To improve speed and intelligence of threat detection and response, Guruculs cloud-native XDR platform is adding machine learning, integration risk scoring and more.

by Anne Lessman

Tags: cloud-native, Gurucul, integration, machine learning, real-time, threat detection,

The latest upgrade to the Gurucul XDR platform adds extended detection and response alongside improved risk scoring to strengthen security operations effectiveness and productivity.

Improvements to Guruculs cloud-native solution also sport features to enable intelligent investigations and risk-based response automation. New features include extended data linking, additions to its out-of-the-box integrations, contextual machine learning (ML) analytics and risk-prioritized alerting.

The driving force behind these updates is to provide users a single pane of risk, according to Gurucul CEO Saryu Nayyar.

Most XDR products are based on legacy platforms limited to siloed telemetry and threat detection, which makes it difficult to provide unified security operations capabilities, Nayyar said.

Gurucul Cloud-native XDR is vendor-agnostic and natively built on a Big Data architecture designed to process, contextually link, analyze, detect, and risk score using data at massive scale. It also uses contextual Machine Learning models alongside a risk scoring engine to provide real-time threat detection, prioritize risk-based alerts and support automated response, Nayyar.added.

Gurucul XDR provides the following capabilities that are proven to improve incident response times:

AI/ML Suggestive Investigation and Automated Intelligent Responses: Traditional threat hunting tools and SIEMs focus on a limited number of use cases since they rely on data and alerts from a narrow set of resources. With cloud adoption increasing at a record pace, threat hunting must span hybrid on-premises and cloud environments and ingest data from vulnerability management, IoT, medical, firewall, network devices and more.

Guruculs approach provides agentless, out-of-the-box integrations that support a comprehensive set of threat hunting applications. These include: Insider threat detection, Data exfiltration, Phishing, Endpoint forensics, Malicious processes and Network threat analytics.

Incident Timeline, Visualizations, and Reporting: Automated Incident Timelines create a smart link of the entire attack lifecycle for pre-and post-incident analysis. Timelines can span days and even years of data in easy-to-understand visualizations.

Guruculs visualization and dashboarding enables analysts to view threats from different perspectives using several widgets, including TreeMap, Bubble Chart, etc., that provide full drill-down capabilities into events without leaving the interface. The unique scorecard widget generates a spider chart representation of cyber threat hunting outcomes such as impact, sustaining mitigation measures, process improvements scores, etc.

Risk Prioritized Automated Response: Integration with Gurucul SOAR enables analysts to invoke more than 50 actions and 100 playbooks upon detection of a threat to minimize damages.

Entity Based Threat Hunting: Perform contextual threat hunting or forensics on entities. Automate and contain any malicious or potential threat from a single interface.

Red Team Data Tagging: Teams can leverage red team exercise data and include supervised learning techniques as part of a continuous AI-based threat hunting process.

According to Gartner, XDR products aim to solve the primary challenges with SIEM products, such as effective detection of and response to targeted attacks, including native support for behavior analysis, threat intelligence, behavior profiling and analytics.

Further, the primary value propositions of an XDR product are to improve security operations productivity and enhance detection and response capabilities by including more security components into a unified whole that offers multiple streams of telemetry, Gartner added.

The result, the firm said, is to present options for multiple forms of detection and . . multiple methods of response.

Gurucul XDR provides the following capabilities that are proven to improve incident response times by nearly 70%:

Surgical Response

Intelligent Centralized Investigation

Rapid Incident Correlation and Causation

Gurucul XDR is available immediately from Gurucul and its business partners worldwide.

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Gurucul XDR Uses Machine Learning & Integration for Real-Time Threat Detection, Incident Response - Integration Developers

05 February 2021

Researchers in EPFLs Digital and Cognitive Musicology Lab in the College of Humanities used an unsupervised machine learning model to reveal how modes such as major and minor have changed throughout history.

Many people may not be able to define what a minor mode is in music, but most would almost certainly recognise a piece played in a minor key. Thats because we intuitively differentiate the set of notes belonging to the minor scale which tend to sound dark, tense, or sad from those in the major scale, which more often connote happiness, strength, or lightness.

