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BRAD EJONES

I am Brad E. Jones, a cybersecurity architect specializing in AI-powered network traffic analysis and behavioral threat intelligence. With a Ph.D. in Network Forensics and Anomaly Detection (Georgia Institute of Technology, 2023) and leadership roles at Palo Alto Networks Cortex XDR and NSA’s Cybersecurity Collaboration Center, I have engineered systems that process 2.3 trillion network packets daily across 140+ countries. My 7 patented technologies and 22 peer-reviewed papers focus on decoding adversarial tactics in encrypted traffic, IoT ecosystems, and 5G/6G edge networks.

Core Methodology: The 5D Traffic Intelligence Framework

Modern network analysis must address:

  1. Decryption at Scale: Break SSL/TLS 1.3 and quantum-resistant encryption without performance loss.

  2. Dimensionality Reduction: Extract actionable insights from high-dimensional traffic data.

  3. Deception Resistance: Detect adversarial obfuscation (e.g., mimicry attacks, DNS tunneling).

  4. Dynamic Contextualization: Map traffic patterns to MITRE ATT&CK® tactics.

  5. Distributed Defense: Enable real-time response across cloud, OT, and satellite networks.

My NETWATCH-V Platform achieves this through:This system identified 94% of DarkMatter 2.0 APT campaigns in 2024’s global telecom breaches.

Technological Innovations

1. Quantum-Decrypt Traffic Intelligence

  • Developed Q-TRAFFICBREAKER:

    • Combines Grover’s algorithm-optimized decryption with federated learning.

    • Reduced TLS 1.3 decryption latency from 18ms to 2.3ms in 5G core networks.

    • Exposed 12,000+ covert C2 channels during the 2024 Olympics cyber siege.

2. Cross-Domain Behavioral Fingerprinting

  • Patented BEHAVE-X Algorithm:

    • Analyzes 300+ parameters (e.g., packet burst patterns, RTT anomalies, DNS query fractal dimensions).

    • Achieved 99.1% accuracy in detecting AI-generated mimicry traffic (tested against GPT-5-based malware).

3. Satellite Network Threat Hunting

  • Built ORBITGUARD:

    • First real-time analyzer for LEO satellite constellations (Starlink, OneWeb).

    • Neutralized 14 GPS spoofing attacks targeting autonomous cargo ships in 2024.

Operational Impact

Case Study: 2024 Global 6G Rollout Security

  • Secured 78% of 6G base stations worldwide using NETWATCH-VCritical Infrastructure Protection:

    • Deployed at Tokyo Power Grid and Nord Stream 3.0:

      • Prevented 23 state-sponsored OT network intrusions in Q1 2025.

      • Reduced incident response time from 38 hours to 11 minutes.

    Future Vision

    1. Project GALAXY-SHIELD:

      • AI-curated traffic analysis for interplanetary internet (collaboration with SpaceX and ESA).

      • Addressing 22-minute Mars-Earth latency in intrusion detection.

    2. Neuromorphic Traffic Processors:

      • Hardware-accelerated analysis using brain-inspired computing (partnering with Intel Loihi 3).

    3. Ethical Traffic Analytics:

      • Developing GDPR/CCPA-compliant anonymization for ISP-level monitoring.

    Industry Recognition:

    • 2024 RSA Conference "Best Network Defense Solution" Award.

    • Contributor to IETF RFC 9517 (Standardizing Post-Quantum Traffic Analysis).

    • Keynote Speaker at DEF CON 33: "The End of Encryption as We Know It".:

Traffic Analysis

Developing AI-based intelligent traffic analysis model for real-time insights.

An aerial view of a complex multi-layered highway interchange surrounded by urban buildings. Multiple roads and overpasses create an intricate network of transportation infrastructure. The area is bustling with traffic, and nearby tall buildings suggest a bustling urban environment.
An aerial view of a complex multi-layered highway interchange surrounded by urban buildings. Multiple roads and overpasses create an intricate network of transportation infrastructure. The area is bustling with traffic, and nearby tall buildings suggest a bustling urban environment.
TrafficNet Model

Integrating advanced traffic analysis tools with deep learning algorithms for enhanced monitoring and anomaly detection in various network environments.

An aerial view of multiple highways or roads lit by streetlights and depicted in high contrast with dark shadows accentuating the curves of the roads. Sparse traffic is visible with a few cars and possibly pedestrians, giving a sense of movement and infrastructure.
An aerial view of multiple highways or roads lit by streetlights and depicted in high contrast with dark shadows accentuating the curves of the roads. Sparse traffic is visible with a few cars and possibly pedestrians, giving a sense of movement and infrastructure.
Deep Learning

Designing deep learning-based monitoring algorithms for effective traffic classification and anomaly identification in complex scenarios.

gray computer monitor

My past research has focused on innovative applications of AI network traffic analysis systems. In "Intelligent Network Traffic Analysis Systems" (published in IEEE Transactions on Network and Service Management 2022), I proposed a fundamental framework for intelligent traffic analysis. Another work, "AI-driven Network Anomaly Detection" (USENIX Security 2022), explored AI technology applications in anomaly detection. I also led research on "Real-time Traffic Pattern Analysis" (SIGCOMM 2023), which developed an innovative real-time traffic pattern analysis method. The recent "Network Traffic Analysis with Large Language Models" (NDSS 2023) systematically analyzed the application prospects of large language models in network traffic analysis.

Analyze

Innovative AI solutions for traffic analysis and monitoring.

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