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【分析レポート:技術】サイバーセキュリティ技術の機械学習

Machine Learning in Cybersecurity Technologies

1Q 2017 | Technology Analysis Report | AN-2312 | 28 pages | 2 tables | 2 charts | 3 figures | PDF

 

出版社 出版年月価格 ページ数図表数
ABI Research
ABIリサーチ
2017年2月お問い合わせください 28 7

サマリー

Machine learning holds the sheer potential to transform every digital system and service currently available, as well as greatly enhance data analytics capabilities. As a response to increasingly threatening and volatile cyber-threats, the cybersecurity industry is one of the latest sectors to heavily invest in machine learning applications. This report focuses on the security enterprise part of machine learning, analyzes emerging technologies, estimates magnitude of potential pitfalls, and delves into a wide spectrum of related technological concepts. These, among others, include machine learning archetypes and applications, deep learning, feature engineering, big data storage, behavioral analytics, client versus vendor side algorithm training, endpoint versus network data harvesting, comparisons versus security information and event management, and instructions to de-mystify machine learning in search of new implementations.



目次

  • 1. MACHINE LEARNING AT THE HEART OF NEW CYBERSECURITY OFFERINGS
    • 1.1. The "New" Weapon in the Cybersecurity Arsenal
    • 1.2. Methodology and Market Overview
    • 1.3. Machine Learning: Snake Oil 2.0
    • 1.4. Cybersecurity and Machine Learning: Looking Past the Hype
    • 1.5. Machine Learning Overview
    • 1.6. User & Entity Behavior Analytics
    • 1.7. Data Sources for Algorithm Training
  • 2. EMERGING TECHNOLOGIES & A GLIMPSE INTO THE FUTURE OF AI
    • 2.1. Predictive Counter-Measures and Deep Learning
    • 2.2. Cross-Group Comparisons (Peer Groups)
    • 2.3. Threat Surface Reduction
    • 2.4. Supervised vs Unsupervised Learning in Cybersecurity
    • 2.5. Machine Learning Implementations in Potentially Compromised Systems
    • 2.6. Flooding of the Data Lakes
    • 2.7. A Future of Collaboration or Survival?
  • 3. COMPANY PROFILES
    • 3.1. IBM
    • 3.2. Symantec
    • 3.3. Niara
    • 3.4. Splunk
    • 3.5. Sqreen
    • 3.6. Vectra Networks
    • 3.7. Gurucul
    • 3.8. StatusToday
    • 3.9. Trudera
    • 3.10. Deep Instinct
    • 3.11. SparkCognition
    • 3.12. Jask

 

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プレスリリース

[プレスリリース原文]

Machine Learning in Cybersecurity to Boost Big Data, Intelligence, and Analytics Spending to $96 Billion by 2021

London, United Kingdom - 30 Jan 2017

Cyber threats are an ever-present danger to global economies and are projected to surpass the trillion dollar mark in damages within the next year. As a result, the cybersecurity industry is investing heavily in machine learning in hopes of providing a more dynamic deterrent. ABI Research forecasts machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021. 

“We are in the midst of an artificial intelligence security revolution,” says Dimitrios Pavlakis, Industry Analyst at ABI Research. “This will drive machine learning solutions to soon emerge as the new norm beyond Security Information and Event Management, or SIEM, and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years.”

ABI Research finds the government and defense, banking, and technology market sectors to be the primary drivers and adopters of machine learning technologies. User and Entity Behavioral Analytics (UEBA) along with Deep Learning algorithm designs are emerging as the two most prominent technologies in cybersecurity offerings, especially in innovative hot tech startups. Established antivirus (AV) players in the market, such as Symantec, continue to transform some of their solutions from highly trained supervised models to unsupervised and semi-supervised ones in preparation of the constantly shifting threat variables.

SIEM’s log-based methods are expected to be separated altogether and integrated within different operations of UEBA, unsupervised, and deep learning solutions. Signature-based AV systems will be absorbed completely and comprise only a subsection of supervised machine learning models.

Enterprise-focused powerhouses like IBM will transform the way enterprises employ machine learning in every market sector, from healthcare to enterprise analytics to cybersecurity. Companies such as Gurucul, Niara, Splunk, StatusToday, Trudera, and Vectra Networks are attempting to take the lead in innovative applications of UEBA. Other market entrants like Deep Instinct and Spark Cognition are employing more feature-agnostic models, deep learning, and natural language processing.

“This radical transformation is already underway and is occurring as a response to the increasingly menacing nature of unknown threats and multiplicity of threat agents,” concludes Pavlakis. “The proliferation of machine learning is also causing an explosion of agile startups, such as JASK, focusing more on SIEM complementary network traffic analysis and even pioneering application protection such as Sqreen.”

These findings are from ABI Research’s Machine Learning in Cybersecurity Technologies report.

 

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