RIT Dubai · Collaborative academic project · Spring 2026

SmartMall AI Network Automation

A proof of concept that turns plain-English requests into explainable network plans, deployment actions, validation evidence, and closed-loop recovery.

Collaboration disclosure: This was a five-person group project. The team designed and built the system collaboratively; Yahya’s documented contribution centered on documentation and error handling. Group outcomes are identified as team results.

CourseNetwork Design & Performance

Team5 collaborators

Yahya’s contributionDocumentation + error handling

EnvironmentGNS3 proof of concept

Original abstract cover for the SmartMall AI network automation case study

01 / System overview

From intent to verified change.

The system combines a browser interface, an AI-assisted intent layer, Python automation, and an emulated Cisco network.

SmartMall AI pipeline from topology discovery and network intent to validation and deployment — Team artifact · System pipeline

01

Presentation

HTML, JavaScript, and CSS provide chat, controls, tenant records, live topology, and system feedback.

02

Intelligence

The Network Brain interprets requests, carries current topology context, and produces structured actions.

03

Automation

Python, Flask, Netmiko, and GNS3 coordinate configuration, validation, tenant lifecycle, and recovery.

02 / Network design

A segmented smart-mall baseline.

The proof of concept used five trust zones, router-on-a-stick inter-VLAN routing, OSPF, ACL policy, and QoS prioritization.

GNS3 topology with two routers, one distribution switch, and segmented smart-mall hosts — Team artifact · Emulated baseline topology

Policy model

Trust zones with explicit access boundaries.

Admin, hardware store, Wi-Fi, CCTV, and payment zones
OSPF Area 0 between the two routers
Extended ACLs enforcing zone-to-zone policy
QoS prioritizing payment traffic, then admin and CCTV
Docker hosts supporting ping, curl, and iperf3 tests

03 / Key workflows

Automation with reviewable evidence.

SmartMall Network Brain preparing the initial segmented network — Network Brain · Initial setup

Conversational onboarding of a fictional bank tenant — Dynamic tenant onboarding · Fictional Bank scenario

Closed-loop correction workflow collecting evidence and validating a proposed repair — Closed-loop correction · Evidence, diagnosis, delta fix, validation

Validation report displayed in the SmartMall dashboard — Audit surface · Validation and correction reports

04 / Interactive explainer

Detect. Explain. Recover.

This simplified frontend interaction demonstrates the closed-loop idea without connecting to the original lab.

CORE

ADMIN

PAYMENT

WI-FI

CCTV

Explainer status

Ready for validation

00:00.00Illustration initialized.

05 / Validation

Team results, stated with boundaries.

Before correction

7 / 10 pings

Passed after the Wi-Fi isolation ACL was deliberately removed.

After correction

9 / 10 pings

Passed after the closed-loop correction process.

Observed runtime

≈ 7 minutes

Reported for the demonstrated correction sequence.

Project scale

Proof of concept

Small emulated topology, not a commercial production network.

Automated ACL policy ping tests for the SmartMall topology — Team artifact · Automated policy validation

06 / Honest limitations

What the proof of concept did not solve.

Performance

Full validation could take about five minutes, with larger cycles reaching roughly fifteen minutes or more.

Concurrency

Simultaneous onboarding requests were not supported and could conflict over VLAN or port assignments.

Scope

The environment was small, IPv4-only, Cisco-focused, single-site, and lacked production RBAC.

AI reliability and cost

Generated ACLs sometimes differed from policy, and model calls were reported as comparatively expensive for the prototype.

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