NavTalks

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<h2><strong>Upcoming  presentations</strong></h2>
<h2><strong>Upcoming  presentations</strong></h2>
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<h3><strong>January 2022</strong></h3>
 
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<table border="0.5" cellspacing="0" style="background:#89B085">
 
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<tr>
 
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            <td align="center" style="width:100px">13</td>
 
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            <td style="width:300px">Rohit Kumar</td>
 
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            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="The complex engineering research field of cyber-physical systems (CPS) is based on integrating computation, communication, and physical processes, providing design, modelling, and analysis techniques as a whole. In this talk, we will present an architectural model of CPS and discuss the requirements and challenges to make CPS safe.">Architectural support and mechanisms for resilient and safe control in Cyber-Physical System</span></td>
 
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            <td style="width:30px">&nbsp;</td>
 
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</tr>
 
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            <td align="center" style="width:100px">13</td>
 
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            <td style="width:300px">Daniel Ângelo</td>
 
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            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="Tor is one of the most popular anonymity networks in the world. Users of this platform range from dissidents to cybercriminals or even ordinary citizens concerned with their privacy. It is based on advanced security mechanisms to provide strong guarantees against traffic correlation attacks that can deanonymize its users and services.
 
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Torpedo is the first known traffic correlation attack on Tor that aims at deanonymizing OS sessions. In a federated way, servers belonging to ISPs around the globe can process deanonymization queries of specific IPs. With the abstraction of an interface, these queries can be submitted by an attacker to deanonymize OSes and clients.
 
-
Initial results show that this attack is able to identify the IP addresses of OS sessions with high confidence (no false positives). However, the current version of Torpedo relies on a central authority to manage the system, which requires ISPs to share sensitive network traffic of their
 
-
clients with a third party.
 
-
Thus, this work seeks to complement the previously developed research with the introduction and study of multi-party computation (MPC) techniques, with the objective of developing and assessing a new attack vector on Tor that can work even if ISPs encrypt their network traffic before correlation. In more detail, we intend to leverage, test and assess some existing general-purpose and machine learning oriented MPC frameworks and build a privacy- preserving solution on top of Torpedo that satisfies its scalability and performance requirements.">Privacy-preserving Deanonymization of Dark Web Tor Onion Services for Criminal Investigations</span></td>
 
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            <td style="width:30px">&nbsp;</td>
 
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</tr>
 
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            <td align="center" style="width:100px">27</td>
 
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            <td style="width:300px">Robin Vassantlal</td>
 
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            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="Byzantine Fault-Tolerant (BFT) State Machine Replication (SMR) is a classical paradigm for implementing trustworthy services that has received renewed interest with the emergence of blockchains and decentralized infrastructures. A fundamental limitation of BFT SMR is that it provides integrity and availability despite a fraction of the replicas being controlled by an active adversary, but does not offer any confidentiality protection. Previous works addressed this issue by integrating secret sharing with the consensus-based framework of BFT SMR, but without providing all features required by practical systems, which include replica recovery, group reconfiguration, and acceptable performance when dealing with a large number of secrets. We present COBRA, a new protocol stack for Dynamic Proactive Secret Sharing that allows implementing confidentiality in practical BFT SMR systems. COBRA exhibits the best asymptotic communication complexity and optimal storage overhead, being able to renew 100k shares in a group of ten replicas 5 times faster than the current state of the art.">COBRA: Dynamic Proactive Secret Sharing for Confidential BFT Services</span></td>
 
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            <td style="width:30px">&nbsp;</td>
 
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</tr>
 
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            <td align="center" style="width:100px">27</td>
 
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            <td style="width:300px">João Loureiro</td>
 
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            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="Connected and autonomous vehicles aim to improve passenger safety and driving quality of experience. However, current self-driving solutions still constitute an entry barrier to many potential users due to their cost and the offloading of the self-driving algorithms to reduce the onboard computing requirements. At the same time, a viable alternative requires a stable connection to the cloud. This work explores deep learning concepts to forecast mobile network KPIs. These models may ultimately be used to adjust the vehicle's operational parameters to improve network signal quality and ensure a reliable connection to the cloud servers.">Deep learning for communication optimization on autonomous vehicles</span></td>
 
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            <td style="width:30px">&nbsp;</td>
 
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</tr>
 
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</table>
 
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<h3><strong>February 2022</strong></h3>
 
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<table border="0.5" cellspacing="0" style="background:#89B085">
 
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<tr>
 
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            <td align="center" style="width:100px">10</td>
 
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            <td style="width:300px">Carlos Mão de Ferro</td>
 
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            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="">TBD</span></td>
 
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            <td style="width:30px">&nbsp;</td>
 
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</tr>
 
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        <tr>
 
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            <td align="center" style="width:100px">10</td>
 
