Considering the pervasiveness of mobile devices, malicious writers are constantly focusing their attention in developing malicious payload aimed to gather sensible information from mobile devices without user content. As a matter of fact, it is really easy for malware writers to embed malicious payloads into legitimate applications, by applying the...More>>
Publications & Demonstrators
All accepted publications from SPARTA partners under its funding as well as videos presenting some of the work done under SPARTA
Publications
Towards Visual Debugging for Multi-Target Time Series Classification
Udo Schlegel, Eren Cakmak, Hiba Arnout, Mennatallah El-Assady, Daniela Oelke, Daniel A Keim
Multi-target classification of multivariate time series data poses a challenge in many real-world applications (e.g., predictive main- tenance). Machine learning methods, such as random forests and neural networks, support training these classifiers. However, the debugging and analysis of possible misclassifications remain chal- lenging due to the often complex relations between...More>>
Towards Using Source Code Repositories to Identify Software Supply Chain Attacks
Duc Ly Vu, Ivan Pashchenko, Fabio Massacci, Henrik Plate, Antonino Sabetta
Increasing popularity of third-party package repositories, like NPM, PyPI, or RubyGems, makes them an attractive target for software supply chain attacks. By injecting malicious code into legitimate packages, attackers were known to gain more than 100 000 downloads of compromised packages. Current approaches for identifying malicious payloads are resource demanding....More>>
Towards Quantum-Enhanced Machine Learning for Network Intrusion Detection
Arnaldo Gouveia, Miguel Correia
Network Intrusion Detection Systems (NIDSs) are commonly used today to detect malicious activities. Quantum computers, despite not being practical yet, are becoming available for experimental purposes. We present the first approach for applying unsupervised Quantum Machine Learning (QML) in the context of network intrusion detection from the perspective of quantum...More>>
Towards an Interpretable Deep Learning Model for Mobile Malware Detection and Family Identification
Giacomo Iadarola, Fabio Martinelli, Francesco, Antonella Santone
Mobile devices are pervading everyday activities of our life. Each day we store a plethora of sensitive and private information in smart devices such as smartphones or tablets, which are typically equipped with an always-on internet connection. These information are of interest for malicious writers that are developing more and...More>>
Machine Learning - the results are not the only thing that matters! What about security, explainability and fairness?
Szczepanski, Choras, Pawlicki, Kozik
Recent advances in machine learning (ML) and the surge in computational power have opened the way to the proliferation of ML and Artificial Intelligence (AI) in many domains and applications. Still, apart from achieving good accuracy and results, there are many challenges that need to be discussed in order to...More>>
SEkey: A Distributed Hardware-based Key Management System
Matteo Fornero, Nicolò Maunero, Paolo Prinetto, Antonio Varriale
Cryptography plays a key role in all the aspects of today cybersecurity and any cryptographic approach relies on cryptographic keys, i.e., series of bits that determine how a plain text is encrypted and decrypted, according to an agreed algorithm. The secrecy and security of an encryption key are thus crucial...More>>
Requirements for a Lightweight AKE for OSCORE
M. Vucinic, G. Selander, J. Mattsson, D. Garcia
This document compiles the requirements for a lightweight authenticated key exchange protocol for OSCORE.
More>>Predicting Probability of Default Under IFRS 9 Through Data Mining Techniques
Fabio Martinelli, Francesco Mercaldo, Domenico Raucci, Antonella Santone
Data mining techniques were employed to automatise decision-making processes in several domains. In the banking context, the introduction of IFRS 9 on Financial Instruments has impacted not only on the area of accounting and financial reporting, but also on banks’ credit risk measurement and management processes, promoting effective and efficient...More>>