The Application of Data Mining Algorithms in Big Data Network Security Defense
In order to study the application of data mining algorithm in big data network security defense,the random forest algorithm was used for data classification and identification,and the random forest algorithm was improved by using the fruit fly optimization algorithm.By extracting key features and preprocessing data,the effectiveness of the improved random forest classification algorithm in network intrusion detection can be im-proved.The research results indicate that compared with support vector machine classifiers and traditional MLPC classifier,the improved random forest algorithm can more accurately identify network intrusion types and it more suitable for application in network intrusion detection and defense.The simulation results show that the improved random forest algorithm has significant advantages and shows high accuracy in different attack types.The im-proved random forest algorithm maintains a detection accuracy of over 0.79 for different attack sequences,while the traditional MLPC classifier maintains a detection accuracy of over 0.66 for different attack sequences,and the support vector machine classifier maintains a detection accuracy of over 0.72 for different attack types.This demonstrates the powerful cyber penetration detection ability of the improved random forest algorithm,which can provide important guarantee for network security defense.
data miningbig datanetwork security defensefruit fly optimization algorithmrandom for-estintrusion detection
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