A comprehensive security operation center based on big data analytics and threat intelligence

Abstract Big data analytics is the often complex process of examining large andvaried data sets to uncover information. The aim of this paper is to describe how Real TimeOperation Center structuring drilling data in an informative and systematic manner throughdigital solution that can help organizations make informed business decisions and leverage business value to deliver wells efficiently and effectively. Real Time Operation Center process of collecting largechunks of structured/unstructured data, segregating and analyzing it and discovering thepatterns and other useful business insights from it. The methods were based on structuringa detailed workflow, RACI, quality check list for every single process of the provision of real-timedrilling data and digitally transform into valuable information through robust auditableprocess, quality standards and sophisticated software. The paper will explain RTOC DataManagement System and how it helped the organization determining which data is relevantand can be analyzed to drive better business decisions in the future. The big data platform, in-house built-in software, andautomated dashboards have helped the company build the links between different assets,analyzing technical gaps, creating opportunities and moving away from manual data entry(e.g. Excel) which was causing data errors, disconnection between information and wastedworker hours due to inefficiency. These solutions leverage analytics and unlock the valuefrom data to enhance operational efficiency, drive performance and maximize profitability. As a result, the company has successfully delivered 160 wells in 2019 (6% higher than 2019 Business Plan and 10% higher than number of delivered wellsin 2018) more efficiently with 28.2 days per 10kft fornew wells (10% better than 2018), without compromising the well objectives and quality of the wells. Moreover, despite increasing complexity, the highest level ofconfidence on data analytics has permitted the company to go beyond their normaloperating envelop and set a major record for drilling the world's fifth longest well as amilestone in 2019.

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Advanced Threat Detection Based on Big Data Technologies

Deep Learning Innovations and Their Convergence With Big Data - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-3015-2.ch001 ◽

2018 ◽

pp. 1-19

Author(s):

Madhvaraj M. Shetty ◽

Manjaiah D. H.

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analytics ◽

Data Science ◽

Predictive Analytics ◽

Big Data Analytics ◽

Threat Detection ◽

Cyber Threats ◽

Threat Intelligence ◽

Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

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Advanced Threat Detection Based on Big Data Technologies

Cyber Warfare and Terrorism ◽

10.4018/978-1-7998-2466-4.ch049 ◽

2020 ◽

pp. 808-822

Author(s):

Madhvaraj M. Shetty ◽

Manjaiah D. H.

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analytics ◽

Data Science ◽

Predictive Analytics ◽

Big Data Analytics ◽

Threat Detection ◽

Cyber Threats ◽

Threat Intelligence ◽

Big Data Technologies

Today constant increase in number of cyber threats apparently shows that current countermeasures are not enough to defend it. With the help of huge generated data, big data brings transformative potential for various sectors. While many are using it for better operations, some of them are noticing that it can also be used for security by providing broader view of vulnerabilities and risks. Meanwhile, deep learning is coming up as a key role by providing predictive analytics solutions. Deep learning and big data analytics are becoming two high-focus of data science. Threat intelligence becoming more and more effective. Since it is based on how much data collected about active threats, this reason has taken many independent vendors into partnerships. In this chapter, we explore big data and big data analytics with its benefits. And we provide a brief overview of deep analytics and finally we present collaborative threat Detection. We also investigate some aspects of standards and key functions of it. We conclude by presenting benefits and challenges of collaborative threat detection.

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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