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FYP-26-S1-09
The purpose of the project is to develop a virtualised dashboard system that allows a network administrator to easily monitor the network through the intrusion detection system (IDS) such as Snort, Kismet, Zeek(Bro), Suricata.
Project Mission
Project Mission
Generating structured insights to provide faster decision making from raw IDS alerts.
Modern Intrusion Detection Systems (IDS) such as Snort, Suricata, Zeek and Kismet generate large volumes of raw alerts that are difficult and time-consuming for network administrators to analyse manually.
Our Final Year Project proposes a lightweight, web-based Intrusion Detection Analysis System, LIA (Lightweight Intelligent Analytics), that centralises, analyses and visualises IDS alerts in a single interactive platform. Instead of replacing existing IDS engines, our system enhances their usability by transforming raw logs into actionable security insights.
Secure Your Network
Explore the platform to experience real-time IDS alert analysis, interactive dashboards and security event monitoring in a controlled environment.
Centralise IDS alert logs from multiple IDS tools
Centralise IDS alert logs from multiple IDS tools
Aggregate alert data generated by different IDS engines into a single platform, allowing administrators to view and analyse network security events without switching between tools or log formats.
Detect known threats and suspicious traffic using configurable rules
Detect known threats and suspicious traffic using configurable rules
Identify malicious or abnormal traffic patterns based on administrator-defined rules, thresholds and blacklists, enabling flexible detection that can be adapted to different network environments.
Applies data mining & machine learning for anomaly detection
Use analytical and lightweight machine learning techniques to identify unusual traffic behaviours that may not be captured by predefined rules, supporting deeper intrusion analysis.
Visualise alerts, trends and attack patterns in a web dashboard
Present processed IDS data through interactive charts, tables and timelines to help administrators quickly understand alert severity, frequency and attack trends.
Improve analysis efficiency for network administrators in small-scale or academic environments
Reduce manual log inspection effort by transforming raw IDS alerts into structured and visual insights, making security monitoring more efficient and accessible without enterprise SIEM complexity.
About Us
About Us
This project is a web-based intrusion detection analysis system designed to support network and security administrators in monitoring, analysing, and responding to security events. It focuses on simplifying the analysis of intrusion detection system (IDS) alerts generated by tools such as Suricata, Snort and Zeek, which often produce large volumes of raw log data.
Our goal is to provide a centralized and user-friendly environment where security-relevant information can be visualized, filtered, and investigated efficiently. By combining rule-based detection, basic anomaly analysis, threat prediction and interactive dashboards, the system helps users identify suspicious behaviour, understand traffic patterns and prioritize potential security threats.
Got Questions?
Got Questions?
Contact lia.support.team@gmail.com to for more information on the project, or find out more through our FAQ page below!
Trusted Integrations
Trusted Integrations
Technologies, platforms or services used and integrated into the project
Signature-Based Intrusion Detection
Real-Time Threat Monitoring
Collaborative Development Platform
Cloud Infrastructure Hosting
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