Absorption, Distribution, Metabolism, Excretion, and Toxicity Evaluation in Drug Discovery. 14. Prediction of Human Pregnane X Receptor Activators by Using Naive Bayesian Classification Technique

2014 ◽  
Vol 28 (1) ◽  
pp. 116-125 ◽  
Author(s):  
Huali Shi ◽  
Sheng Tian ◽  
Youyong Li ◽  
Dan Li ◽  
Huidong Yu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Pregnane X Receptor ◽  
Bayesian Classification ◽  
Toxicity Evaluation ◽  
Naive Bayesian ◽  
Classification Technique ◽  
Naïve Bayesian

NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks

2018 ◽  
Vol 74 (10) ◽  
pp. 5156-5170 ◽  
Author(s):  
Amjad Mehmood ◽  
Mithun Mukherjee ◽  
Syed Hassan Ahmed ◽  
Houbing Song ◽  
Khalid Mahmood Malik
Keyword(s):  
Bayesian Classification ◽  
Multi Agent System ◽  
Ddos Attacks ◽  
Agent System ◽  
Naive Bayesian ◽  
Classification Technique ◽  
Naïve Bayesian ◽  
Multi Agent

scholarly journals Malware Classification using Machine Learning Algorithms and Tools

2021 ◽  
Vol 9 (VI) ◽  
pp. 69-73
Author(s):  
Mr. Vikram Chavan
Keyword(s):  
Bayesian Classification ◽  
Internet Security ◽  
Machine Learning Algorithms ◽  
System Model ◽  
Naive Bayesian ◽  
Classification Technique ◽  
Naïve Bayesian ◽  
Mathematical Equations ◽  
The One ◽  
Day By Day

The explosive growth of malware variants poses a major threat to information security. Malware is the one which frequently growing day by day and becomes major threats to the Internet Security. According to numerous increasing of worm malware in the networks nowadays, it became a serious danger that threatens our computers. Networks attackers did these attacks by designing the worms. A designed system model is needed to defy these threats, prevent it from multiplying and spreading through the network, and harm our computers. In this paper, we designed a classification on system model for this issue. The designed system detects the worm malware that depends on the information of the dataset that is taken from website, the system will receive the input package and then analyze it, the Naïve Bayesian classification technique will start to work and begin to classify the package, by using the data mining Naïve Bayesian classification technique, the system worked fast and gained great results in detecting the worm. By applying the Naïve Bayesian classification technique using its probability mathematical equations for both threat data and benign data, the technique will detect the malware and classify data whether it was threat or benign.

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