Pruning-based intrusion detection for maximizing the traffic management in Internet of Things.

Abstract

This work considers the problem of maximizing the number of packets to be classified by the network security system in programmable switches in Internet of Things. With the purpose of developing a lightweight security method for programming switches with limited computing resource, we present a neural-network-based intrusion detection model that combines with a neuron pruning method to achieve low model complexity without significant sacrifice in accuracy. Then, we formulate an integer linear programming (ILP) problem that maximizes the amount of monitored traffic by all switches under requirements of classification accuracy and computing resources. The optimization problem is considered in two cases: using and not using the neuron pruning (NP)-based models to show the benefits of the proposed lightweight architecture. The evaluation results show that NP-based models allow switches to manage more data traffic while satisfying given requirements of accuracy and computing resources.