Scholarlink

<p>As IoT devices become more common, they’re also becoming a bigger target for security threats, especially botnet attacks. This research presents a new approach to address these risks by introducing an Intrusion Detection System (IDS) using Federated Learning (FL). Unlike traditional methods that collect data centrally, this FL-based IDS identifies botnet threats without needing a single point for data storage, which helps to keep user data private. The model is built on the N-BaIoT dataset and uses the FedAvg aggregation technique, achieving a precision of 92% and an F1 score of 0.89, a performance that is on par with centralized models. This approach not only enhances privacy but also scales better across diverse IoT networks. However, there are some challenges. High communication demands, varied data quality from different devices, and stability issues in the model suggest there’s room for improvement. Future work will focus on making communication more efficient and reinforcing the model to resist adversarial threats. This research marks a step forward in secure, collaborative IDS frameworks for IoT networks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Iot botnet intrusion detection system using federated learning techniques

  • 期刊论文

  • 发表日期：

    2026年2月9日

查看全文
  • Amro Saleh,
  • Mouhammd Alkasassbeh,
  • Mohammad Almseidin

摘要

As IoT devices become more common, they’re also becoming a bigger target for security threats, especially botnet attacks. This research presents a new approach to address these risks by introducing an Intrusion Detection System (IDS) using Federated Learning (FL). Unlike traditional methods that collect data centrally, this FL-based IDS identifies botnet threats without needing a single point for data storage, which helps to keep user data private. The model is built on the N-BaIoT dataset and uses the FedAvg aggregation technique, achieving a precision of 92% and an F1 score of 0.89, a performance that is on par with centralized models. This approach not only enhances privacy but also scales better across diverse IoT networks. However, there are some challenges. High communication demands, varied data quality from different devices, and stability issues in the model suggest there’s room for improvement. Future work will focus on making communication more efficient and reinforcing the model to resist adversarial threats. This research marks a step forward in secure, collaborative IDS frameworks for IoT networks.