EdgeGuard: IoT Intrusion Detection

• 

  safety first

Inspiration

As smart homes and small businesses increasingly rely on interconnected devices, the attack surface for cyber threats has expanded exponentially. Traditional cloud-based Intrusion Detection Systems (IDS) often suffer from latency issues and raise significant data privacy concerns, as sensitive network traffic must be sent off-site for analysis. I was inspired to create EdgeGuard, a localized solution that brings advanced deep learning directly to the edge, ensuring immediate response times and keeping data on-premises.

What it does

EdgeGuard acts as an intelligent sentinel for your local network. Running entirely on edge hardware, it continuously monitors incoming and outgoing network packets. Instead of relying purely on signature-based detection (which struggles with zero-day attacks), it utilizes a lightweight Deep Learning model to identify anomalous behavioral patterns in real-time, instantly flagging potential intrusions before they can compromise the network.

How we built it

The system is built around a localized architecture to maximize efficiency:

Hardware: Deployed on a Raspberry Pi 4 to serve as a low-cost, low-power edge node.

Data Ingestion: Used Python-based packet sniffing libraries to capture real-time network traffic features (like packet size, time intervals, and protocols).

AI Engine: Trained a lightweight machine learning model. To ensure it runs smoothly on the Raspberry Pi without overheating, the model was optimized to use fewer resources.

Detection Logic: The anomaly detection simply looks at the incoming network traffic features and compares them against a dynamic safety limit. If the system detects a pattern of traffic that crosses this limit, it immediately flags it as an intrusion.

Challenges we ran into

The biggest hurdle was balancing the AI's accuracy with the Raspberry Pi's hardware limits. AI models usually need a lot of computing power. Making the model run fast enough on a small device—without freezing the CPU—required a lot of testing and reducing unnecessary data features. Additionally, preventing the system from triggering false alarms (like mistaking a normal, heavy video stream for a cyber attack) took a lot of careful adjustments to the detection limits.

Accomplishments that we're proud of

learned more about the software and the model used and basic underlaying security protocol and how things actually work and problems people face

What we learned

I gained hands-on experience bridging the gap between artificial intelligence and hardware design. Specifically, I learned how to deploy and optimize deep learning models for edge computing, handle real-time network data streams, and build resilient systems that don't rely on constant internet connectivity to function.

What's next for EdgeGuard: IoT Intrusion Detection

The next step is to implement automated mitigation—allowing EdgeGuard not just to detect an intrusion, but to actively manipulate router firewall rules to block malicious IP addresses instantly.

Built With

• cybersecurity
• deep-learning
• edge-ai
• networking
• python
• raspberry-pi
• tensorflow-lite