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CyberNeuro-rt
Quantum Software's CyberNeuro-RT (CNRT) technology offering.
CyberNeuro-RT (CNRT) is an AI/ML-driven, highly scalable, real-time network defense and threat intelligence solution designed to handle HPC-scale computing systems and petabyte-scale data. It can retrain itself as novel threats emerge, support the most common attack types, and is deployed via a reliable and effective distributed system architecture. The offering is available for both on-premises and cloud deployments, supporting privacy-centric environments with at-the-edge deployment capabilities using neuromorphic processing. It leverages both supervised and unsupervised deep learning techniques for detecting novel systems
Cyberneuro-rt architecture
The architecture represents a cybersecurity platform, CyberNeuroRT, which integrates with various systems to provide threat intelligence and real-time threat detection. It processes data from multiple sources:
CyberNeuro-RT System Integration Diagram
The architecture depicted above represents a complex system integration setup for cybersecurity, specifically threat detection.It involves the following components and flows:
CyberNeuro-RT Network Integration
CyberNeuro-RT Dashboard
Dashboard
Visualize network traffic through a variety of charts, each equipped with a drill-down mechanism for in-depth investigation of network activities. The dashboard features flashcards, a GeoMap for traffic routing, a Threat Distribution chart, and charts for the top 5 traffic groups (such as source IP, destination IP, URL, protocol, countries, etc.). It also includes centralized threat notifications with tooltips, charts for traffic response times, and charts for unusual connections. Analysts have the capability to download any chart in multiple formats, including PNG, JPEG, CSV, etc., and can print charts according to user requirements.
Live Traffic Monitor
Visualize real-time network traffic to distinguish between anomalous (threat) and benign traffic through the inference of ML models using multiple behavior analyses. The threats on the page are categorized based on targeted countries and threat types. A GeoMap facilitates the analysis of traffic routes; clicking on each country displays the number of threats per threat class. Below the GeoMap, a table presents all traffic with the number of features, and each traffic entry allows redirection to a detailed page displaying all metadata. Live traffic is further classified into All Traffic, Benign Traffic, and Anomaly Traffic.
All Traffic: Displays both benign and anomalous traffic based on predictions from either Supervised or Unsupervised Machine Learning models.
Benign Traffic: Shows only benign traffic when predictions from both Supervised and Unsupervised Machine Learning models indicate the traffic as benign.
Anomaly Traffic: Shows only anomalous traffic if the prediction from either the Supervised or Unsupervised Machine Learning model indicates the traffic as anomalous.
Historical Traffic
This traffic analyzer displays all historical network traffic in a table and on a GeoMap, facilitated by advanced column search, full-text search, and wildcard search capabilities. Each entry in the table provides a link to a detailed page that showcases all related metadata. Furthermore, the system features manual threat updates, Email Notification, PDF export functionality, and traffic isolation options.
Threat Update
Analysts are permitted to manually update threats and their severity levels. Every manual update immediately triggers an email notification to the associated manager, outlining the updates along with a reference URL for instant access.
Email Notification
After a threat update, an email notification is sent to the associated manager, including the reference URL for the traffic analyzer for instant access. The table in the email body provides details such as traffic ID, updated threat type, comments, priority, date, etc.
Threat Report
Exporting to PDF allows for customization of the content based on user requirements. The report features a breakdown of attack types presented in a visually intuitive pie chart format. Additionally, it includes the total number of attacks and the attack data itself in a tabular format.
Traffic Isolation
Traffic Isolation enables the control or isolation of traffic through various operations listed below:
- Remote Shutdown: In the event that a threat has already begun to spread, an analyst or administrator can immediately mitigate the risk by shutting down the selected source or destination node, thus removing it from danger.
- Stopping Source Traffic: Depending on the suspicion ratio, an analyst can halt the traffic originating from a selected source IP, port, or both.
- Stopping Destination Traffic: Similarly, based on the suspicion ratio, traffic heading to a selected destination IP, port, or both can be stopped by an analyst.
- Traffic ReRouting: Each network typically includes a connected free-flow machine designed to maintain fake traffic. This allows an analyst to reroute all traffic to a free-flow machine, enabling traffic monitoring without impacting critical traffic.
- Custom Firewall Rule: If an analyst possesses technical expertise in firewall rules, they can write their custom rules to control the traffic via iptables.
Traffic Aggregation
Traffic aggregation groups the traffic by source and destination IP addresses. This method aggregates traffic from node to node, making it easier for analysts to evaluate. Useful visualizations, such as bar charts (on the left) and IP traffic wheel charts (on the right), aid users in identifying traffic patterns, malicious data sources, and common network attacks.
WHO WE ARE
Srini Vasan
President & CEO
Srini Vasan is a software entrepreneur with specialization in databases, ERP, Big Data, General Management, Social Media Analytics and Digital Marketing.
Aiming to derive valuable insights, he is currently involved in very large scale data analytics, data harvesting and open data projects like 800 million voter data in India, SEC database real-time data, US public school systems, Medicare/ Medicaid/ health insurance data and health systems data in 50 states.
As a SAP expert, he had implemented very large global SAP clients. He had sold his earlier publicly held IT companies to other public companies.Besides developing Big data start-ups, he is also advising other start-ups. He is a qualified Cost Accountant with Degrees in Math and Management degree from MIT Sloan School, Boston. He is a relentless networker and technologist with a keen eye on future. He sees limitless potential for positive change and runs a 501c3, Global Help for all Inc.
Aaron Goldberg
Vice President
For the past 15 years, Aaron Goldberg has been a freelance consultant working in Business Development, Strategic Partnerships and Sales & Marketing for ‘boot-strap’ start-ups of $1mm budgets or less.
Currently based in Silicon Valley, Goldberg is involved in multiple high-budget Technology Development Initiatives, enterprise-scale Customized Software Sales and Commodities Procurement, Refining & Brokerage.
Howard Messer
Director & Chief Financial Officer
Mr. Howard J. Messer, CPA, is Chief Financial Officer at Zuma360 Software, Inc.and a Member at The American Institute of Certified Public Accountants, and a Member at New Jersey Society of Certified Public Accountants.
Mr. Messer was previously employed as Audit Manager by Arthur Andersen LLP, Chief Financial Officer & Vice President by Compuflex Systems, Inc.
He received his undergraduate degree from the University of VirginiaLet's talk.
Contact
+81 90-25660661
2-4-10 Tokocho Moriguchi City
Osaka, 570-0035
