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What is Machine Learning? - Security Boulevard


This big data discipline of artificial intelligence gives systems the freedom to automatically gain information and improve from experience without manual programming. Machine learning is literally just that – "letting the machine learn". The definition of machine learning is "the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as'training data', in order to make predictions or decisions without being explicitly programmed to perform the task".

Gurucul on Twitter


Gurucul is a leading provider of User and Entity Behavior Analytics #UEBA and Identity Analytics #IdA.

Machine Learning Proves Key to Privileged Account Protection


Behavioral analytics is quickly becoming the cornerstone of most every Infosec technology. However, it takes a lot more than simply analyzing user activity with rules and statistics, it takes applying ML (Machine Learning) to access and activity data, as well as employing AI (Artificial Intelligence) to reduce false positives and accurately risk score. Two critical capabilities that a multitude of security vendors have yet to address in their products to enable automated risk response. Those lacking machine-based cognitive abilities have come to rely on static pattern definitions, signatures and policies for a legacy world of known good and bad. Today, we must assume compromise and assess risk, even more importantly for privileged accounts with the access keys to IT environments.

Futuristic Trends in Risk Analytics Market 2019 Evaluates High Growth Key Players Study- SAP SE, Gurucul, Axiomsl Inc - 5Gigs News


New York City, NY: October 15, 2019 – Published via (Wired Release) – MarketResearch.Biz authorities conduct an exhaustive approach of primary and secondary research on Risk Analytics Market and carry out a detailed analysis of distinct factors, including Risk Analytics technological developments and the supply/demand shifts in different Risk Analytics Market across the globe to precisely forecast the industry's growth probabilities. To help clients to plan effective tactics for Risk Analytics market growth for the period of 2019 to 2028. The researchers have offered quantitative and qualitative study along with opportunity assessment in the Risk Analytics market report. In addition, the report serves SWOT analysis, Porter's Five Forces analysis and PESTLE analysis for detail comparisons and other crucial analysis. Each section of the Risk Analytics market report has crucial information to offer for market players to improve their gross margin, Risk Analytics sales and marketing strategy, and profit margins.

Machine Learning: Arthur Samuel, Artificial Intelligence (AI) & Big Data


Programmed by Arthur Samuel, this big data discipline of artificial intelligence replaces the tedious task of trying to understand the problem well enough to be able to write a program, which can take much longer or be virtually impossible. Techopedia defines the discipline of machine learning as "an artificial intelligence (AI) discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience. Machine learning facilitates the continuous advancement of computing through exposure to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in subsequent (though not identical) situations." In 1959, IBM employee Arthur Samuel wanted to teach a computer to play checkers.