Enhance Network Intrusion Detection System by Exploiting BR Algorithm as an Optimal Feature Selection

Abstract

This chapter aims to build a proposed Wire/Wireless Network Intrusion Detection System (WWNIDS) to detect intrusions and consider many of modern attacks which are not taken in account previously. The proposal WWNIDS treat intrusion detection with just intrinsic features but not all of them. The dataset of WWNIDS will consist of two parts; first part will be wire network dataset which has been constructed from KDD'99 that has 41 features with some modifications to produce the proposed dataset that called modern KDD and to be reliable in detecting intrusion by suggesting three additional features. The second part will be building wireless network dataset by collecting thousands of sessions (normal and intrusion); this proposed dataset is called Constructed Wireless Data Set (CWDS). The preprocessing process will be done on the two datasets (KDD & CWDS) to eliminate some problems that affect the detection of intrusion such as noise, missing values and duplication.

Intrusion Detection System Based on Ada boosting and Bagging Algorithm

“…Due to the consequences of rising security threats nowadays, Network Intrusion Detection Systems (NIDS) have become the most crucial component of modern network infrastructure. Although the Intrusion Detection System (IDS) generates a lot of alarms, it uses algorithmic processes to limit false positives [7], [8], and [9]. Ensemble learning is a machine learning technique that entails teaching a bunch of weak learners (models) to solve a problem and then combining their findings to get superior results.…”

Network Intrusion Detection with a Hashing Based Apriori Algorithm Using Hadoop MapReduce

“…Kola Sujatha et al [21] used a combination of SVM, fuzzy logic and genetic network programming (GNP) to create rules to detect the network intrusions. Hashem et al [22] used the Bee Ranker (BR) algorithm based on the foraging behavior of honeybees for selection of features useful for detection of network intrusions. Gao et al [23] combined an adaptive principal component (A-PCA) for adaptive selection of network traffic features, and incremental extreme learning machine (I-ELM) for intrusion detection.…”