2026 Vol.9 Feb.N01   

• Title: An Anomaly Detection Method for Wireless Communication Networks Based on Python Data Mining
• Name: Bin Wang,Chaorong Zhu
• Company: Nanchang Universily College of Science and Technology,Jiujiang，332020,China
• Abstract:

  Most methods for detecting operational anomalies in wireless communication networks rely on fixed thresholds or simple statistical analysis. By setting threshold ranges for traffic, anomalies are identified when monitored data exceeds these limits. However, the lack of deep capture of network traffic characteristics makes it difficult to address complex and dynamic anomaly patterns, resulting in poor detection accuracy. To address this, a Python data mining-based method for detecting operational anomalies in wireless communication networks is proposed. First, a Scrapy crawler framework is established to mine network traffic data using Python software. Data is discretized via equal-width binning, uniformly dividing the range into fixed-width intervals. Information entropy is employed to reflect the concentration or dispersion of traffic attribute distributions, effectively extracting traffic behavioral characteristics. Anomaly coefficients are defined by calculating item set pattern support and mining edge weights and weight coefficients. By comparing anomaly coefficients against thresholds, we determine whether operational anomalies occur at specific time points. Experimental validation of detection accuracy confirms that the proposed method achieves stable anomaly pattern recognition completeness exceeding 0.95, demonstrating highly effective detection performance.

  
  

• Keyword: Python; Data Mining; Wireless Communication Networks; Anomaly Detection; Detection Accuracy;
• DOI: 10.12250/jpciams2026090205
• Citation form: Bin Wang,Chaorong Zhu.An Anomaly Detection Method for Wireless Communication Networks Based on Python Data Mining[J]. Computer Informatization and Mechanical System,2026,Vol.9,pp.

[1] Sreevidya B, Supriya D M. Malicious Nodes Detection and Avoidance Using Trust-based Routing in Critical Data Handling Wireless Sensor Network Applications. Journal of Internet Services and Information Security, 2024, 14(3):226-244.

[2] Subramani S, Selvi M. Intrusion detection system and fuzzy ant colony optimization-based secure routing in wireless sensor networks. Soft Computing, 2024, 28(17-18):10345-10367.

[3] Gowdhaman V, Dhanapal R. Hybrid deep learning-based intrusion detection system for wireless sensor networks[J]. International Journal of Vehicle Information and Communication Systems, 2024, 9(3):239-255.

[4] Ram R S, Saminathan A G. An Intrusion Detection System in WSN Using an Optimized Self-Attention-Based Progressive Generative Adversarial Network[J]. IETE Journal of Research, 2025, 71(4):1176-1189.

[5] Zhang H, Yang J, Gao C Y. Spectrum Usage Anomaly Detection from Sub-Sampled Data Stream via Deep Neural Network[J]. Journal of Communications and Information Networks, 2023, 8(1):13-23.

[6] Akilandeswari T, Surekha B, Subbarao P, et al. Arduino-Based Secured Access Control in Smart Homes by Implementing Anomaly Detection in Fingerprint-Based Door Lock and Real-Time Monitoring with OpenCV[J]. 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA), 2024:161-166.

[7] Skaperas S, Mamatas L, Tsaoussidis V. A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks[J]. IEEE Transactions on Machine Learning in Communications and Networking, 2024, 2(000):495-510.

[8] Darshan S, Radhika N, Radhika G. A Hybrid Prediction Model for Disseminating the Factors Related to Anomaly Detection in Software Defined Networks[J]. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2024:1-5.

[9] Kuchar K, Fujdiak R. Anomaly Detection in Industrial Networks: Current State, Classification, and Key Challenges[J]. Sensors Journal, IEEE, 2025, 25(3):5031-5043.

[10] Hayati H, Wouw N V D, Murguia C. Immersion and Invariance-based Coding for Privacy in Remote Anomaly Detection[J]. IFAC PapersOnLine, 2023, 56(2):11191-11196.

[11] Antony P D, Subiramaniyam N P. Integrating Advanced Convolutional Neural Networks and IoT in Health Monitoring: A Novel Approach to Real-Time Health Anomaly Detection and Risk Stratification Through Multi-Sensor Data Analysis[J]. Journal of Theoretical and Applied Information Technology, 2024, 102(6):2671-2692.

[12] Carletti V, Foggia P, Rosa F, et al. Detecting malicious IoT network communication through Graph Neural Networks in real-world conditions[J]. Pattern Recognition Letters, 2025, 189(000):92-98.

[13] Gawali V S, Ranjan N M. Anomaly detection system in 5G networks via deep learning model[J]. Int. J. Wireless & Mobile Computing, 2023, 24:287-302.

[14] Wang Y, Nakachi T. Network Traffic Anomaly Detection: A Revisiting to Gaussian Process and Sparse Representation[J]. IEICE Transactions on Fundamentals of Electronics, Communications & Computer Sciences, 2024, E107/A(1):125-133.

[15] Song Q. Research on the Application of Deep Learning-Based Computer Vision in Anomaly Detection in Communication Networks[J]. 2024 IEEE 4th International Conference on Data Science and Computer Application (ICDSCA), 2024:602-609.