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<p>This study introduces the Opposition Based learning (OB) algorithm and Nelder–Mead (NM) algorithm coupled with the Arithmetic Optimization Algorithm (AOA) via hybridization mechanism called OBAOANM to enhance the detection accuracy of telecom network anomalies. The proposed algorithm OBAOANM features extending the exploitation and exploration phases of AOA algorithm into OB and NM algorithms to improve identification of network bypass anomalies The broad-spectrum benefit of the proposed algorithm is to boost the identification precision of telecom network intrusion detection (NID) capabilities thus making the OBAOANM algorithm a more suitable tool for solving network traffic problems. The experimental results prove that this OBAOANM algorithm aids maximizing classification accuracies of NID by attaining an average of 93.10% of the overall comparative experimentations.</p>

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Hybrid arithmetic optimization algorithm for telecom network anomaly detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月18日

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  • Abdulle Hassan Mohamud

摘要

This study introduces the Opposition Based learning (OB) algorithm and Nelder–Mead (NM) algorithm coupled with the Arithmetic Optimization Algorithm (AOA) via hybridization mechanism called OBAOANM to enhance the detection accuracy of telecom network anomalies. The proposed algorithm OBAOANM features extending the exploitation and exploration phases of AOA algorithm into OB and NM algorithms to improve identification of network bypass anomalies The broad-spectrum benefit of the proposed algorithm is to boost the identification precision of telecom network intrusion detection (NID) capabilities thus making the OBAOANM algorithm a more suitable tool for solving network traffic problems. The experimental results prove that this OBAOANM algorithm aids maximizing classification accuracies of NID by attaining an average of 93.10% of the overall comparative experimentations.