scholarly journals Mining Complex Network Data for Adaptive Intrusion Detection

10.5772/48308 ◽  
2012 ◽  
Author(s):  
Dewan Md. ◽  
Mohammad Zahidur ◽  
Chowdhury Mofizur
Keyword(s):  
Intrusion Detection ◽  
Complex Network ◽  
Network Data ◽  
Mining Complex

scholarly journals NOESIS: A Framework for Complex Network Data Analysis

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Víctor Martínez ◽  
Fernando Berzal ◽  
Juan-Carlos Cubero
Keyword(s):  
Data Mining ◽  
Open Source ◽  
Complex Network ◽  
Open Source Software ◽  
Network Data ◽  
Analysis Techniques ◽  
Software Analysis ◽  
Open Source Framework ◽  
Network Properties ◽  
Real World Problems

Network data mining has attracted a lot of attention since a large number of real-world problems have to deal with complex network data. In this paper, we present NOESIS, an open-source framework for network-based data mining. NOESIS features a large number of techniques and methods for the analysis of structural network properties, network visualization, community detection, link scoring, and link prediction. The proposed framework has been designed following solid design principles and exploits parallel computing using structured parallel programming. NOESIS also provides a stand-alone graphical user interface allowing the use of advanced software analysis techniques to users without prior programming experience. This framework is available under a BSD open-source software license.

scholarly journals Analysis and Applications of Location-Aware Big Complex Network Data

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-2 ◽  
Author(s):  
Jianxin Li ◽  
Ke Deng ◽  
Xin Huang ◽  
Jiajie Xu
Keyword(s):  
Complex Network ◽  
Network Data ◽  
Location Aware

Research and Implementation of Intrusion Detection Based Genetic Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 946-951 ◽  
Author(s):  
Guang Cai Cui ◽  
Bai Tong Liu
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Real Time ◽  
Original Data ◽  
Network Data ◽  
Initial Population ◽  
Classification Rules ◽  
Detection Technology ◽  
Network Intrusions ◽  
Data Source

For traditional intrusion detection technology, the lack of intelligent and self-adaptive has become increasingly prominent when they cope with unknown attacks. A method based on genetic algorithm was presented for discovering and learning the intrusion detection rules. This algorithm uses the network data packet as an original data source, after pretreatment, initialized them to be the initial population of the genetic algorithm, then derive the classification rules. These rules were used to detect or classify network intrusions in a real-time network environment, selecting the intrusion packets. The experiment proves the efficiency of the presented method.

Mining network data for intrusion detection through combining SVMs with ant colony networks

2014 ◽  
Vol 37 ◽  
pp. 127-140 ◽  
Author(s):  
Wenying Feng ◽  
Qinglei Zhang ◽  
Gongzhu Hu ◽  
Jimmy Xiangji Huang
Keyword(s):  
Intrusion Detection ◽  
Ant Colony ◽  
Network Data
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