scholarly journals Network Intrusion Detection Method Based on PCA and Bayes Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Bing Zhang ◽  
Zhiyang Liu ◽  
Yanguo Jia ◽  
Jiadong Ren ◽  
Xiaolin Zhao
Keyword(s):  
Intrusion Detection ◽  
Naive Bayes ◽  
Detection Method ◽  
Monitoring Network ◽  
Naïve Bayes ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Time ◽  
Network Intrusion ◽  
Bayes Algorithm

Intrusion detection refers to monitoring network data information, quickly detecting intrusion behavior, can avoid the harm caused by intrusion to a certain extent. Traditional intrusion detection methods are mainly focused on rule files and data mining. They have the disadvantage of not being able to detect new types of attacks and have the slow detection speed. To address these issues, an intrusion detection method based on improved PCA combined with Gaussian Naive Bayes was proposed. By weighting the first few feature vectors of the traditional PCA, data pollution can be reduced. The number of final weighted principal components is 2 through sequential selection. The dimensionality reduction of the data is achieved through improved PCA. Finally, the intrusion behaviors were detected by using the Gaussian Naive Bayes classifier. The indexes of detection accuracy, detection time, precision rate, and recall rate were applied to evaluate the results. The experimental results show that, comparing with the traditional Bayes method, the method proposed in this article can reduce the detection time by 60%, shorten it to 0.5s, and increase the detection rate to 91.06%. The mean value of detection accuracy is about 86% by cross-validation.

scholarly journals An Intrusion Detection Model based on Hybrid Classification algorithm

2018 ◽  
Vol 246 ◽  
pp. 03027
Author(s):  
Manfu Ma ◽  
Wei Deng ◽  
Hongtong Liu ◽  
Xinmiao Yun
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Performance Requirements ◽  
Hybrid Classification ◽  
Bayes Algorithm

Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest distance. Finally, by naive Bayes to get the final result. The experimental results on the NSL-KDD dataset show that the KNN-NB algorithm can meet the requirement of balanced performance than the traditional KNN and Naive Bayes algorithm in term of accuracy, sensitivity, false detection rate, specificity, and missed detection rate.

scholarly journals Accuracy Enhancement of Diabetic Retinopathy Detection Using Naive Bayes Algorithm

2021 ◽  
Vol 9 (VII) ◽  
pp. 3648-3652
Author(s):  
Mithileshkumar Yadav
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Automated System ◽  
Diabetic Patients ◽  
Detection Accuracy ◽  
Monitoring Model ◽  
Bayes Algorithm ◽  
Retinal Fundus

Diabetic retinopathy (DR) is a disease of eye which is caused by diabetes. Sometime the DR leads the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to protect the eyesight and provide help for timely treatment. The detection of DR can be done manually by ophthalmologists and can also be done by an automated system. An ophthalmologist is required to analyze and explain retinal fundus images in the manual system, which is a time consuming and very expensive task. While, In the automated system, artificial intelligence is used to perform an significant role in the area of ophthalmology and specifically in the early detection of DR over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported, But still research for accurate detection of DR is going on. In this paper, a new diabetic retinopathy monitoring model is proposed by using the Naive Bayes method to improve the accuracy of detection of DR. The model is trained on mixture of two datasets Messidor and Kaggle, and evaluated on the Messidor dataset. By using proposed method detection accuracy is found to be higher than existing methods.

scholarly journals Feature Dependent Naïve Bayes For Network Intrusion Detection System

2020 ◽  
Vol 1641 ◽  
pp. 012023
Author(s):  
Panny Agustia Rahayuningsih ◽  
Reza Maulana ◽  
Windi Irmayani ◽  
Dedi Saputra ◽  
Deasy Purwaningtias
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Research on Key Security Detection Method of Cross Domain Information Sharing Based on PKG Trust Gateway

2021 ◽  
Author(s):  
RunQi Li
Keyword(s):  
Information Sharing ◽  
Detection Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Time ◽  
System Parameters ◽  
Management Platform ◽  
Cross Domain ◽  
Domain Information ◽  
Detection Effect

Aiming at the problems of low precision, long detection time and poor detection effect in current cross domain information sharing key security detection methods, a cross domain information sharing key security detection method based on PKG trust gateway is proposed. By analyzing bilinear pairing based on elliptic curve and identity based encryption scheme, according to the independent system parameters of PKG management platform, cross domain authentication access mechanism is proposed. PKG of different trust domains is used as the trust gateway for cross domain authentication. The key escrow problem of PKG of different trust domains is solved through key sharing, and the communication key agreement mechanism is established to mutually authenticate the user nodes in the trust domains with different system parameters. The formal description of the rule detection of cryptographic functions, parameters and other information, supported by the dynamic binary analysis platform pin, dynamically records the encryption and decryption process information during the operation of the program, and realizes cross domain information sharing key security detection through the design of correlation vulnerability detection algorithm. The experimental results show that the cross-domain information shared key security detection effect of the proposed method is better, which can effectively improve the detection accuracy and shorten the detection time.

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