Anomaly Detection based on GA&FART Approach of Computer Network Security

2012 ◽  
Author(s):  
Preecha Somwang ◽  
Woraphon Lilakiatsakun ◽  
Surat Srinoy
Keyword(s):  
Network Security ◽  
Anomaly Detection ◽  
Computer Network ◽  
Computer Network Security

scholarly journals An Exhaustive Research on the Application of Intrusion Detection Technology in Computer Network Security in Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yajing Wang ◽  
Juan Ma ◽  
Ashutosh Sharma ◽  
Pradeep Kumar Singh ◽  
Gurjot Singh Gaba ◽  
...  
Keyword(s):  
Network Security ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Outlier Detection ◽  
Computer Network ◽  
Clustering Algorithms ◽  
Nearest Neighbors ◽  
Detection Technology ◽  
Computer Network Security ◽  
Semisupervised Clustering

Intrusion detection is crucial in computer network security issues; therefore, this work is aimed at maximizing network security protection and its improvement by proposing various preventive techniques. Outlier detection and semisupervised clustering algorithms based on shared nearest neighbors are proposed in this work to address intrusion detection by converting it into a problem of mining outliers using the network behavior dataset. The algorithm uses shared nearest neighbors as similarity, judges whether it is an outlier according to the number of nearest neighbors of a data point, and performs semisupervised clustering on the dataset where outliers are deleted. In the process of semisupervised clustering, vast prior knowledge is added, and the dataset is clustered according to the principle of graph segmentation. The novelty of the proposed algorithm lies in outlier detection while effectively avoiding the dependence on parameters, thus eliminating the influence of outliers on clustering. This article uses real datasets: lypmphography and glass for simulation purposes. The simulation results show that the algorithm proposed in this paper can effectively detect outliers and has a good clustering effect. Furthermore, the experimentation reveals that the outlier detection-based SCA-SNN algorithm has the best practical effect on the dataset without outliers, clearly validating the clustering performance of the outlier detection-based SCA-SNN algorithm. Furthermore, compared to the other state-of-the-art anomaly detection method, it was revealed that the anomaly detection technology based on outlier mining does not require a training process. Thus, they overcome the current anomaly detection problems caused due to incomplete normal patterns in training samples.

Computer Network Security and Directory Services Architecture

2000 ◽  
Author(s):  
James D. Johannes ◽  
Andrew Fanning ◽  
Kyle Hoover ◽  
Tim Lewis ◽  
Marsha Robinson
Keyword(s):  
Network Security ◽  
Computer Network ◽  
Computer Network Security ◽  
Directory Services

Methods for evaluating the computer network security with fuzzy number intuitionistic fuzzy dual Hamy mean operators

2020 ◽  
Vol 39 (3) ◽  
pp. 4427-4441
Author(s):  
Bin Xu
Keyword(s):  
Network Security ◽  
Membership Function ◽  
Fuzzy Number ◽  
Fuzzy Numbers ◽  
Computer Network ◽  
Information Aggregation ◽  
Security Evaluation ◽  
Uncertain Information ◽  
Intuitionistic Fuzzy ◽  
Computer Network Security

The concept of fuzzy number intuitionistic fuzzy sets (FNIFSs) is designed to effectively depict uncertain information in decision making problems which fundamental characteristic of the FNIFS is that the values of its membership function and non-membership function are depicted with triangular fuzzy numbers (TFNs). The dual Hamy mean (DHM) operator gets good performance in the process of information aggregation due to its ability to capturing the interrelationships among aggregated values. In this paper, we used the dual Hamy mean (DHM) operator and dual weighted Hamy mean (WDHM) operator with fuzzy number intuitionistic fuzzy numbers (FNIFNs) to propose the fuzzy number intuitionistic fuzzy dual Hamy mean (FNIFDHM) operator and fuzzy number intuitionistic fuzzy weighted dual Hamy mean (FNIFWDHM) operator. Then the MADM methods are proposed along with these operators. In the end, we utilize an applicable example for computer network security evaluation to prove the proposed methods.

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