Visual dashboard for real-time network monitoring and anomaly detection system

Abstract

A real-time network monitoring and anomaly detection system comprising a) a data collection module configured to collect network traffic data from one or more network nodes; b) a data processing module configured to normalise and extract features from the collected data; c) an anomaly detection module that uses statistical methods and / or machine learning models to detect anomalous network behavior; (d) a real-time visual dashboard configured to display network activity and detected anomalies through interactive graphical elements; and (e) an alert management module configured to generate notifications based on anomaly detection results, f) wherein the system is further configured to adaptively improve the recognition accuracy based on user feedback and system learning.

Description

[0001] The present invention relates to the field of network security and monitoring systems. More specifically, it relates to real-time network traffic visualization, monitoring, and anomaly detection using intelligent dashboards. The system integrates data analytics and machine learning for proactive threat detection and network performance optimization.

[0002] With the rapid growth of digital infrastructure, organizations increasingly rely on complex and interconnected networks to manage operations, communications, and data flows. As these networks grow, so does the risk of cyber threats, unauthorized access, data breaches, and performance issues. Traditional network monitoring tools often rely on static, rule-based systems that are unable to detect evolving or complex threats in real time. Furthermore, most existing solutions lack intuitive visualization and real-time responsiveness, making it difficult for network administrators to proactively manage and secure their environments.

[0003] There is a growing need for intelligent systems that combine real-time data collection, advanced anomaly detection, and interactive visualization to provide end-to-end situational awareness of network health and security. By leveraging machine learning, automated alerts, and user-friendly dashboards, such systems can help reduce detection time, improve threat response, and increase overall network reliability. The present invention addresses these needs by providing an integrated, adaptive, and visually driven platform for monitoring network activity and detecting anomalies as they occur.

[0004] One goal of this disclosure is to enable real-time monitoring of network activities for immediate visibility.

[0005] Another objective of this disclosure is to detect anomalies and threats with high accuracy using AI / ML techniques.

[0006] Another objective of this disclosure is to reduce response time through immediate alerts and notifications.

[0007] Another objective of this disclosure is to provide intuitive visual dashboards for easy interpretation of the data.

[0008] Another goal of the present disclosure is the continuous improvement of recognition through adaptive learning mechanisms.

[0009] Another objective of this disclosure is to minimize false alarms by integrating user feedback.

[0010] Another objective of the present disclosure is to support seamless integration into the existing security infrastructure.

[0011] Another objective of this disclosure is to improve overall network security and operational efficiency.

[0012] The present invention relates to the continuous collection of live network data from various nodes, including routers, switches and endpoints, to ensure complete transparency.

[0013] Another embodiment of the present invention is the cleansing and transformation of raw network traffic into structured, analyzable data with enriched contextual metadata.

[0014] Another embodiment of the present invention uses rule-based logic and machine learning models to detect anomalies such as DDoS, malware, and insider threats.

[0015] Another embodiment of the present invention is to display network activity and threat insights in real time through user-friendly graphical visualizations and filters.

[0016] Another embodiment of the present invention is to generate instant alerts and notifications across multiple channels to enable administrators to respond quickly to threats.

[0017] Another embodiment of the present invention improves recognition accuracy over time through user feedback and continuous model improvement.

[0018] Another embodiment of the present invention enables easy integration into existing SIEM tools, firewalls and network infrastructures for holistic threat management.

[0019] Another embodiment of the present invention enables early detection and automatic or manual response actions to prevent damage from evolving cyber threats.

[0020] The present invention relates to a real-time network monitoring and anomaly detection system with intelligent visualization capabilities. It continuously collects and processes network traffic data from various sources.

