Internet Digital Economy Development Forecast Based on Artificial Intelligence and SVM-KNN Network Detection

Area of Science:

• Computer Science
• Cybersecurity
• Data Analysis

Background:

• Internet technology facilitates online activities but increases security risks.
• Digital economy growth faces challenges from security vulnerabilities and cyber-attacks.
• Effective web log analysis is crucial for identifying and mitigating internet security threats.

Purpose of the Study:

• To conduct a theoretical analysis of mainstream classification algorithms for web log analysis.
• To propose a hybrid Support Vector Machine (SVM) and K-nearest neighbor (KNN) algorithm for improved classification.
• To provide technical support for enhancing the KNN algorithm's weight factor.

Main Methods:

• Detailed theoretical analysis of mainstream classification algorithms.
• Literature research on hybrid SVM-KNN algorithms and KNN classifiers.
• Reasoning analysis to improve the KNN algorithm's weight factor.

Main Results:

• Identified the significance and practical value of web log analysis.
• Proposed a hybrid SVM-KNN algorithm integrating mainstream classification techniques.
• Provided a framework for enhancing the KNN algorithm's performance in classification tasks.

Conclusions:

• Web log analysis using hybrid algorithms like SVM-KNN is vital for digital security.
• Addressing capital and technology constraints is essential for digital economy security.
• Continuous improvement of algorithms and regulations is needed to combat evolving cyber threats.