proof of concept, we apply the Active Anomaly Discovery (AAD) algorithm to light curves from the Open Supernova Catalog and compare its results to those of a static Isolation Forest (IF). For both methods, we visually inspected objects within 2% highest anomaly scores. We show that AAD was able to identify 80% more true anomalies than IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

Historically, the MixMode platform has provided its users with a forensic hunting platform with intel-based Indicators and Security Events from public & proprietary sources. While these detections still have their place in the security ecosystem, the increase in state-sponsored attacks, insider threats and adversarial artificial intelligence means there are simply too many threats to your network to rely on solely intelligence-based detections or proactive hunting. Many of these threats are sophisticated enough to evade traditional threat detection or, in the case of zero-day threats, signature-based detection may not even be possible. In the face of this growing threat, the best defense is to supplement these traditional methods with anomaly detection, a term that is quickly becoming genericized as it is rapidly bandied about within the industry. Here we will discuss some of the opportunities and challenges that can arise with anomaly detection as well as MixMode's unique approach to the solution.

--We present a method to compute the Shapley values of reconstruction errors of principal component analysis (PCA), which is particularly useful in explaining the results of anomaly detection based on PCA. Because features are usually correlated when PCA-based anomaly detection is applied, care must be taken in computing a value function for the Shapley values. We utilize the probabilistic view of PCA, particularly its conditional distribution, to exactly compute a value function for the Shapely values. We also present numerical examples, which imply that the Shapley values are advantageous for explaining detected anomalies than raw reconstruction errors of each feature. Anomaly detection based on machine learning has been actively studied and now plays an important role in various industrial applications such as fraud detection in finance [1], intrusion detection [2], and fault detection of mechanical systems [3]. Up to date, there have been proposed many types of anomaly detection algorithms based on different assumptions and technical principles (see, e.g., [4]-[6]).

In recent years, there has been a growing interest in identifying anomalous structure within multivariate data streams. We consider the problem of detecting collective anomalies, corresponding to intervals where one or more of the data streams behaves anomalously. We first develop a test for a single collective anomaly that has power to simultaneously detect anomalies that are either rare, that is affecting few data streams, or common. We then show how to detect multiple anomalies in a way that is computationally efficient but avoids the approximations inherent in binary segmentation-like approaches. This approach, which we call MVCAPA, is shown to consistently estimate the number and location of the collective anomalies, a property that has not previously been shown for competing methods. MVCAPA can be made robust to point anomalies and can allow for the anomalies to be imperfectly aligned. We show the practical usefulness of allowing for imperfect alignments through a resulting increase in power to detect regions of copy number variation.

Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies. Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.

Artificial intelligence is no longer in the future. You will learn how to: Detect anomalies in IoT applications using TIBCO Data Science with deep learning libraries (e.g. H2O, Python, TensorFlow, Amazon SageMaker) Use TIBCO Data Science models on the AWS Marketplace Deploy models into operations for real-time monitoring and surveillance Optimize your business and experience explosive growth with real-time anomaly detection.