Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection

by   Dewan Md. Farid, et al.

In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.


Hybrid Model For Intrusion Detection Systems

With the increasing number of new attacks on ever growing network traffi...

A Novel Hierarchical Intrusion Detection System based on Decision Tree and Rules-based Models

This paper proposes a novel intrusion detection system (IDS) that combin...

Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System

Sparse events, such as malign attacks in real-time network traffic, have...

Base-Rate Fallacy Redux and a Deep Dive Review in Cybersecurity

This paper examines the current state of the science underlying cybersec...

Can process mining help in anomaly-based intrusion detection?

In this paper, we consider the naive applications of process mining in n...

An Autonomous Intrusion Detection System Using Ensemble of Advanced Learners

An intrusion detection system (IDS) is a vital security component of mod...