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- Efficient feature selection for intrusion detection systems with priority queue-based GRASPPublication . Quincozes, Vagner E.; Quincozes, Silvio E.; Albuquerque, Célio; Passos, Diego; Massé, DanielThe Greedy Randomized Adaptive Search Proce dure for Feature Selection (GRASP-FS) is a recently-proposed metaheuristic that optimizes the feature selection process for Intrusion Detection Systems (IDS) by combining exploration and refinement techniques for more assertive intrusion detection. However, GRASP-FS may be time and resource-consuming for large datasets. In this work, we propose GRASPQ-FS, an extended version of GRASP-FS using Priority Queues to reduce resource consumption and processing time. As an additional contribution, we provide a comprehensive analysis of the most suitable parameters for our RASPQ-FS. Our results reveal that GRASPQ-FS can speed up feature selection up to 90% over GRASP-FS, without compromising F1-Score. Also, we observed that a priority queue with 50 solutions saved 50% in execution time while increasing the F1-Score by 4.5%.
- Blockchain for smart grid security: applications, trends, and challengesPublication . Mattos, Diogo; Varela De Medeiros, Dianne Scherly; Passos, Diego; Fernandes, Natalia Castro; Muchaluat-Saade, Débora Christina; Moraes, Igor Monteiro; Albuquerque, CélioThe electric power grid is the world's largest engineering system, and its secure and reliable operation is vital to human activities. The introduction of intelligence in the electrical power grid through smart grids imposes challenges that require new techniques and approaches to provide cyber-physical security. In this article, we discuss the use of blockchain to provide security and reliability to smart grids. Blockchain allows untrusted nodes to correctly and verifiably interact with each other in a distributed peer-to-peer network, without any reliable intermediary. We explore smart contracts, codes resident in blockchain that automate multi-step processes, as a way to automatically trade electric energy. We also discuss initiatives, challenges, and research opportunities of blockchain technologies in the electrical sector.
- Wireless multipath video transmission: when IoT video applications meet networking-a surveyPublication . Bhering, Fabiano; Passos, Diego; Ochi, Luiz Satoru; Obraczka, Katia; Albuquerque, CélioAdvances in video camera and wireless communication technology have enabled a variety of video applications over the Internet. However, meeting these applications' quality-of-service requirements poses significant challenges to the underlying network and has attracted significant attention from the networking research community. In particular, wireless multipath video transmission has been proposed as a viable alternative to deliver adequate performance to Internet video applications. This survey provides a thorough review of the current state-of-the-art in multipath video transmission focusing on IoT applications. We introduce a taxonomy to classify existing approaches based on their application-specific mechanisms (e.g., video coding techniques) as well as networking-specific techniques. In addition to describing existing approaches in light of the proposed taxonomy, we also discuss directions for future work.
- Towards feature engineering for intrusion detection in IEC-61850 communication networksPublication . Quincozes, Vagner; Ereno Quincozes, Silvio; Passos, Diego; Albuquerque, Célio; Mosse, DanielDigital electrical substations are fundamental in providing a reliable basis for smart grids. However, the deployment of the IEC-61850 standards for communication between intelligent electronic devices (IEDs) brings new security challenges. Intrusion detection systems (IDSs) play a vital role in ensuring the proper function of digital substations services. However, the current literature lacks efficient IDS solutions for certain classes of attacks, such as the masquerade attack. In this work, we propose the extraction and correlation of relevant multi-layer information through a feature engineering process to enable the deployment of machine learning-based IDSs in digital substations. Our results demonstrate that the proposed solution can detect attacks that are considered challenging in the literature, attaining an F1-score of up to 95.6% in the evaluated scenarios.
- Assessing machine learning techniques for intrusion detection in cyber-physical systemsPublication . Santos, Vinicius F.; Albuquerque, Célio; Passos, Diego; Ereno Quincozes, Silvio; Mossé, DanielCyber-physical systems (CPS) are vital to key infrastructures such as Smart Grids and water treatment, and are increasingly vulnerable to a broad spectrum of evolving attacks. Whereas traditional security mechanisms, such as encryption and firewalls, are often inadequate for CPS architectures, the implementation of Intrusion Detection Systems (IDS) tailored for CPS has become an essential strategy for securing them. In this context, it is worth noting the difference between traditional offline Machine Learning (ML) techniques and understanding how they perform under different IDS applications. To answer these questions, this article presents a novel comparison of five offline and three online ML algorithms for intrusion detection using seven CPS-specific datasets, revealing that offline ML is superior when attack signatures are present without time constraints, while online techniques offer a quicker response to new attacks. The findings provide a pathway for enhancing CPS security through a balanced and effective combination of ML techniques.