AIDA’s researchers present poster at EuroSys Conference

AIDA’s researchers present poster at EuroSys 2023 Earlier this month, two researchers from INESC TEC, one partner of the AIDA project, presented a poster at this year EuroSys Conference, in Rome, with the title “Emission-Aware Federated Learning: A Case Study on Transportation and Carbon Footprint”. The poster is focused on the addition of Federated Learning”, which enables the promotion of sustainable and personalised travel behaviours while preserving data privacy. As written in the poster, the main goal of the research “is to preserve the privacy of users data while increasing awareness on their carbon footprint”. EuroSys Conference is one of […]

AIDA promoted a networking event with PhD students

AIDA promoted a networking event with PhD students On March 29, eight PhD students got together to share and discuss the work they have been developing within the AIDA project. This event was an excellent networking opportunity for the students to meet each other and to get to know other investigation topics. There were presentations in three main areas related to the project outputs: Distributed Systems, Security, and Deep Learning and Anomaly Detection. The meeting was moderated by Pedro Fidalgo, from Mobileum, and had the participation of Luís Ferreira, from INESC TEC, José Flora, Iury Araújo, Mariana Cunha […]

There is always one more data bug

There is always one more data bug If we, as data scientists, receive a dataset from a reliable source, we should go ahead with the analysis (classification, clustering, deep learning, etc), right? Well, yes, that’s what most of us (myself included) often do, especially if there is a tight deadline. However, this could be dangerous. Let me describe some rude awakenings I suffered over the past decades, as well as remind you some fast and easy preventive measures. Examples (a.k.a. horror stories) E1 Geographical data Two decades ago, we got access to a public dataset of cross-roads in California, […]

AIDA presented to PhD students

AIDA presented to PhD students INESC TEC, one of AIDA’s partners, presented AIDA to the Doctoral Program in Computer Science (MAP-I)’S first year students. This visit, which took place on December 6, 2022, happened during a visit to the High-Assurance Software Laboratory from INESC TEC at the University of Minho. This was an opportunity to present the work carried out by the AIDA project and get to know a good example of international collaboration between industry and academia. It is important to mention that the initiative was also an opportunity to share the project’s vision and expected outcomes, while promoting […]

AIDA Research on Suspicious Behavior and Anomaly Detection

AIDA Research on Suspicious Behavior and Anomaly Detection One of the goals of the AIDA Project is to investigate and identify new ways to help Analysts find Anomalous Behavior (of any kind) in large and complex pools of data. We hope this can ultimately lead to significant improvements in the detection of fraudulent activity occurring on Telecom Networks, especially in the light of new technologies like 5G already expanding on the market. Moreover, we now see over more sophisticated, complex and robust methods being used for committing fraud on Telecomm Networks but also increasingly expanding to the world of Communication […]

AIDA participated in the CMU Portugal Summit 2022

AIDA participated in the CMU Portugal Summit 2022 Pedro Fidalgo, from our coordinator partner Mobileum, participated in the roundtable “Adaptive, automated, and autonomic computing” at this year’s CMU Portugal Summit, which happened under the motto “New Frontiers in tech”. During the conference, the AIDA project had a meeting with the CMU Portugal External Review Committee, an advisory board charged with assessing the project’s performance and making recommendations. Paula Raissa Silva, from INESC TEC, also participated in the conference with a paper entitled “Federated Anomaly Detection over Distributed Data Streams”. The conference aimed at bringing experts to present research progress in […]

Protecting the Security and Privacy of AIDA and its Data

Protecting the Security and Privacy of AIDA and its Data Adapting the RAID platform comes with many security and privacy issues that need to be addressed. These issues appear mainly due to the transition to an edge architecture, which imposes the use of computation resources at the edges of the network, but also due to the need of supporting multiple tenants and network slicing with 5G technology. As mobile phones connect and disconnect, the network is always changing, requiring adaptation and monitorization tools to be used to maximize resources. And since the complexity of the system increases, attackers have more […]

Fraud detection, micro-clusters and scatterplots

Fraud detection, micro-clusters and scatterplots Acknowledgements The results and analysis presented here were done with contributions from Mirela Cazzolato (USP, and CMU), Saranya Vijayakumar (CMU), Xinyi (Carol) Zheng (CMU), Meng-Chieh (Jeremy) Lee (CMU), Namyong Park (CMU), Pedro Fidalgo (Mobileum), Bruno Lages (Mobileum), and Agma Traina (USP). Reminders – Problem definition and past insights As we mentioned in the February 2022 blog post, the problem we are focusing on is to spot fraudulent behavior in a who-calls-whom-and-when graph. We distinguished between the supervised case (where we are given a list of fraudulent subscribers (labeled data)), and the un-supervised one, where […]

AIDA paper presents a mechanism that ensures better performance and reduces costs in IoT devices

AIDA paper presents a mechanism that ensures better performance and reduces costs in IoT devices “Adaptive Database Synchronization for an Online Analytical Cloud-to-Edge Continuum” is the new paper of the AIDA project, which was presented this week at the ACM Dependable, Adaptive, and Secure Distributed Systems (DADS 2022). Internet of Things devices are generally underpowered, but they exist in large volumes and are located very close to where the data is captured. The use of these devices is increasingly common in the industry, agriculture, running cities, and even at homes. Because of their limitations, the data is traditionally transferred periodically to […]

Federated Machine Learning

Federated Machine Learning Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to overcome the challenges of data storage and data sensibility [1]. The last few years have been strongly marked by artificial intelligence, machine learning, smart devices, and deep learning. As a result, two challenges arose in data science, impacting how data can be accessed and used. First, with the creation of the General Data Protection Regulation (GDPR) [2], the data became protected by the regulation. Institutions cannot store or share data without users’ authorization. Another challenge is that in the era of big data, a large volume of […]