scholarly journals A Hybrid Data Mining based Intrusion Detection System for Wireless Local Area Networks

2012 ◽  
Vol 49 (10) ◽  
pp. 19-28
Author(s):  
M. Moorthy ◽  
S. Sathiyabama
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Local Area Networks ◽  
Local Area ◽  
Wireless Local Area Networks ◽  
Hybrid Data

scholarly journals Optimal Performance for Intrusion Detection in WirelessLan Network Using Data MiningTechniques

2021 ◽  
Vol 12 (9) ◽  
Author(s):  
K. Raja, Et. al.
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Detection System ◽  
Local Area Networks ◽  
Local Area ◽  
Transport Layer ◽  
Area Network ◽  
Using Data

The objective of this paper is to identify the intruder of the wireless local area network based on the network and transport layer while accessing the internet within organizations and industries. The Intrusion detection system is the security that attempts to identify anomalies attributes who are trying to misuse a network without authorization and those who have legitimate access to the system but are abusing their privileges. The fact of the existing system deals with a firewall to protect and detect the unauthorized person using Wireless Local Area Network. Since the administrator may block or unblock the intruder based on the priority. This paper presents an enhanced framework, to detect and monitor the anomalies in the wireless sensor networks in an organization or an institution. The proposed approach to detect and filter the intruder in the wireless local area networks. Hence optimize the intrusion detection system in the particular organization or industries. The proposed IDS results are compared with the existing Decision Tree, Naive Bayes, and Random Forest algorithms.

Data Mining: A Bagged Decision Tree Classifier Algorithm For Ids Intrusion Detection System Based Attacks Classification

2021 ◽  
pp. 1826-1839
Author(s):  
Sandeep Adhikari, Dr. Sunita Chaudhary
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Tree Classifier

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.

Intrusion Detection Using Modern Techniques

2011 ◽  
pp. 259-273
Author(s):  
Tarum Bhaskar ◽  
Narasimha Kamath B.
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Intrusion Detection System ◽  
Rough Set Theory ◽  
Detection System ◽  
Mining Tools

Intrusion detection system (IDS) is now becoming an integral part of the network security infrastructure. Data mining tools are widely used for developing an IDS. However, this requires an ability to find the mapping from the input space to the output space with the help of available data. Rough sets and neural networks are the best known data mining tools to analyze data and help solve this problem. This chapter proposes a novel hybrid method to integrate rough set theory, genetic algorithm (GA), and artificial neural network. Our method consists of two stages: First, rough set theory is applied to find the reduced dataset. Second, the results are used as inputs for the neural network, where a GA-based learning approach is used to train the intrusion detection system. The method is characterized not only by using attribute reduction as a pre-processing technique of an artificial neural network but also by an improved learning algorithm. The effectiveness of the proposed method is demonstrated on the KDD cup data.

Study on Intrusion Detection System Based on Data Mining

2015 ◽  
Vol 713-715 ◽  
pp. 2499-2502
Author(s):  
Jiang Kun Mao ◽  
Fan Zhan
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Clustering Algorithm ◽  
Detection Efficiency ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Cluster Center ◽  
Security Technology ◽  
Learning Techniques

Intrusion detection system as a proactive network security technology, is necessary and reasonable to add a static defense. However, the traditional exceptions and errors detecting exist issues of leakage police, the false alarm rate or maintenance difficult. In this paper, The intrusion detection system based on data mining with statistics, machine learning techniques in the detection performance, robustness, self-adaptability has a great advantage. The system improves the K-means clustering algorithm, focus on solving two questions of the cluster center node selection and discriminating of clustering properties, the test shows that the system further enhance the detection efficiency of the system.

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