AEGIS DLP: A Unified Endpoint Security Platform with Deep Learning-Based Threat Detection

By Ayush Gupta

Modern cyberattacks rarely occur in isolation. A single phishing email can trigger a chain of events-network intrusion, malware deployment, sensitive data access, and ultimately data exfiltration or ransomware. Aegis DLP presents a unified endpoint security platform that integrates multiple defensive capabilities into a single locally deployed system. The platform combines deep learning models, real-time monitoring, and an event-driven architecture to detect and correlate threats across multiple attack stages. This book documents the design and engineering of a complete Data Loss Prevention platform featuring nine integrated security modules including phishing detection, network intrusion detection, sensitive data classification, ransomware-aware file monitoring, device control, malware analysis, and a central correlation engine. Built using transformer models such as RoBERTa with LoRA fine-tuning, multi-dataset IDS training, and a real-time event bus architecture, the system demonstrates how modern AI techniques can enhance endpoint security while maintaining privacy through fully local execution. Aegis DLP provides a practical blueprint for building intelligent security platforms and offers insights into the challenges of integrating machine learning, system architecture, and cybersecurity engineering into a single unified solution.

• Format: Paperback
• Language: English
• ISBN: 9999339506
• ISBN13: 9789999339506
• Release Date: March 2026
• Publisher: Eliva Press
• Length: 61 Pages
• Weight: 0.21 lbs.
• Dimensions: 9.0" x 0.1" x 6.0"