Network Intrusion Detection System
An ML-based IDS that classifies network flows as normal or attack traffic and raises prioritised alerts.
Project overview
The system reads network flow records, extracts statistical features per connection, and classifies each flow as benign or as one of several attack categories. Alerts are grouped and prioritised so an operator sees incidents rather than thousands of individual rows.
Working with a research dataset such as NSL-KDD or a CICIDS release keeps the project legal and reproducible while still exercising real detection engineering.
Problem statement
Signature-based detection only catches attacks that have already been catalogued. Novel or modified attacks pass through, while high alert volume causes real incidents to be missed among false positives.
Proposed solution
Train supervised classifiers on labelled flow data for known attack classes, and add an unsupervised anomaly detector to catch traffic that matches no known class. Present detections through a monitoring dashboard with severity, affected host and recommended action.
Key features
- Flow feature extraction from packet captures or dataset records
- Multi-class classification across attack categories
- Anomaly detection branch for unseen behaviour
- Alert aggregation so one incident is not reported a hundred times
- Severity scoring based on attack class and target
- Live monitoring dashboard with a rolling traffic view
- Exportable incident log for the report
Technology stack
Traffic
Models
Backend
Dashboard
System architecture
The flow below is what goes into your architecture diagram and the corresponding chapter of the report.
- Capture stage reads a live interface or replays a stored packet capture
- Flow assembly groups packets into bidirectional connections
- Feature stage computes duration, byte counts, flag ratios and rate statistics
- Supervised classifier assigns an attack class or benign label
- Anomaly detector scores flows that the classifier is uncertain about
- Alerting stage aggregates, scores severity and pushes to the dashboard
Expected output
- Classified flows with attack category and confidence
- Prioritised incident list with affected hosts
- Detection rate and false positive rate per attack class
- Live traffic dashboard for the demonstration
Suitable branches, difficulty and duration
Documentation, review, demo and viva support
Documentation support
Abstract, problem statement, literature survey guidance, system architecture, ER and UML diagrams, module descriptions, test cases and screenshots — prepared in your department format.
Project review support
Delivery is split across your review calendar so review one has a design and plan, review two has working modules, and the final review has the integrated, tested system.
Demo support
A rehearsed demo path, seeded sample data, a reset script, and a recorded walkthrough kept as a backup in case something fails in the review room.
Viva preparation
A written question bank for this domain, a code walkthrough session, and practice at explaining each module in your own words — because the examiner will ask you.
Customisation options
Colleges reject duplicate topics, so most projects are adjusted before we start. These are the usual directions for this one.
- Focus on a single attack family such as DDoS for a deeper analysis
- Add automated blocking through firewall rules in a lab environment
- Compare a deep learning model against the classical baselines
- Add a report generator that summarises incidents for a daily briefing
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Enquire about the Network Intrusion Detection System project
Send your branch, college and first review date. You will get confirmation of scope, what the customisation would look like, and a delivery plan mapped to your reviews.