Random Forest-Based Oppositional Henry Gas Solubility Optimization Model for Service Attack Improvement in WSN

2022 ◽  
Author(s):  
S. Jeyalakshmi ◽  
S. Sekar ◽  
S. Ravikumar ◽  
D. Kavitha
Keyword(s):  
Random Forest ◽  
Optimization Model ◽  
Gas Solubility ◽  
Service Attack

Detecting DDoS Attack

2021 ◽  
pp. 55-66
Author(s):  
Megala G. ◽  
S. Prabu ◽  
Liyanapathirana B. C.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Denial Of Service ◽  
Economic Loss ◽  
High Accuracy ◽  
Denial Of Service Attack ◽  
Ddos Attack ◽  
Internet Users ◽  
Service Attack

The major network security problems faced by many internet users is the DDoS (distributed denial of service) attack. This attack makes the service inaccessible by exhausting the network and resources with high repudiation and economic loss. It denies the network services to the potential users. To detect this DDoS attack accurately in the network, random forest classifier which is a machine learning based classifier is used. The experimental results are compared with naïve Bayes classifier and KNN classifier showing that random forest produces high accuracy results in classification. Application of machine learning, detecting DDoS attacks is modeled based on the supervised learning algorithm to produce best outcome with high accuracy of training algorithm on network dataset.

An optimization model of self-paced tracking

1984 ◽  
Author(s):  
M. A. Montazer ◽  
Colin G. Drury
Keyword(s):  
Optimization Model

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

Database-Driven Modeling based on Variable Selection using Random Forest and Its Application for Linear Air Fuel Ratio Sensor Output Prediction

2019 ◽  
Vol 139 (8) ◽  
pp. 850-857
Author(s):  
Hiromu Imaji ◽  
Takuya Kinoshita ◽  
Toru Yamamoto ◽  
Keisuke Ito ◽  
Masahiro Yoshida ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Sensor Output ◽  
Fuel Ratio
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