INTELLIGENT DEEP LEARNING FRAMEWORK FOR DYNAMIC TRAFFIC MANAGEMENT AND ANOMALY DETECTION IN NEXT-GENERATION COMPUTER NETWORKS

Abstract

This invention presents an intelligent deep learning framework designed to optimize traffic management and detect anomalies in next-generation computer networks. By leveraging advanced machine learning techniques, the system adapts to real-time network conditions, improves resource allocation, and enhances security through proactive anomaly detection. This invention provides a deep learning-based framework for real-time traffic management and anomaly detection in next-generation computer networks. The system leverages advanced deep neural networks to dynamically monitor, analyze, and optimize network traffic while identifying and mitigating anomalies, such as cyber threats or performance bottlenecks. By utilizing adaptive machine learning algorithms, this framework continuously learns from historical and real-time data to improve its accuracy and responsiveness. The invention incorporates predictive analytics to forecast network congestion and potential failures, enabling proactive adjustments and load balancing. This intelligent, automated approach enhances network performance, reduces latency, and ensures a resilient and secure network environment, ideal for use in complex and large-scale networks including 5G, IoT, and data center infrastructures.

Specification

Description:This invention introduces a framework that integrates deep learning models with dynamic traffic management systems to:
1. Analyze network traffic patterns.
2. Predict traffic surges and potential bottlenecks.
3. Detect anomalies indicative of security threats or network failures.
1. System Architecture
? Components:
? Data Collection Module: Gathers data from various network points (routers, switches, etc.).
? Preprocessing Module: Cleans and normalizes data, preparing it for analysis.
? Deep Learning Engine: Utilizes neural networks (e.g., CNNs, RNNs) to analyze traffic patterns and detect anomalies.
? Decision-Making Module: Implements algorithms for real-time traffic management and resource allocation.
2. Deep Learning Models
? Model Selection: Describe the specific architectures used (e.g., Long Short-Term Memory (LSTM) networks for time-series prediction).
? Training Process:
? Data Sources: Historical network traffic data, labeled datasets for anomaly detection.
? Training Techniques: Supervised and unsupervised learning methods to enhance model accuracy.
3. Dynamic Traffic Management
? Traffic Prediction: Utilize deep learning models to forecast traffic loads based on historical data and current trends.
? Resource Allocation: Algorithms to dynamically allocate bandwidth and prioritize critical applications based on predicted traffic.
4. Anomaly Detection
? Detection Techniques:
? Implement threshold-based and machine learning-based anomaly detection.
? Real-time alerts for network administrators when anomalies are detected.
? False Positive Reduction: Techniques to minimize false alarms using refined models and feedback loops.
Implementation
? Deployment: Outline how the framework can be deployed in existing network infrastructure.
? Integration: Describe compatibility with existing network management tools and protocols (e.g., SNMP, NetFlow).
Advantages
? Real-Time Adaptability: Enhances the ability to respond to changing network conditions dynamically.
? Improved Security: Proactive anomaly detection helps mitigate potential threats before they impact the network.
? Resource Efficiency: Optimizes bandwidth utilization, leading to cost savings and improved performance.
, Claims:We claim that,
1. Claim 1: A system for dynamic traffic management and anomaly detection utilizing deep learning models, comprising:
? A data collection module for gathering real-time traffic data.
? A preprocessing module for normalizing the collected data.
? A deep learning engine for analyzing traffic patterns and detecting anomalies.
? A decision-making module for implementing real-time traffic management strategies.
2. Claim 2: The system of claim 1, wherein the deep learning engine employs LSTM networks for traffic prediction.
3. Claim 3: The system of claim 1, further comprising an alert system that notifies network administrators of detected anomalies.
4. Claim 4: A method for optimizing bandwidth allocation based on predicted traffic loads using the system described in claim 1.

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