Weigh-in-Motion: Lightweight Real-Time Identification of Gbps Wireless Traffic.

Area of Science:

• Computer Science
• Network Engineering

Background:

• Distinguishing wireless and wired network traffic is crucial for security and Quality of Service (QoS).
• Traditional methods rely on slower wireless link speeds, an assumption now outdated by high-speed Wi-Fi (e.g., 802.11ac/ax).

Purpose of the Study:

• To develop a method for identifying high-speed Wi-Fi traffic in network middleboxes.
• To address the limitations of existing methods that fail as wireless speeds approach wired speeds.

Main Methods:

• Introduced Weigh-in-Motion, a lightweight online detection scheme.
• Utilized a novel concept called ACKBunch to capture unique high-speed Wi-Fi traffic characteristics.

Main Results:

• The proposed scheme accurately identifies wireless traffic from/to Gigabit 802.11 devices.
• Demonstrated effectiveness through extensive real-world experiments.

Conclusions:

• Weigh-in-Motion effectively distinguishes high-speed Wi-Fi traffic from wired traffic.
• Provides a viable solution for network middleboxes managing modern high-capacity wireless networks.