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            <td style="width:300px">David Silva</td>
 
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            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="">Developing a scalable IoT solution for remote monitoring and control</span></td>
 
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            <td style="width:30px">&nbsp;</td>
 
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</tr>
 
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            <td align="center" style="width:100px">24</td>
 
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            <td style="width:300px">Adriano Mão de Ferro</td>
 
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            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="">TBD</span></td>
 
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            <td style="width:30px">&nbsp;</td>
 
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</tr>
 
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</table>
 
<h3><strong>March 2022</strong></h3>
<h3><strong>March 2022</strong></h3>
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             <td align="center" style="width:100px">10</td>
             <td align="center" style="width:100px">10</td>
             <td style="width:300px">Miracle Aniakor</td>  
             <td style="width:300px">Miracle Aniakor</td>  
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             <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="Deep Learning is a relatively new method in the machine learning industry that has been used for a variety of tasks; it has sparked considerable attention among academics worldwide. Deep learning has been applied to complicated problems that need a high degree of human intelligence, most notably in the healthcare industry. One of the medical sectors that deep learning is widely used is radiology. In radiology, different radiographic techniques are used to identify brain tumors, which are among the most severe and fatal types of tumors, with a limited survival rate if not treated at its early stage. While classifying tumors in radiographic images is a critical task in the health sector, it is a hard and time-consuming procedure that radiologists must undertake, with the accuracy of their analysis entirely depending on their knowledge. Today's radiological diagnostic, such as magnetic resonance (MR) tests, is mostly subjective and may be inadequately accurate, posing a significant danger to patients. As a result, harnessing Artificial Intelligence (AI) technologies to decrease diagnostic mistakes is critical. This work used deep learning and radiomics to develop an automated approach for identifying cancers in patients using magnetic resonance imaging (MRI) datasets from kaggle third party API database. The suggested approach employs a Convolutional Neural Network (CNN) with transfer learning as our deep learning model for performing binary classification on our magnetic resonance images. In other words, this study used a pre-trained AlexNet model and moved it to a CNN architecture, and then assessed the model's efficacy and performance using an image dataset that the model had never seen after training, achieving an accuracy of 93.1 percent.">BRAIN TUMOR CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK</span></td>  
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             <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="It is prevalent that buildings are one of the fastest growing energy-consuming sectors since they account for one-third of the global final energy consumption. Strategies are needed to ameliorate buildings’ energy efficiency to mitigate the impact of this growing demand. The goal of this ongoing work is to develop a context-aware predictive framework to compose and support the automated building energy self-assessment and optimization services. This presentation overviews the proposed framework and details the initial contributions on extending energy-related ontologies for better describing building subsystems and their energy consumption.">CONTEXT-AWARE PREDICTIVE FRAMEWORK FOR BUILDING ENERGY SELF-ASSESSMENT AND OPTIMIZATION</span></td>  
             <td style="width:30px">&nbsp;</td>
             <td style="width:30px">&nbsp;</td>
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<h3><strong>January 2022</strong></h3>
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<table border="0.5" cellspacing="0" style="background:#89B085">
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<tr>
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            <td align="center" style="width:100px">13</td>
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            <td style="width:300px">Rohit Kumar</td>
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            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="The complex engineering research field of cyber-physical systems (CPS) is based on integrating computation, communication, and physical processes, providing design, modelling, and analysis techniques as a whole. In this talk, we will present an architectural model of CPS and discuss the requirements and challenges to make CPS safe.">Architectural support and mechanisms for resilient and safe control in Cyber-Physical System</span></td>
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            <td style="width:30px">&nbsp;</td>
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</tr>
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        <tr>
