An intrusion detection mechanism based on feature based data clustering

Author(s):  
Debasish Das ◽  
Utpal Sharma ◽  
D. K. Bhattacharyya
2010 ◽  
Vol 29 (8) ◽  
pp. 859-874 ◽  
Author(s):  
Ioanna Kantzavelou ◽  
Sokratis Katsikas

The distributed computing is the buzz in recent past, cloud computing stands first in this category. This is since, the users can adapt anything related to data storage, magnificent computing facilities on a system with less infrastructure from anywhere at any time. On other dimension such public and private cloud computing strategies would also attracts the foul players to perform intrusion practices. This is since, the comfortability that the cloud platform providing to end users intends them to adapt these services in regard to save or compute the sensitive data. The scope of vulnerability to breach the data or services over cloud computing is more frequent and easier, which is since, these services relies on internet protocol. In this regard, the research in intrusion detection defense mechanisms is having prominent scope. This manuscript, projecting a novel intrusion detection mechanism called "calibration factors-based intrusion detection (CFID)" for cloud computing networks. The experimental study portrayed the significant scope of the proposal CFID to detect the intrusion activities listed as remoteto-Local, Port Scanning, and Virtual-Machine-Trapping.


Author(s):  
Sudarshan Nandy ◽  
Mainak Adhikari ◽  
Mohammad Ayoub Khan ◽  
Varun G Menon ◽  
Sandeep Verma

Author(s):  
Wilson Wong

Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However, the applicability of existing data clustering approaches to a wider range of applications is limited due to issues such as complexity involved in semantic computation, long pre-processing time required for feature preparation, and poor extensibility of semantic measurement due to non-incremental feature source. This chapter first summarises the many commonly used clustering algorithms and feature-based semantic measurements, and then highlights the shortcomings to make way for the proposal of an adaptive clustering approach based on featureless semantic measurements. The chapter concludes with experiments demonstrating the performance and wide applicability of the proposed clustering approach.


2020 ◽  
Vol 97 ◽  
pp. 101984 ◽  
Author(s):  
Dongzi Jin ◽  
Yiqin Lu ◽  
Jiancheng Qin ◽  
Zhe Cheng ◽  
Zhongshu Mao

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