Countermeasures against adversarial machine learning based on eXplainable and Reliable Artificial Intelligence
Sara Narteni, Alberto Carlevaro
CNR-IEIIT PhD Students
CNR-IEIIT
event online by Microsoft Teams
April 13th, 2023
5.30 pm
CNR-IEIIT PhD Students
CNR-IEIIT
event online by Microsoft Teams
April 13th, 2023
5.30 pm
Abstract
On April 13th, 2023 at 5.30 pm, a new appointment of the “Thursday meetings – A glance to the future at cocktail-hour”, organized by EIIT – CNR Institute, will take place online by Microsoft Teams.
The series of fortnightly meetings deal with specific themes connected to IEIIT – CNR institute with transversal vision through application domains and technological areas with particular attention to their evolutions.
The next seminar is a special session of the "Thursday meetings", called "Youth of IEIIT", dedicated to young researchers in training (PhD students, research fellows, etc.) of the CNR-IEIIT who will be given the opportunity to present their research activities carried out within the reference research groups. The "Youth of IEIIT" sessions of the "Thursday meetings" will alternate with the classic sessions which will continue with the usual biweekly cadence.
The new seminar will be held by Dr. Sara Narteni (PhD) and Dr. Alberto Carlevaro (PhD).
Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and testing phases are more precise. An important concept to analyze when defining ML algorithms concerns adversarial machine learning attacks. These attacks aim to create manipulated datasets to mislead ML algorithm decisions.
In this talk, we will present our research on new approaches able to detect and mitigate malicious adversarial machine learning attacks against a ML system. In particular, we investigate the Carlini-Wagner (CW), the fast gradient sign method (FGSM) and the Jacobian based saliency map (JSMA) attacks. The aim of the work is to exploit detection algorithms as countermeasures to these attacks. Initially, we performed some tests by using canonical ML algorithms with a hyperparameters optimization to improve metrics. Then, we adopt original reliable AI algorithms, either based on eXplainable AI (Logic Learning Machine) or Support Vector Data Description (SVDD). The obtained results show how the classical algorithms may fail to identify an adversarial attack, while the reliable AI methodologies are more prone to correctly detect a possible adversarial machine learning attack. The evaluation of the proposed methodology was carried out in terms of good balance between FPR and FNR on real world application datasets: Domain Name System (DNS) tunneling, Vehicle Platooning and Remaining Useful Life (RUL). In addition, a statistical analysis was performed to improve the robustness of the trained models, including evaluating their performance in terms of runtime and memory consumption.
Participation is free but registration is required. Please register here.
The series of fortnightly meetings deal with specific themes connected to IEIIT – CNR institute with transversal vision through application domains and technological areas with particular attention to their evolutions.
The next seminar is a special session of the "Thursday meetings", called "Youth of IEIIT", dedicated to young researchers in training (PhD students, research fellows, etc.) of the CNR-IEIIT who will be given the opportunity to present their research activities carried out within the reference research groups. The "Youth of IEIIT" sessions of the "Thursday meetings" will alternate with the classic sessions which will continue with the usual biweekly cadence.
The new seminar will be held by Dr. Sara Narteni (PhD) and Dr. Alberto Carlevaro (PhD).
Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and testing phases are more precise. An important concept to analyze when defining ML algorithms concerns adversarial machine learning attacks. These attacks aim to create manipulated datasets to mislead ML algorithm decisions.
In this talk, we will present our research on new approaches able to detect and mitigate malicious adversarial machine learning attacks against a ML system. In particular, we investigate the Carlini-Wagner (CW), the fast gradient sign method (FGSM) and the Jacobian based saliency map (JSMA) attacks. The aim of the work is to exploit detection algorithms as countermeasures to these attacks. Initially, we performed some tests by using canonical ML algorithms with a hyperparameters optimization to improve metrics. Then, we adopt original reliable AI algorithms, either based on eXplainable AI (Logic Learning Machine) or Support Vector Data Description (SVDD). The obtained results show how the classical algorithms may fail to identify an adversarial attack, while the reliable AI methodologies are more prone to correctly detect a possible adversarial machine learning attack. The evaluation of the proposed methodology was carried out in terms of good balance between FPR and FNR on real world application datasets: Domain Name System (DNS) tunneling, Vehicle Platooning and Remaining Useful Life (RUL). In addition, a statistical analysis was performed to improve the robustness of the trained models, including evaluating their performance in terms of runtime and memory consumption.
Participation is free but registration is required. Please register here.