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            <td align="center" style="width:100px">13</td>
 +
            <td style="width:300px">Daniel Ângelo</td>
 +
            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="Tor is one of the most popular anonymity networks in the world. Users of this platform range from dissidents to cybercriminals or even ordinary citizens concerned with their privacy. It is based on advanced security mechanisms to provide strong guarantees against traffic correlation attacks that can deanonymize its users and services.
 +
Torpedo is the first known traffic correlation attack on Tor that aims at deanonymizing OS sessions. In a federated way, servers belonging to ISPs around the globe can process deanonymization queries of specific IPs. With the abstraction of an interface, these queries can be submitted by an attacker to deanonymize OSes and clients.
 +
Initial results show that this attack is able to identify the IP addresses of OS sessions with high confidence (no false positives). However, the current version of Torpedo relies on a central authority to manage the system, which requires ISPs to share sensitive network traffic of their
 +
clients with a third party.
 +
Thus, this work seeks to complement the previously developed research with the introduction and study of multi-party computation (MPC) techniques, with the objective of developing and assessing a new attack vector on Tor that can work even if ISPs encrypt their network traffic before correlation. In more detail, we intend to leverage, test and assess some existing general-purpose and machine learning oriented MPC frameworks and build a privacy- preserving solution on top of Torpedo that satisfies its scalability and performance requirements.">Privacy-preserving Deanonymization of Dark Web Tor Onion Services for Criminal Investigations</span></td>
 +
            <td style="width:30px">&nbsp;</td>
 +
</tr>
 +
        <tr>
 +
            <td align="center" style="width:100px">27</td>
 +
            <td style="width:300px">Robin Vassantlal</td>
 +
            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="Byzantine Fault-Tolerant (BFT) State Machine Replication (SMR) is a classical paradigm for implementing trustworthy services that has received renewed interest with the emergence of blockchains and decentralized infrastructures. A fundamental limitation of BFT SMR is that it provides integrity and availability despite a fraction of the replicas being controlled by an active adversary, but does not offer any confidentiality protection. Previous works addressed this issue by integrating secret sharing with the consensus-based framework of BFT SMR, but without providing all features required by practical systems, which include replica recovery, group reconfiguration, and acceptable performance when dealing with a large number of secrets. We present COBRA, a new protocol stack for Dynamic Proactive Secret Sharing that allows implementing confidentiality in practical BFT SMR systems. COBRA exhibits the best asymptotic communication complexity and optimal storage overhead, being able to renew 100k shares in a group of ten replicas 5 times faster than the current state of the art.">COBRA: Dynamic Proactive Secret Sharing for Confidential BFT Services</span></td>
 +
            <td style="width:30px">&nbsp;</td>
 +
</tr>
 +
        <tr>
 +
            <td align="center" style="width:100px">27</td>
 +
            <td style="width:300px">João Loureiro</td>
 +
            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="Connected and autonomous vehicles aim to improve passenger safety and driving quality of experience. However, current self-driving solutions still constitute an entry barrier to many potential users due to their cost and the offloading of the self-driving algorithms to reduce the onboard computing requirements. At the same time, a viable alternative requires a stable connection to the cloud. This work explores deep learning concepts to forecast mobile network KPIs. These models may ultimately be used to adjust the vehicle's operational parameters to improve network signal quality and ensure a reliable connection to the cloud servers.">Deep learning for communication optimization on autonomous vehicles</span></td>
 +
            <td style="width:30px">&nbsp;</td>
 +
</tr>
 +
</table>
 +
 +
<h3><strong>February 2022</strong></h3>
 +
<table border="0.5" cellspacing="0" style="background:#89B085">
 +
<tr>
 +
            <td align="center" style="width:100px">10</td>
 +
            <td style="width:300px">Carlos Mão de Ferro</td>
 +
            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="">TBD</span></td>
 +
            <td style="width:30px">&nbsp;</td>
 +
</tr>
 +
        <tr>
 +
            <td align="center" style="width:100px">10</td>
 +
            <td style="width:300px">David Silva</td>
 +
            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="">Developing a scalable IoT solution for remote monitoring and control</span></td>
 +
            <td style="width:30px">&nbsp;</td>
 +
</tr>
 +
        <tr>
 +
            <td align="center" style="width:100px">24</td>
 +
            <td style="width:300px">Adriano Mão de Ferro</td>
 +
            <td style="width:600px"><span style="border-bottom: dashed 1px #000" title="">TBD</span></td>
 +
            <td style="width:30px">&nbsp;</td>
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</tr>
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</table>
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</div>
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Revision as of 20:25, 7 March 2022

