scholarly journals Information Theory and Data-Mining Techniques for Network Traffic Profiling for Intrusion Detection

2014 ◽  
Vol 02 (11) ◽  
pp. 24-30 ◽  
Author(s):  
Pablo Velarde-Alvarado ◽  
Rafael Martinez-Pelaez ◽  
Joel Ruiz-Ibarra ◽  
Victor Morales-Rocha
Keyword(s):  
Data Mining ◽  
Information Theory ◽  
Intrusion Detection ◽  
Network Traffic ◽  
Data Mining Techniques

Classification of Intrusion Detection Using Data Mining Techniques

2018 ◽  
pp. 753-764 ◽  
Author(s):  
Roma Sahani ◽  
Shatabdinalini ◽  
Chinmayee Rout ◽  
J. Chandrakanta Badajena ◽  
Ajay Kumar Jena ◽  
...  
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Data Mining Techniques ◽  
Using Data

Intrusion Detection in Smart Grid Using Data Mining Techniques

2018 ◽  
Author(s):  
Abdulhamit Subasi ◽  
Khloud Al-Marwani ◽  
Reem Alghamdi ◽  
Aisha Kwairanga ◽  
Saeed M. Qaisar ◽  
...  
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Smart Grid ◽  
Data Mining Techniques ◽  
Using Data

Spatial and Temporal Characterization of Network Traffic for Intrusion Detection Based on Information Theory

2018 ◽  
Vol 14 (2) ◽  
pp. 1-7
Author(s):  
Pablo Velarde Alvarado ◽  
Rafael Martinez Pelaez ◽  
Luis J. Mena-Camaré ◽  
Alberto M. Ochoa Brust ◽  
Efrain Moreno Garcia ◽  
...  
Keyword(s):  
Information Theory ◽  
Intrusion Detection ◽  
Network Traffic

CLUSTERING-BASED NETWORK INTRUSION DETECTION

2007 ◽  
Vol 14 (02) ◽  
pp. 169-187 ◽  
Author(s):  
SHI ZHONG ◽  
TAGHI M. KHOSHGOFTAAR ◽  
NAEEM SELIYA
Keyword(s):  
Data Mining ◽  
Network Security ◽  
Intrusion Detection ◽  
Unsupervised Learning ◽  
Network Traffic ◽  
Clustering Algorithms ◽  
Network Intrusion Detection ◽  
Learning Methods ◽  
High Detection Rate ◽  
Network Intrusion

Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection — a challenging task in network security. Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Classification-based data mining models for intrusion detection are often ineffective in dealing with dynamic changes in intrusion patterns and characteristics. Consequently, unsupervised learning methods have been given a closer look for network intrusion detection. We investigate multiple centroid-based unsupervised clustering algorithms for intrusion detection, and propose a simple yet effective self-labeling heuristic for detecting attack and normal clusters of network traffic audit data. The clustering algorithms investigated include, k-means, Mixture-Of-Spherical Gaussians, Self-Organizing Map, and Neural-Gas. The network traffic datasets provided by the DARPA 1998 offline intrusion detection project are used in our empirical investigation, which demonstrates the feasibility and promise of unsupervised learning methods for network intrusion detection. In addition, a comparative analysis shows the advantage of clustering-based methods over supervised classification techniques in identifying new or unseen attack types.

Sign in / Sign up

Export Citation Format

Share Document