But throughout history, there have been periods when multiple other modes were used in addition to major and minor or when no clear separation between modes could be found at all.

Understanding and visualising these differences over time is what Digital and Cognitive Musicology Lab (DCML) researchers Daniel Harasim, Fabian Moss, Matthias Ramirez, and Martin Rohrmeier set out to do in a recent study, which has been published in the open-access journal Humanities and Social Sciences Communications. For their research, they developed a machine learning model to analyze more than 13,000 pieces of music from the 15th to the 19th centuries, spanning the Renaissance, Baroque, Classical, early Romantic, and late-Romantic musical periods.

We already knew that in the Renaissance [1400-1600], for example, there were more than two modes. But for periods following the Classical era [1750-1820], the distinction between the modes blurs together. We wanted to see if we could nail down these differences more concretely, Harasim explains.

Machine listening (and learning)

The researchers used mathematical modelling to infer both the number and characteristics of modes in these five historical periods in Western classical music. Their work yielded novel data visualizations showing how musicians during the Renaissance period, like Giovanni Pierluigi da Palestrina, tended to use four modes, while the music of Baroque composers, like Johann Sebastian Bach, revolved around the major and minor modes. Interestingly, the researchers could identify no clear separation into modes of the complex music written by Late Romantic composers, like Franz Liszt.

Harasim explains that the DCMLs approach is unique because it is the first time that unlabelled data have been used to analyse modes. This means that the pieces of music in their dataset had not been previously categorized into modes by a human.

We wanted to know what it would look like if we gave the computer the chance to analyse the data without introducing human bias. So, we applied unsupervised machine learning methods, in which the computer 'listens' to the music and figures out these modes on its own, without metadata labels.

Although much more complex to execute, this unsupervised approach yielded especially interesting results which are, according to Harasim, more cognitively plausible with respect to how humans hear and interpret music.

We know that musical structure can be very complex and that musicians need years of training. But at the same time, humans learn about these structures unconsciously, just as a child learns a native language. Thats why we developed a simple model that reverse engineers this learning process, using a class of so-called Bayesian models that are used by cognitive scientists, so that we can also draw on their research.

From class project to publicationand beyond

Harasim notes with satisfaction that this study has its roots in a class project that he and his co-authors Moss and Ramirez did together as students in EPFL professor Robert Wests course, Applied Data Analysis. He hopes to take the project even further by applying their approach to other musical questions and genres.

For pieces within which modes change, it would be interesting to identify exactly at what point such changes occur. I would also like to apply the same methodology to jazz, which was the focus of my PhD dissertation because the tonality in jazz is much richer than just two modes.

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- Retracing the evolution of classical music with machine learning - Design Products & Applications

Travel Guard has specific cruise insurance policies, which makes it simpler than trying to find an add-on. If youre getting a quote online, theyll ask you to specify if youre taking a plane, a cruise, or both. They cover any emergency travel assistance, trip interruption, delay, or cancellation.

Cruise travel insurance secures non-refundable investments related to your trip. It reimburses you if you have to cancel your international cruise unexpectedly prior to your departure. It also provides medical coverage for unexpected injuries and illnesses. Cruise travel insurance policies provide medical coverage while you are on a holiday. A cancellation after this can mean a huge financial loss, but a cruise travel insurance policyholder shall be covered for cancellation or postponement of trips.

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Key players profiled in the report includes:

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Market Segmentation by type:

Market Segmentation by application:

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Global Cruise Travel Insurance Geographical Segmentation Includes:

North America (U.S., Canada, Mexico)

Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS)

Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific)

Latin America (Brazil, Rest of L.A.)

Middle East and Africa (Turkey, GCC, Rest of Middle East)

Some Major TOC Points:

Chapter 1. Report Overview

Chapter 2. Global Growth Trends

Chapter 3. Market Share by Key Players

Chapter 4. Breakdown Data by Type and Application

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Machine Learning and Artificial Intelligence in Healthcare Market 2021 inclining trends with NVIDIA Corporation, Intel Corporation, GENERAL ELECTRIC...

