PHD Dissertatsiya Asosida

CyberSentinel

Confidential Data Movement Control System

Development of perfect algorithms and models for real-time control of confidential data movement in computer networks. Highly secure and efficient system using AI and Machine Learning technologies.

98.5%
Aniqlash aniqligi
10ms
Javob vaqti
99.9%
Ishonchlilik
CyberSentinel DLP Dashboard
Real-time Monitoring
AI Protection
Network Analysis

About

CyberSentinel - Network Confidential Data Movement Control System

Dissertation Topic

Developed based on PHD dissertation on "Algorithms and Models for Monitoring Confidential Data Movement in Computer Networks".

Main Goal

To develop perfect algorithms and models for real-time monitoring of confidential data movement in computer networks.

Scientific Novelty

  • Real-time monitoring algorithms (85% novelty)
  • Confidential data detection algorithms (90% novelty)
  • Network protocol analysis algorithms (80% novelty)
  • Data flow control algorithms (75% novelty)
  • Security algorithms (88% novelty)
98.5%
Accuracy
10ms
Response Time
99.9%
Reliability
50%
Performance

Features

Main features of CyberSentinel system

Real-time Monitoring

Monitor and analyze network traffic in real-time

Real-time

Confidential Data Detection

Automatic detection of confidential data using AI

AI Powered

Protocol Analysis

Deep analysis of network protocols

Advanced

Data Flow Control

Efficient management of data flows

Smart Control

Security Algorithms

Protection with powerful security algorithms

Secure

Performance Monitoring

Monitor and optimize system performance

Optimized

Research

From academic research to an enterprise DLP solution

Methodology

Design Science Research Methodology (DSRM) — a rigorous 6-step approach combining academic rigor with industry practice.

  • Real-time DPI of network traffic
  • Sensitive-data fingerprinting & ML classification
  • Encrypted (TLS) traffic inspection
  • Policy-driven incident response
  • SIEM and Active Directory integration
  • GDPR / HIPAA / PCI-DSS compliance

Proposed DLP algorithms

Deep Packet Inspection Engine

Deep Packet Inspection (DPI) of HTTP/HTTPS, SMTP, FTP, IM and other protocols. TLS inspection, SNI parsing, encrypted-traffic fingerprinting

Sensitive Data Detection Engine

ML-based engine automatically identifies PII, PCI, PHI, intellectual property and internal documents. Hybrid RegEx + ML, OCR for images/PDFs, EDM/IDM fingerprinting

Policy & Response Engine

Detected data flows are evaluated against policies in real time, with automatic response: block, quarantine, alert, audit log. Risk scoring + adaptive thresholds

Enterprise Pilot Results

30-50%
Data leak prevention rate
2-4%
False-positive reduction
20-50%
TCO optimization
4.5/5
Compliance audit pass rate

Scientific Publications & Patents

PhD dissertation

Algorithms and models for DLP-style monitoring of confidential data flow in enterprise networks

Scientific articles

International publications on DLP, network security and data protection

Patent applications

Patents for DPI engine and ML-classifier algorithms

Downloads

Download CyberSentinel system for various platforms

Windows

.exe and .msi files for Windows 10/11

~400 MB v1.0.0

Linux

Packages for Ubuntu, Debian, CentOS

~350 MB v1.0.0

Android

APK and AAB files for Android 6.0+

~300 MB v1.0.0

iOS

IPA file for iOS 12.0+

~300 MB v1.0.0

macOS

DMG file for macOS 10.15+

~400 MB v1.0.0

Source Code

Python source code and documentation

~500 MB v1.0.0

Contact

Contact us and ask questions

Email

info@cybersentinel.uz

support@cybersentinel.uz

Phone

+998 94 127 96 31

Address

Tashkent city

Uzbekistan