This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework aimed at bolstering the security of Internet of Things (IoT) environments by synergistically integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The proposed hierarchical architecture spans edge, fog, and cloud layers, ensuring efficient and scalable collaborative intrusion detection. Trustworthiness is established through the incorporation of distributed ledger technology (DLT), leveraging blockchain frameworks to enhance the reliability and transparency of communication among IoT devices. Furthermore, the research adopts federated learning (FL) techniques to address privacy concerns, allowing devices to collaboratively learn from decentralized data sources while preserving individual data privacy. Validation of the proposed approach is conducted using the CICIoT2023 dataset, demonstrating its effectiveness in enhancing the security posture of IoT ecosystems. This research contributes to the advancement of secure and resilient IoT infrastructures, addressing the imperative need for lightweight, trust-managing, and privacy-preserving solutions in the face of evolving cybersecurity challenges. According to our experiments, the proposed model achieved an average accuracy of 97.65%, precision of 97.65%, recall of 100%, and F1-score of 98.81% when detecting various attacks on IoT systems with heterogeneous devices and networks. The system is a lightweight system when compared with traditional intrusion detection that uses centralized learning in terms of network latency and memory consumption. The proposed system shows trust and can keep private data in an IoT environment.

The botnet attack is one of the coordinated attack types that can infect Internet of Things (IoT) devices and cause them to malfunction. Botnets can steal sensitive information from IoT devices and control them to launch another attack, such as a Distributed Denial-of-Service (DDoS) attack or email spam. This attack is commonly detected using a network-based Intrusion Detection System (NIDS) that monitors the network device’s activity. However, IoT network is dynamic and IoT devices have many types with different configurations and vendors in IoT environments. Therefore, this research proposes an Intrusion Detection System (IDS) by ensemble-ing traffic from heterogeneous IoT devices. This research proposes Deep Neural Network (DNN) to create a training model from each heterogeneous IoT device. After that, each training model from each heterogeneous IoT device is used to predict the traffic. The prediction results from each training model are averaged using the ensemble averaging method to determine the final result. This research used the N-BaIoT dataset to validate the proposed IDS model. Based on experimental results, ensemble averaging DNN can detect botnet attacks in heterogeneous IoT devices with an average accuracy of 97.21, precision of 91.41, recall of 87.31, and F1-score 88.48.

This review article looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative learning methods. FL is promising collaborative learning method in recent research. This review aims to offer insights and lesson learn for creating a taxonomy of collaborative anomaly detection in CIDS using FL as a collaborative learning method. Our findings suggest that a taxonomy is required to map the discussion area, including an algorithm for training the learning model, the dataset, global aggregation model, system architecture, security, and privacy. Our results indicate that FL is a promising approach for collaborative anomaly detection in CIDS, and the proposed taxonomy could be useful for future research in this area. Overall, this review contributes to the growing knowledge of FL for CIDS, providing insights and lessons for researchers and practitioners. This research also concludes significant challenges, opportunities, and future directions in CIDS based on collaborative anomaly detection using FL.

This study proposes a prototype model from access control security system in the Internet of Things (IoT) that uses the Message Queuing Telemetry Transport (MQTT) protocol as its communication and fog computing as its architecture with the authentication server and secure protocol. In the MQTT protocol, there are security mechanism issues such as publisher (device) authentication and data privacy protection is still not good. This causes the integrity and confidentiality of a data used by the subscriber to be less secure. Device nodes and gateway devices in IoT that become publishers will be authenticated to obtain tokens using authentication server via HTTPS. Tokens are embedding with sensor data inside secure payload format and publish to MQTT broker. Authentication server will perform management and validation credentials on all publishers and secure payload in MQTT broker. In addition, SSL certificates are applied to the MQTT protocol to secure their communications. Based on evaluation and security analysis, the application of access control mechanisms can be implemented on the MQTT protocol and can secure the integrity and confidentiality of data that sent from the device to the cloud over the internet. In the overhead analysis, there is a significant increase in payload because of the payload data sent with the token, but the latency of delivery time, CPU, memory usage is still up to a reasonable limit of not more than 50%.

Ethereum is one of the oldest examples of blockchain technology provides a system that converts centralized storage to distributed and records transactions by way of decentralized and not by a centralized system and can be verified by each node, therefore it is suitable for storing fingerprints from official diploma documents and transcripts that are published. Smart contract is needed for making contract transactions to Ethereum with programming code, so contracts such as diplomas and transcripts uploaded on the Ethereum blockchain can distribute and produce diploma validation and the authenticity of transcripts with transaction hash, consensus, and comply with ERC-721 token standardization. The results showed that a sample of 5 electronic documents in pdf format with a transaction speed of 1 second on each file that were published and secured with Ethereum blockchain technology can be easily verified for authenticity, the system proposed and developed by us takes in consideration invalid and failure cases by giving the necessary feedback to the user.

Detecting coordinated attacks in cybersecurity is challenging due to their sophisticated and distributed nature, making traditional Intrusion Detection Systems often ineffective, especially in heterogeneous networks with diverse devices and systems. This research introduces a novel Collaborative Intrusion Detection System (CIDS) using a Weighted Ensemble Averaging Deep Neural Network (WEA-DNN) designed to detect such attacks. The WEA-DNN combines deep learning techniques and ensemble methods to enhance detection capabilities by integrating multiple Deep Neural Network (DNN) models, each trained on different data subsets with varying architectures. Differential Evolution optimizes the model’s contributions by calculating optimal weights, allowing the system to collaboratively analyze network traffic data from diverse sources. Extensive experiments on real-world datasets like CICIDS2017, CSE-CICIDS2018, CICToNIoT, and CICBotIoT show that the CIDS framework achieves an average accuracy of 93.8%, precision of 78.6%, recall of 60.4%, and an F1-score of 62.4%, surpassing traditional ensemble models and matching the performance of local DNN models. This demonstrates the practical benefits of WEA-DNN in improving detection capabilities in real-world heterogeneous network environments, offering superior adaptability and robustness in handling complex attack patterns.