The NavTalks is a series of informal talks given by Navigators members or some special guests about every two-weeks at Ciências, ULisboa.

Leave mouse over title's presentation to read the abstract.



Contents

Upcoming presentations

March 2022

10 Miracle Aniakor CONTEXT-AWARE PREDICTIVE FRAMEWORK FOR BUILDING ENERGY SELF-ASSESSMENT AND OPTIMIZATION  
10 Diogo Duarte TBD  
24 Nuno Dionísio TBD  

April 2022

7 Samaneh Shafee TBD  
7 Diogo Pires TBD  
21 Žygimantas Jasiūnas TBD  

May 2022

5 Allan Espíndola TBD  
5 Gabriel Freitas TBD  
19 Tiago R. N. Carvalho TBD  
19 Gonçalo Reis TBD  

June 2022

2 Rafael Ramires TBD  
2 Inês Sousa Integration of various data sources and implementation of a dashboard for the remotemonitorization system  
16 Pedro Rosa Lightweight Cryptography for Internet of Things (IoT) Devices  
16 Jorge Martins TBD  
30 Pedro Alves TBD  
30 Lívio Rodrigues TBD  

July 2022

14 Miguel Oliveira TBD  



Past presentations

September 2018

20 Alysson Bessani SMaRtChain: A Principled Design for a New Generation of Blockchains  
20 Rui Miguel Named Data Networking with Programmable Switches  

October 2018

4 Bruno Vavala (Research Scientist in Intel Labs) Private Data Objects  
4 Marcus Völp (Research Scientist, CritiX, SnT, Univ. of Luxembourg) Reflective Consensus  
18 Yair Amir (Professor, Johns Hopkins University) Timely, Reliable, and Cost-Effective Internet Transport Service using Structured Overlay Networks  

November 2018

13 Salvatore Signorello The Past, the Present and some Future of Interest Flooding Attacks in Named-Data Networking  
13 Tiago Oliveira Vawlt - Privacy-Centered Cloud Storage  
27 Nuno Neves Segurança de Software - Como Encontrar uma Agulha num Palheiro?  
27 Ricardo Mendes Vawlt - The Zero-knowledge End-to-end Encryption Protocol  

December 2018

11/12 António Casimiro AQUAMON: Dependable Monitoring with Wireless Sensor Networks in Water Environments  
11/12 Carlos Nascimento Review of wireless technology for AQUAMON: Lora, sigfox, nb-iot  

January 2019

15/01 Fernando Alves A comparison between vulnerability publishing in OSINT and Vulnerability Databases  
15/01 Ibéria Medeiros SEAL: SEcurity progrAmming of web appLications  
29/01 Fernando Ramos Networks that drive themselves…of the cliff  
29/01 Miguel Garcia Some tips before rushing into LaTeX (adapted from: How (and How Not) to Write a Good Systems Paper)  

February 2019

19/02 Ana Fidalgo Conditional Random Fields and Vulnerability Detection in Web Applications  
19/02 João Sousa Towards BFT-SMaRt v2: Blockchains and Flow Control  

March 2019

13/03 Fernando Ramos How to give a great -- OK, at least a good -- research talk  
13/03 Ricardo Morgado Automatically correcting PHP web applications  


March 2019

27/03 Nuno Dionísio Cyberthreat Detection from Twitter using Deep Neural Networks  
27/03 Fernando Ramos My network protocol is better than yours!  


April 2019

10/04 Adriano Serckumecka SIEMs  
10/04 Tulio Ribeiro BFT Consensus & PoW Consensus (blockchain).  


May 2019

08/05 Miguel Garcia Diverse Intrusion-tolerant Systems  
29/05 Pedro Ferreira The concept of the next navigators cybersecurity H2020 project  
29/05 Vinicius Cogo Auditable Register Emulations  


June 2019

05/06 Diogo Gonçalves Network coding switch  
05/06 Francisco Araújo Generating Software Tests To Check For Flaws and Functionalities  
26/06 Joao Pinto Implementation of a Protocol for Safe Cooperation Between Autonomous Vehicles  
26/06 Tiago Correia Design and Implementation of a Cloud-based Membership System for Vehicular Cooperation  
26/06 Robin Vassantlal Confidential BFT State Machine Replication  


March 2021

24 Ana Fidalgo Machine Learning approaches for vulnerability detection  

April 2021

7 Vasco Leitão Discovering Association Rules Between Software System Requirements and Product Specifications  
21 João Caseirito Improving Web Application Vulnerability Detection Leveraging Ensemble Fuzzing  


May 2021

5 Paulo Antunes Web Vulnerability Discovery at an Intermediate Language Level  
19 Frederico Apolónia Building Occupancy Assessment  

June 2021

2 Bernardo Portela Secure Conflict-free Replicated Data Types  
16 Žygimantas Jasiūnas and Vasco Ferreira Monitoring and Integration of heterogeneous building IoT platforms and smart systems  
30 João Inácio Automatic Removal of Flaws in Embedded System Software  

July 2021

14 André Gil Platform Architecture and data management for cloud-based buildings energy self-assessment and optimization  
28 João Valente Data quality and dependability of IoT platform for buildings energy assessment  

December 2021

16 Paulo Antunes Web Vulnerability Discovery at an Intermediate Language Level  
16 Bruno Matos TBD  

January 2022

13 Rohit Kumar Architectural support and mechanisms for resilient and safe control in Cyber-Physical System  
13 Daniel Ângelo Privacy-preserving Deanonymization of Dark Web Tor Onion Services for Criminal Investigations  
27 Robin Vassantlal COBRA: Dynamic Proactive Secret Sharing for Confidential BFT Services  
27 João Loureiro Deep learning for communication optimization on autonomous vehicles  

February 2022

10 Carlos Mão de Ferro TBD  
10 David Silva Developing a scalable IoT solution for remote monitoring and control  
24 Adriano Mão de Ferro TBD  











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