scholarly journals A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network

2017 ◽  
Author(s):  
Bayu Adhi Tama ◽  
Kyung-Hyune Rhee
Keyword(s):  
Intrusion Detection ◽  
Detailed Analysis ◽  
Wireless Network ◽  
Classifier Ensembles

Intrusion Detection for Wireless Network

2007 ◽  
pp. 505-533
Author(s):  
Cynthia Kersey ◽  
Jeffrey Tsai ◽  
Zhenwei Yu
Keyword(s):  
Intrusion Detection ◽  
Wireless Network

A Study on Password Strength in Wireless Encryption Protocols

2019 ◽  
Vol 8 (2) ◽  
pp. 21-26
Author(s):  
Gabriel PETRICĂ
Keyword(s):  
Operating System ◽  
Wireless Networks ◽  
Network Security ◽  
Intrusion Detection ◽  
Wireless Network ◽  
Data Encryption ◽  
The Internet ◽  
Wireless Network Security ◽  
Password Strength

Solutions that can be implemented to secure a LAN include firewalls and intrusion detection / prevention systems (IDS / IPS). For a wireless network, security is a challenge considering the specific elements of this type of network: the physical area from which the connection is possible, and the weaknesses of the protocols used for data encryption. This article presents a case study on the most widely used protocols (WEP, WPA and WPA2) to secure wireless networks and the methodology by which passwords can be decrypted using Kali Linux distribution - available for free on the Internet - and applications included in this operating system.

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

2015 ◽  
pp. 17-32 ◽  
Author(s):  
Soukaena Hassan Hashem
Keyword(s):  
Intrusion Detection ◽  
Wireless Network ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Wireless Data ◽  
Data Set ◽  
Network Intrusion ◽  
Network Intrusion Detection System

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.

scholarly journals An Improved K -Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bin Xie ◽  
Xinyu Dong ◽  
Changguang Wang
Keyword(s):  
Wireless Networks ◽  
Intrusion Detection ◽  
Wireless Network ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Network Data ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set ◽  
Network Intrusion

The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k -means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k -means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.

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