Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection

Algorithm 1

Scalable and adaptive learning approach for network intrusion detection.

	Input: original training dataset D
	Output: classification instance as attack or normal
	Use features selection and extract best features
	Train machine learning algorithms ML, where ML is machine learning
	Select best classifiers such as random forest (RF)
	Incorporate RF with D as KB, where KB is the knowledge base
	While (new instance == true)
	{
	Apply classifier RF,
	Get class of instance I, as attack or normal, where I is the classified instance
	For (I == true)
	{
	KB fetch classified instance I, where KB consists ML and D
	string comp=compare I with D
	If (comp is not true)
	{
	new instance is not added to D, where D is training dataset
	training dataset not updated
	}
	Else
	{
	new instance is not added to D, where D is training dataset
	training dataset is updated and ready for next training
	New pattern P is generated
	Applied for next classification
	}
	}
	}