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The IoT's vulnerability to network attacks has motivated the design ofintrusion detection schemes (IDS) using Machine Learning (ML), with a lowcomputational cost for online detection but intensive offline learning. SuchIDS can have high attack detection accuracy and are easily installed on serversthat communicate with IoT devices. However, they are seldom evaluated inrealistic operational conditions where IDS processing may be held up by thesystem overload created by attacks. Thus we first present an experimental studyof UDP Flood Attacks on a Local Area Network Test-Bed, where the first line ofdefence is an accurate IDS using an Auto-Associative Dense Random NeuralNetwork. The experiments reveal that during severe attacks, the packet andprotocol management software overloads the multi-core server, and paralyses IDSdetection. We therefore propose and experimentally evaluate an IDS design wheredecisions are made from a very small number of incoming packets, so thatattacking traffic is dropped within milli-seconds after an attack begins andthe paralysing effect of congestion is avoided.

Measurement Based Evaluation and Mitigation of Flood Attacks on a LAN Test-Bed