Machine Learning in Cybersecurity Technologies
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.
Companies Mentioned
Table of Contents
- 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