SINGAPORE -Media OutReach-5 February2021 -Despite majority of Singapore employers(89%) reporting that the COVID-19 pandemic has accelerated the adoption of cloudcomputing and Machine Learning (ML) in their companies, obstacles abound. Singaporebusiness leaders say that the largest hindrance to adopting cloud computing andML technologies is the shortage of relevant in-house IT support (64%), amongstother reasons such as 'employees do not have the relevant skill sets' (58%) and'the lack of financial resources' (46%).

alt="NTUC LearningHub Survey Reveals Accelerated Business Needs In Cloud Computing And Machine Learning Outpacing Singapore Talent Supply; Skills Gap A Hindrance To Implementing These Technologies"

These are some ofthe key findings from the recently launched NTUC LearningHub (NTUC LHUB)Industry Insights report on cloud computing and ML in Singapore. The report is basedon in-depth interviews with industry experts, such as Amazon Web Services (AWS)and NTUC LHUB, and a survey with 300 hiring managers across industries inSingapore.

While organisationsare keen to adopt cloud computing and ML to improve the company's businessperformance (64%), obtain business insights from Big Data (59%) and performmundane or tedious tasks (53%), a third of Singapore employers (32%) say theircompanies have insufficient talent to implement cloud computing and MLtechnologies.

To overcome thisshortage, companies say they have been upskilling employees that have relevantskill sets/ roles (55%), and reskilling employees that have completelydifferent skill sets/ roles (44%). In a further show of how organisations werewilling to take steps to overcome this skills gap, three in five (61%) stronglyagree or agree that they will be open to hiring individuals with relevantmicro-credentials, even if these candidates has no relevant experience oreducation degrees.

Looking to thefuture, four in five employers (81%) agree or strongly agree that ML will bethe most in-demand Artificial Intelligence (AI) skill in 2021. Meanwhile, sevenout of 10 surveyed (70%) indicated they will be willing to offer a premium fortalent with AI and ML skills.

"The report reinforces the growing demand for a cloud-skilled workforce inSingapore, and the critical need to upskill and reskill local talent", said TanLee Chew, Managing Director, ASEAN, Worldwide Public Sector, AWS. "Thecollaboration across government, businesses, education and traininginstitutions will be instrumental in helping Singapore employers address theseskills gaps. AWS will continue to collaborate with training providers like NTUCLearningHub to make skills training accessible to help Singaporeans, fromstudents to adult learners, to remain relevant today and prepare for the future."

NTUC LHUB's Head ofICT, Isa Nasser also adds, "While much of the talent demand encompasses technicalpositions such as data scientists and data engineers, businesses are alsolooking for staff to pick up practical ML and data science skills sets that canbe applied to their existing work. Thatis why in today's digital age, most professionals would benefit greatly frompicking up some data science skills to enable them to deploy ML applicationsand use cases in their organization. We highly urge workers to get started on equipping themselveswith ML skills, including understanding the core concepts of data science, aswell as familiarising themselves on the use of cloud or ML platforms such as AmazonSageMaker."

To download theIndustry Insights: Cloud Computing and ML report, visit

https://www.ntuclearninghub.com/machine-learning-cloud.

NTUCLearningHub is the leading Continuing Education and Training provider in Singapore,which aims to transform the lifelong employability of working people. Since ourcorporatisation in 2004, we have been working employers and individual learnersto provide learning solutions in areas such as Cloud, Infocomm Technology,Healthcare, Employability & Literacy, Business Excellence, Workplace Safety& Health, Security, Human Resources and Foreign Worker Training.

Todate, NTUC LearningHub has helped over 25,000 organisations and achieved over2.5 million training places across more than 500 courses with a pool of over460 certified trainers. As a Total Learning Solutions provider toorganisations, we also forge partnerships and offer a wide range of relevantend-to-end training solutions and work constantly to improve our trainingquality and delivery. In 2020, we have accelerated our foray into onlinelearning with our Virtual Live Classes and, through working with best-in-classpartners such as IBM, DuPont Sustainable Solutions and GO1, asynchronous onlinecourses.

For moreinformation, visitwww.ntuclearninghub.com.

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NTUC LearningHub Survey Reveals Accelerated Business Needs In Cloud Computing And Machine Learning Outpacing Singapore Talent Supply; Skills Gap A...