Detection of IoT based DDoS Attacks by Network Traffic Analysis using Feedforward Neural Networks

In this paper an optimized feedforward neural network model is proposed for detection of IoT based DDoS attacks by network traffic analysis aimed towards a specific target which could be constantly monitored by a tap. The proposed model is applicable for DoS and DDoS attacks which consist of TCP, UDP and HTTP flood and also against keylogging, data exfiltration, OS fingerprint and service scan activities. It simply differentiates such kind of network traffic from normal network flows. The neural network uses Adam optimization as a solver and the hyperbolic tangent activation function in all neurons from a single hidden layer. The number of hidden neurons could be varied, depending on targeted accuracy and processing speed. Testing over the Bot IoT dataset reveals that developed models are applicable using 8 or 10 features and achieved discrimination error of 4.91.10-3%.