Harness AI for Real-Time Packet Inspection & Threat Discovery
Leverage AIEngine’s advanced detection engine to uncover threats, anomalies, and unknown patterns across high-speed traffic flows.
Overview
AIEngine is a high-performance, artificial intelligence-based network analyzer designed to detect threats, anomalies, and irregular traffic behaviors in real time. Unlike traditional rules-based systems, AIEngine builds a contextual understanding of traffic patterns and applies AI models to reveal emerging risks and zero-day exploits. At NetworkTestingTech, we help enterprises integrate AIEngine into their cybersecurity infrastructure to strengthen real-time detection, reduce noise, and gain actionable visibility across complex environments. Based in Phoenix, Arizona, we serve organizations across North America by providing industry-tailored configurations, continuous product refinement, and a strong support system. AIEngine is the ideal solution for organizations needing intelligent, adaptive, and lightweight analysis at network scale.
Core Components
Software
- AIEngine core engine with traffic learning, DNS parsing, and TLS fingerprinting
- CLI-based and scriptable configuration interface with Lua support
- Built-in traffic analyzers for protocols such as HTTP, DNS, SMTP, and FTP
Hardware
- High-performance NIC-enabled servers with multi-core CPUs
Best paired with Ethernet Analyzers to inspect, decode, and verify high-speed data transfers at the packet level. - SSD or NVMe storage for fast memory caching and real-time rule matching
Enhanced by Amplifiers In Transmission to maintain signal integrity and consistency in high-throughput environments. - Compact edge appliances for remote or distributed environments Compatible with CATV Meters & Instruments for diagnostics and performance monitoring at remote locations.
Cloud Services
- AIEngine container support for cloud-native deployment on AWS, GCP, Azure
- Scalable event streaming to cloud SIEMs via Kafka or syslog
- Remote policy management and AI model training through RESTful APIs
Key Features and Functionalities
- AI-assisted real-time traffic analysis using dynamic learning models
- Detection of malicious domains, tunnels, and anomalous behavior
- Lightweight design—no dependency on large rule sets or signature files
- Protocol fingerprinting, DNS anomaly detection, and TLS inspection
- Rich metadata generation for forensic and historical traffic review
Integrations
- Integration with Zeek, Suricata, and ELK for enriched SOC visibility
- Compatible with Kafka, syslog-ng, and Logstash for event forwarding
- API hooks for orchestration with SOAR platforms and SIEMs
Compatibility
- Linux-native tool (Ubuntu, CentOS, Fedora supported)
- Available as Docker container or compiled binary
- Compatible with physical, virtual, and container-based network environments
Benefits
- Intelligent detection of unknown or evasive threats
- Reduced reliance on signature updates and manual tuning
- Fast deployment and lightweight performance footprint
- Enhanced anomaly awareness through AI-based learning models
Applications
- AI-driven SOC threat detection
- Industrial control systems monitoring
- University campus-wide behavioral analytics
- Cloud-native network edge threat monitoring
Industries
- Telecommunications
- Higher Education
- Energy & Utilities
- Financial Services
- Government Cybersecurity
Relevant U.S. & Canadian Standards and Regulations
- NIST SP 800-94
- FISMA
- CISA Zero Trust Maturity Model
- PIPEDA
- NERC CIP
Case Studies
Case Study 1 – Telecom Provider (Denver, Colorado)
A telecom company with a growing fiber backbone partnered with NetworkTestingTech to deploy AIEngine at key peering points. The AI-based traffic analysis engine uncovered DNS tunneling activities and helped the security team build adaptive responses to zero-day threats.
Case Study 2 – Smart City Initiative (Chicago, Illinois)
As part of a broader IoT security initiative, a municipal cybersecurity team integrated AIEngine across traffic management networks. NetworkTestingTech supported anomaly policy development, helping detect malformed packet patterns during off-peak hours, reducing false positives.
Case Study 3 – Cloud Service Provider (Ottawa, Canada)
A Canadian CSP leveraged AIEngine to monitor east-west traffic between virtual machines. With our expert assistance, they implemented custom Lua scripts for dynamic alerting, improving detection rates for abnormal TLS session fingerprints and unauthorized DNS activity.
Ready to elevate your network visibility with real-time, AI-powered analysis?
Contact NetworkTestingTech to discuss how AIEngine can integrate into your environment. Our specialists are available to assist with tailored deployments, model optimization, and ongoing support.