Automated Threat Management - in Real Time. Vectra Networks

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1 Automated Threat Management - in Real Time

2 Security investment has traditionally been in two areas Prevention Phase Active Phase Clean-up Phase Initial Infection Key assets found in the wild $$$$ $$$ $$ Firewalls IPS Proxies Sandboxes Attackers had free rein in breached networks for 205 days 1 SIEM analysis Forensic consultants $ Source: M-Trends

3 The cyber attacker blueprint 1 Gain privileged access to the network 2 Extend compromise across the network 3 Steal or destroy key assets Employees, partners Phishing Social engineering N Spread malware Elevate access Establish control N Find key assets Aggregate data Tunnel out of the network N W E W E W E S S S 3

4 Vectra detects all phases of an active cyber attack Standard C&C Botnet Monetization Initial Infection Custom C&C Internal Recon Lateral Movement Acquire Data Exfiltrate Data Custom C&C & RAT 4

5 Vectra augments existing security products PAYLOAD ANALYSIS FORENSICS Botnet Monetization Standard C&C Initial Infection Custom C&C Internal Recon Lateral Movement EVENT &Acquire LOG MANAGEMENTExfiltrate Data Data Custom C&C & RAT Security Information Event Management Vectra Networks 5

6 Focus on bad actions, not names Trying to name all bad things only ensures that you are always behind Use of data science and machine learning to expose the true purpose and impact of traffic Malicious behaviors remain consistent across platforms 3

7 Threat detection requires the right data Data source options Endpoint agents Incomplete coverage Lack of context beyond the device Data Quality and Speed Endpoint agents NetFlow Network Coverage Network traffic SIEM & logs NetFlow Volume-based traffic monitoring Optimized for performance Lacks details for threat detection SIEM and logs Relies on 2 nd hand summaries Network traffic analysis Full real-time access to real traffic Context across time & all devices 7

8 How we automatically detect breaches in real time All packets - passive Behavioral N-S, E-W traffic Machine learning Any OS, app, device Continuous Monitoring Real-time Detection Correlated over time Centralized Learning No signatures Correlated by host No policies Rapid installation Automation Simplicity Prioritized Contextual Results Prioritized by risk Insight into attack 8

9 Software Architecture Reporting & 3rd-party Integration Host Correlation and Scoring Command & Control Botnet Activity Reconnaissance Lateral Movement Exfiltration Flow Assembler & Metadata Extractor Appliance Hardware or Virtual Machine 9

10 Full cyber security visibility Remote Site Sensors Internet Data Center Core Switch Perimeter devices have N-S visibility only Brain / Head-End Packet Capture (24 x 7) Analytic Unifying DB > WebUI Access Switch Sensors Packet Capture (24 x 7) Meta-Data Creation & Forward (0,5% of initial traffic) 10

11 Existing security tools enlarge your haystack A larger amount of possible events are time consuming in analysis Not necessarily lead to better security 11

12 Solution: analyze your haystack and find the needles Automated analysis of events Correlation of events Visibility per host From reactive to active security Improved allocation of specialists resources Out of the box - without days of complex setup Automated analytics of events Allowing Security Analysts to spend their time working on solutions Consulting instead of tedious research 12

13 Use cases: Goals, Benefits Values (PoV) Automation Improve SecOps efficiency by augmenting human capabilities with machine-based learning APT Detect APT early and often BYOD / IOT Get visibility for any device, OS or app SecOp's Threat Management Focus on bad actions, not names Visibility INSIDE Monitor N-S & E-W traffic 13

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