[0021] Anomaly detection is performed using rule-based and machine learning models to accurately identify threats. Interactive dashboards display live network status, alerts, and insights for user-friendly monitoring. The system includes modules for data collection, processing, anomaly detection, visualization, alerting, and adaptive learning. Data acquisition and traffic recording module

[0022] This module is responsible for collecting real-time network traffic data from various sources such as routers, switches, servers, and endpoints. It uses packet sniffing, flow-based protocols (e.g., NetFlow, sFlow), and API integrations to continuously capture metadata and payloads. The data is preprocessed to extract key metrics such as IP addresses, port numbers, protocol types, bandwidth utilization, and connection duration. Data processing and feature extraction module

[0023] After capture, the raw network data is cleaned, normalized, and processed to extract relevant features. These include statistical summaries (mean, variance), session behavior, traffic patterns, and temporal characteristics. This module also performs data labeling (if applicable) and enriches the logs with geolocation, user identity, and device metadata to support contextual analysis. Anomaly detection module

[0024] This central analytics engine uses rule-based logic, statistical techniques, and machine learning algorithms (e.g., clustering, classification, deep learning) to detect unusual patterns or suspicious activity. It detects anomalies such as DDoS attacks, port scans, malware behavior, insider threats, and abnormal data transfers. Threshold-based alerts and anomaly scores are calculated in real time for each event. Real-time visualization dashboard module

[0025] The front-end interface provides a dynamic, user-friendly dashboard that visualizes live and historical network activity. Components include interactive charts, heat maps, network topology maps, and time-series charts. Users can filter traffic by protocol, IP, region, or anomaly severity. The dashboard offers drill-down capabilities for detailed forensic analysis and supports customizable alert views. Alert management and incident response module

[0026] When an anomaly is detected, this module generates real-time alerts via email, SMS, or integrated SIEM systems. It provides an incident triage interface that allows network administrators to classify, confirm, and escalate events. Integration with automated response tools (e.g., firewall updates, IP blocking) enables immediate remediation actions to mitigate the impact of the threat. System configuration, learning and feedback module

[0027] This module allows users to configure detection thresholds, update rule sets, train or retrain ML models, and adjust visualization parameters. It also collects user feedback on false positives / negatives to continuously improve detection accuracy. Over time, the system evolves through reinforcement learning and adaptive tuning to adapt to changing network behavior.

[0028] The invention is explained again below with reference to the figure. It shows: Fig. : the visual dashboard for real-time network monitoring and anomaly detection system.

[0029] Fig.illustrates the visual dashboard for real-time network monitoring and anomaly detection system. The system operates by continuously collecting and processing real-time network traffic data from various network nodes such as switches, routers, firewalls, and connected devices. This data is passed through a preprocessing pipeline that normalizes it and enriches it with contextual information such as geolocation, protocol type, and user / device identity. After preprocessing, the data is analyzed by an anomaly detection module that uses a combination of rule-based heuristics and machine learning models to detect deviations from normal behavior. These can include sudden spikes in traffic, unauthorized access attempts, malware communications, or any other form of network irregularity.

[0030] The analysis results are presented on an interactive visual dashboard that updates in real time, providing network administrators with a clear and intuitive overview of the system's health and performance. Users can monitor traffic patterns, track anomalies, drill down into specific events, and view alerts with their corresponding severity levels. The system also supports automated alerts and responses, allowing incidents to be detected and resolved more quickly. Furthermore, user feedback on detected events is fed into the system to refine the detection algorithms, thus improving the platform's accuracy and adaptability over time.

Claims

[1] A real-time network monitoring and anomaly detection system comprising a) a data collection module configured to collect network traffic data from one or more network nodes; b) a data processing module configured to normalise and extract features from the collected data; c) an anomaly detection module that uses statistical methods and / or machine learning models to detect anomalous network behavior; (d) a real-time visual dashboard configured to display network activity and detected anomalies through interactive graphical elements; and (e) an alert management module configured to generate notifications based on anomaly detection results, f) wherein the system is further configured to adaptively improve the recognition accuracy based on user feedback and system learning. [2] The system (100) of claim 1, wherein the data collection module collects data using flow-based protocols, including NetFlow, sFlow, or IPFIX. [3] The system (100) of claim 1, wherein the anomaly detection module uses unsupervised learning algorithms including clustering or autoencoders to detect zero-day threats. [4] The system (100) of claim 1, wherein the dashboard displays network topology, traffic heatmaps, and time series visualizations of traffic patterns. [5] The system (100) of claim 1, wherein the alarm management module is integrated with external security systems or SIEM platforms for incident escalation. [6] The system (100) of claim 1 further comprises a response automation module configured to trigger predefined actions, including blocking IPs or changing firewall rules, in response to detected threats. [7] The system (100) of claim 1, wherein the system incorporates user feedback into a continuous learning model to reduce false positives and improve the accuracy of anomaly detection over time.

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