## 3 Related Work  
IoT has rapidly expanded to link many systems and devices. With this linkage comes security concerns that must be addressed. Researchers [50; 51; 52] have proposed taxonomies to classify IoT risks into several levels: service, device, infrastructure, and communication. IoT systems' physical objects, protocols, data, and software components are all open to security breaches [53]. To handle these problems, researchers have proposed several countermeasures including key managements, authentication, access control, and privacy preservation strategies [54; 51; 55]. They also focused on developing robust detection methods capable of identifying attack signatures within the unique constraints of IoT environments [50; 56]. However, the protection of IoT ecosystems remains a challenge due to device limitations and non-standard IoT settings [57; 51; 58]. Agent-based approaches are being investigated recently to reduce DDoS attacks on IoT systems. The researchers in [59; 60] explored and studied adaptive traffic filtering, anomaly detection using ML techniques, and collaborative agent-based detection systems. Some researchers have built agent-based simulators to study protection measures against DDoS attacks [61], and other suggest lightweight agents for IoT to identify and mitigate attacks [60; 62]. As novel techniques, blockchain-based collaborative detection and learning-driven detection mitigation have been investigated [63; 64; 65]. Although these approaches improve IoT network resilience against evolving DDoS attacks while utilizing scalability and resource limitations, they still present some drawbacks. Agent-based techniques commonly face challenges related to resource consumption when deployed on resource-limited IoT devices, potentially creating performance bottlenecks when deployed at scale. Collaborative detection systems, while effective in theory, face significant challenges with secure communication between agents and can fail when attackers target the coordination mechanisms themselves. Approaches like adaptive traffic filtering, blockchain-based detection, and learning-driven mitigation suffer from high false positive rates when facing sophisticated low-rate attacks, and typically require extensive training data that may not be representative of evolving attack patterns. [PERSON] et al. [22] presented Edge-IIoTset, a complete cybersecurity dataset for IoT and IIoT applications, which includes realistic network traffic from over 10 types of IoT devices across a seven-layer testbed architecture, thereby addressing constraints in existing datasets. Deep neural networks (DNNs) in centralized and federated learning environments, as well as traditional ML techniques like DT, RF, SVMs, and K-Nearest Neighbor (KNN) were evaluated. Accuracy values of 99.99% for binary classification and 94.67% for 15-class classification were obtained by using DNNs. The dataset outperforms current IoT/IIoT datasets and offers privacy-preserving federated learning, making it a useful resource for building intrusion detection systems in IoT/IIoT environments. Designed especially for intrusion detection in IIoT systems, [PERSON] et al. [66] presented a federated learning architecture. Their method uses CNNs and RNNs as baseline classifiers inside a federated learning system. The system is evaluated on the Edge-IIoT dataset and achieved an accuracy of 92.49%. Although it is less accurate than centralized models (93.92%), the suggested architecture offers significant advantages by lowering bandwidth use and ensuring data privacy. The key contribution of this study is the capacity of peripheral devices to perform intrusion detection independently, free from the need of always being connected to a central server. This immediately tackles IoT network's basic issues with limited bandwidth and privacy. Focusing on DenseNet and Inception Time architectures across three main datasets: ToN-IoT, UNSW-NB15, and Edge-IIoT, the authors in [67] performed a detailed comparative evaluation of DL models for IoT cybersecurity.