The rapid expansion of edge computing and Internet of Things (IoT) ecosystems has introduced new cybersecurity challenges, particularly in decentralized, resource-constrained environments where traditional security models often fall short. This paper proposes an immune-inspired artificial intelligence framework (I3AI) that draws on core principles of biological immune systems including self-organization, local learning, and immune memory to enable adaptive, privacy-preserving defense mechanisms across distributed edge nodes. The architecture incorporates federated learning to maintain a decentralized threat intelligence network while ensuring data privacy and minimal communication overhead.... More >

Traditional centralized machine learning approaches for IoT botnet detection pose significant privacy risks, as they require transmitting sensitive device data to a central server. This study presents a privacy-preserving Federated Learning (FL) approach that employs Federated Averaging (FedAvg) to detect prevalent botnet attacks, such as Mirai and Gafgyt, while ensuring that raw data remain on local IoT devices. Using the N-BaIoT dataset, which contains real-world benign and malicious traffic, we evaluated both the IID and non-IID data distributions to assess the effects of decentralized training. Our approach achieved 97.5% accuracy in IID and 95.2% in highly skewed non-IID scenarios, clos... More >