Contact person: Business Client

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Location: New Delhi, India

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"What to build

A real-time flow-based DDoS detector running at the SDN controller (northbound app) that flags suspicious flows/hosts and triggers prevention actions.

Detection should use features available in controller flow/stats (per-flow packet rate, byte rate, unique src IPs per dst, SYN rate, avg packet size, entropy of src IPs, flow duration).

Methods & models (practical choices)

Start with TabNet or tree-based models (LightGBM / RandomForest) for tabular flow features — TabNet shows strong results in SDN-VANET DDoS detection in recent work. Train offline first then deploy model inference in controller.
ScienceDirect

As a baseline implement simpler signature/statistical detectors (thresholds, moving-average + z-score, entropy rules) to compare.

For streaming detection consider light LSTM/CNN on short time windows (1–5s) if sequence features are useful.

Implementation pieces

Emulation: Mininet-WiFi + SUMO to produce mobility and wireless behavior; use Mininet-WiFi’s OpenFlow support so the controller sees realistic flows.
Mininet-WiFi

Controller: Ryu / ONOS / OpenDaylight — implement a northbound app that:

Pulls flow stats periodically (1s–5s).

Extracts sliding-window features.

Calls the ML model (local inference).

Installs mitigation flows (rate limit / drop / redirect to honeypot).
(OpenDaylight/ONOS docs/examples helpful for REST API usage.)
OpenDaylight Documentation

Datasets for training: Use CIC-IDS/CICFlowMeter features for general traffic patterns and augment with VANET-specific/synthetic datasets (see Objective 3). CIC-IDS2017 is commonly used for IDS training.
University of New Brunswick

Evaluation metrics

Detection: precision, recall, F1, AUC.

Operational: detection latency (time from attack start to flag), mitigation effectiveness (packets dropped, throughput restored), false positive rate (FP impacts on benign vehicles).

Controller overhead: CPU, memory, flow-table usage." (client-provided description)