A novel approach to ensemble MLP and random forest for network security

Abstract

The following paper provides a novel approach for Network Intrusion Detection System using Machine Learning and Deep Learning. This approach uses two MLP (Multi-Layer Perceptron) models one having 3 layers and other having 6 layers. Random Forest is also used for classification. These models are ensembled in such a way that the final accuracy is boosted and also the testing time is reduced. Researchers have implemented various ways for the ensemble of multiple models but we are using contradiction management concept to ensemble machine learning models. Contradiction Management concept means if two machine learning models are contradicting in their decisions (in our case 3-layer MLP and Random Forest), then the third model’s (6-layer MLP) decision is considered whose accuracy is higher than the previous models. The third model is only used for testing when the previous two models contradict in their decision because the testing time of third model is higher than the two previous models as the third model has complex architecture. This approach increased the final accuracy as ensemble of multiple models is done and also testing time has reduced. The novelty of this paper is the choice and the combination of the models for the purpose of Network security.

Machine learning for Internet of things anomaly detection under low-quality data

“…In recent years, it has been widely used in the field of anomaly detection. [25][26][27] Figure 8 shows a simple MLP network structure. MLP is a multi-layer neural network that can contain multiple hidden layers.…”

Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits

“…Like other neural network algorithms, MLP is built upon many neurons in which each neuron has its own specific weight [37]. In any case that one neuron is insufficient to explain the algorithm, MLP will be useful by providing multi-neurons [38,39]. Radial Basis Function (RBF) is another ML algorithm commonly used in plant sciences [40][41][42].…”