from 1st March to 15th May

Publications & Demonstrators

All accepted publications from SPARTA partners under its funding as well as videos presenting some of the work done under SPARTA


Self-secured PUF: Protecting the Loop PUF by Masking

Tebelmann, L., Danger, J. L., & Pehl, M.

Physical Unclonable Functions (PUFs) provide means to gen-erate chip individual keys, especially for low-cost applications such as theInternet of Things (IoT). They are intrinsically robust against reverseengineering, and more cost-effective than non-volatile memory (NVM).For several PUF primitives, countermeasures have been proposed to mit-igate side-channel weaknesses. However, most mitigation techniques re-quire...More>>

Domains: Physically, Unclonable, Function, Side-Channel, Analysis, RO, PUF, Loop, Masking, Countermeasure, IoT

Ensemble-Based Classification Using Neural Networks and Machine Learning Models for Windows PE Malware DetectionNeural Networks

Damaševičius, Robertas; Venčkauskas, Algimantas; Toldinas, Jevgenijus; Grigaliūnas, Šarūnas

The security of information is among the greatest challenges facing organizations and institutions. Cybercrime has risen in frequency and magnitude in recent years, with new ways to steal, change and destroy information or disable information systems appearing every day. Among the types of penetration into the information systems where confidential...More>>

Domains: Neural, Networks, Machine, Learning, Malware

Information Sharing in Cyber Defence Exercises

Eduardas Kutka, Aušrius Juozapavičius, Linas Bukauskas, Agnė Brilingaitė

Availability and easy access to sophisticated cyber penetration testing tools enable exploitation of vulnerabilities in different systems globally. Repetitive nature and recognisable signatures of attacks raise demand for effective information sharing. Timely warnings about cyber incidents in other systems make it possible to identify related attacks locally. International cyber community...More>>

Domains: cyber, defence, exercises, incident, information, sharing, indicators, of, compromise, collaborative

Machine Learning for Driver Detection through CAN bus

Fabio Martinelli, Francesco Mercaldo, Antonella Santone

In last years vehicular network safety and security are attracting interest from both industries and researchers. In this paper, starting from a set of features gathered from the in-vehicle CAN bus, we show how machine learning algorithms can be useful to discriminate between the car owner and impostors. Furthermore, we...More>>

Domains: Automated, vehicles, Machine, Learning

Model checking and machine learning techniques for HummingBad mobile malware detection and mitigation

Fabio Martinelli, Francesco Mercaldo, Vittoria Nardone, Antonella Santone, Gigliola Vaglini

Android currently represents the most widespread operating system focused on mobile devices. It is not surprising that the majority of malware is created to perpetrate attacks targeting mobile devices equipped with this operating systems. In the mobile malware landscape, there exists a plethora of malware families exhibiting different malicious behaviors....More>>

Domains: Android

Towards the Use of Generative Adversarial Neural Networks to Attack Online Resources

Lelio Campanile, Mauro Iacono, Fabio Martinelli, Fiammetta Marulli, Michele Mastroianni, Francesco Mercaldo, Antonella Santone

The role of remote resources, such as the ones provided by Cloud infrastructures, is of paramount importance for the implementation of cost effective, yet reliable software systems to provide services to third parties. Cost effectiveness is a direct consequence of a correct estimation of resource usage, to be able to...More>>

Domains: Security, Generative, Adversarial, Networks, Deep, learning, Cloud, Serverless, Software, services, Microservices, Energy, attacks

Visualizing the outcome of dynamic analysis of Android malware with VizMal

Andrea De Lorenzo, Fabio Martinelli, Eric Medvet, Francesco Mercaldo, Antonella Santone

Malware detection techniques based on signature extraction require security analysts to manually inspect samples to find evidences of malicious behavior. This time-consuming task received little attention by researchers and practitioners, as most of the effort is on the identification as malware or non-malware of an entire sample. There are no...More>>

Domains: Malware, analysisAndroidMachine, learningMultiple, instance, learningLSTM-RNNSecurity

VisualDroid: automatic triage and detection of Android repackaged applications

Rosangela Casolare, Carlo De Dominicis, Fabio Martinelli, Francesco Mercaldo, Antonella Santone

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>>

Domains: Android

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>>

Domains: Classification