A Kind of Botnet Detection Method Based on State Transition of Zombie

2012 ◽  
pp. 1082-1089
Author(s):  
Li hua Jiang ◽  
Wan gang Wang
Keyword(s):  
State Transition ◽  
Detection Method ◽  
Botnet Detection

Detection Method for a Local Normal-State Transition in a No-Insulation REBCO Pancake Coil

2017 ◽  
Vol 27 (4) ◽  
pp. 1-6 ◽  
Author(s):  
Tao Wang ◽  
Kazuki Katsumata ◽  
Atsushi Ishiyama ◽  
So Noguchi
Keyword(s):  
Normal State ◽  
State Transition ◽  
Detection Method ◽  
Pancake Coil

scholarly journals A Lightweight Hybrid Detection Method for Botnet

2021 ◽  
Vol 15 ◽  
pp. 960-969
Author(s):  
Wei Ma ◽  
Xing Wang ◽  
Jiguang Wang ◽  
Qianyun Chen
Keyword(s):  
Detection Method ◽  
The Internet ◽  
Botnet Detection ◽  
Domain Names ◽  
Data Packets ◽  
Computational Overhead ◽  
Signature Detection ◽  
Hybrid Detection ◽  
Domain Name Server ◽  
Great Damage

Botnet is a serious threat for the Internet and it has created great damage to the Internet. How to detect botnet has become an ongoing endeavor research. Series of methods have been discussed in recent research. However, one of the remaining challenges is that the high computational overhead. In this paper, a lightweight hybrid botnet detection method is proposed. Considering the features in the botnet data packets and the characteristic of employing DGA (Domain Generation Algorithm) domain names to connect to the botnet, two sensors are designed and deployed individually and parallelly. Signature detection is used on the gateway sensor to dig out known bot software and deep learning based techniques are used on the DNS (Domain Name Server) server sensor to find DGA domain names. With this method, the computational overhead would be shared by the two sensors and experiments are conducted and the results indicate that the method is effective in detecting botnet

PsyBoG: A scalable botnet detection method for large-scale DNS traffic

2016 ◽  
Vol 97 ◽  
pp. 48-73 ◽  
Author(s):  
Jonghoon Kwon ◽  
Jehyun Lee ◽  
Heejo Lee ◽  
Adrian Perrig
Keyword(s):  
Large Scale ◽  
Detection Method ◽  
Botnet Detection

A botnet detection method based on FARIMA and hill-climbing algorithm

2018 ◽  
Vol 32 (32) ◽  
pp. 1850356
Author(s):  
Huang Kun ◽  
Wu Jun
Keyword(s):  
Detection Method ◽  
Detection Efficiency ◽  
Hill Climbing ◽  
Key Factors ◽  
Botnet Detection ◽  
Evaluation Indexes ◽  
Hill Climbing Algorithm ◽  
Detection Speed

In order to solve the problem of detection efficiency and the detection speed in botnet detection, a novel botnet detection method is proposed based on hill-climbing algorithm and FARIMA. At first, the evaluation indexes are presented in this method, and botnet and infection hosts are quickly searched with hill-climbing algorithm. Then, FARIMA model is introduced to cut down the long-correlation of detection index. Finally, a simulation was conducted to research on the key factors with MATLAB. The result shows that, compared to other algorithms, it has good adaptability, and it could effectively search for infected hosts and botnets.

A New Scalable Botnet Detection Method in the Frequency Domain

2018 ◽  
pp. 141-166
Author(s):  
Giovanni Bottazzi ◽  
Giuseppe F. Italiano ◽  
Giuseppe G. Rutigliano
Keyword(s):  
Frequency Domain ◽  
Detection Method ◽  
Botnet Detection
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