The accelerated growth of the Internet and smart devices has increased the volume of data generated at the network edge, also raising the incidence of attacks targeting the exploitation of sensitive information. Thus, the relevance and necessity of distributed, efficient Intrusion Detection Systems (IDS) that preserve user privacy become evident. This paper proposes FedPEAC-Net, an IDS based on federated learning that combines an autoencoder and a binary classifier trained in parallel, integrated with a feature selection technique guided by reconstruction error. The adopted methodology involves experimentation with the CICIDS2017 dataset in a federated scenario with 10 clients. The results show that FedPEAC-Net achieves an F1-score of 0.95 before and 0.93 after feature selection, showing only a 2.3% drop, while ResNet and LeNet-5 models showed reductions of 15.6% and 15%, respectively. FedPEAC-Net also achieved the lowest total training time among the evaluated models, attaining 542 seconds over 20 federated rounds, compared to 819 seconds for LeNet-5 and 1742 seconds for ResNet. These results confirm the effectiveness and efficiency of the proposed approach in distributed environments, even with resource-constrained devices.
Rapid global adoption of applications requires robust architectural designs for scalability, latency, and cost-efficiency. This paper presents the architectural transformation of CaieFinder, a search engine for the past papers and mark schemes of Cambridge Assessment International Examinations (CAIE), from a monolithic system to microservices and, ultimately, to serverless infrastructure. Initially deployed on a single virtual machine in a monolithic architecture, CaieFinder faced latency spikes exceeding 300 ms for users outside of Southeast Asia despite serving 7.51 million monthly queries. Transitioning to a microservices-based architecture using Kubernetes-orchestrated Docker containers improved modularity and fault tolerance but introduced orchestration overhead and increased operational costs. Finally, migration to a serverless architecture, i.e., leveraging Function-as-a-Service (FaaS) and edge computing, significantly reduced global response times by $\mathbf{4 4 \%}(\mathbf{1 5 0 ~ m s}$ on average) and lowered cloud computing costs by $9 \%$, while enabling granular auto-scaling and superior fault isolation. We present our real-world deployment data, including latency, cost, and resource utilization, to demonstrate the advantages and trade-offs of each architecture.
The proliferation of Internet of Things (IoT) devices has significantly advanced automation and connectivity across various domains, including smart homes, cities, industry, agriculture, transportation, and healthcare. However, this widespread adoption also presents substantial management and security challenges. In smart home environments, the limited computational capabilities and lack of robust encryption mechanisms in IoT devices make them prime targets for cyberattacks. Accurate identification of IoT devices is essential for implementing effective cybersecurity measures. Previous studies have proposed various approaches for identifying IoT devices; however, these studies are constrained by complex networks under strict conditions, with a small number of IoT devices in the datasets and variations in implementations. This study introduces ScanIoT, an easily deployable data collection framework designed to capture IoT device traffic in smart homes. Using this framework, we collected the Concordia University IoT device identification dataset (CU2025). We also propose a DFNN model that achieves an accuracy of $\mathbf{9 9 . 9 7 \%}$ for the device category classification and 99. 73% for the device classification on the CICIoT2023 dataset. DFNN achieves an accuracy of $\mathbf{9 9 . 9 9 \%}$ on the CU2025 dataset for device classification.
Efficient and secure data processing in heterogeneous networks, such as the Internet of Things, is a challenge for ensuring Quality of Service (QoS) and security. In this sense, applying artificial neural networks for intrusion detection and decision-making faces difficulties related to high computational cost and dependence on labeled data, which is complex and expensive to acquire. This paper proposes a self-supervised approach based on data representation with Bloom Filters as a pre-training step to transform network flows into binary matrices. The proposal extracts latent representations from the data using self-supervised learning, eliminating the need for labels and reducing computational costs. The experimental results demonstrate the effectiveness of the proposed approach. Among the evaluated models, SimCLR and Barlow Twins achieved the best performance, with an F1-score of 0.77 and an AUC of up to 0.86, indicating their potential for building more robust and efficient intrusion detection systems.
With the deployment of Elastic Optical Networks (EONs), fragmentation has often been viewed as a loose proxy for network blocking. In prior work, we examined the correlation between various global fragmentation metrics and blocking rates, revealing that global metrics alone are unreliable predictors of blocking.In this paper, we extend our investigation to assess the predictive capabilities of path-level fragmentation metrics and formalize a novel fragmentation-as-a-picture approach. Through extensive simulations across different network topologies, congestion levels, and connection granularities, we collected comprehensive blocking data. Using this dataset, we trained several machine learning models and found that both path-level metrics and fragmentation-as-a-picture representations significantly improve prediction accuracy.Notably, aggregating path-specific fragmentation values, augmented by statistical descriptors such as kurtosis, standard deviation, and skewness, yields strong predictive performance. Moreover, the fragmentation-as-a-picture approach proves highly effective in predicting the blocking status of individual lightpaths by capturing spatial and structural spectrum patterns in a visual form.
