Fog Computing is a new paradigm which is meant to solve some new challenges in IoT like a wide-spread geographical distribution and mobility of the devices, multiple nodes, heterogeneity of the hardware capabilities and communication technologies. A Fog Computing Orchestration enables the control of multiple devices connected to the Fog...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
Omega: a Secure Event Ordering Service for the Edge
Cláudio Correia, Luis Rodrigues, Miguel Correia
Edge computing is a paradigm that extends cloud computing with storage and processing capacity close to the edge of the network that can be materialized by using many fog nodes placed in multiple geographic locations. Fog nodes are likely to be vulnerable to tampering, so it is important to secure...More>>
Domains: Security, IoT, Fog, Edge, Intel SGX
Neural Networks for Driver Behavior Analysis
Fabio Martinelli, Fiammetta Marulli, Francesco Mercaldo, Antonella Santone
The proliferation of info-entertainment systems in nowadays vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. With the purpose to provide an architecture to increase safety and security in automotive context, in this paper we propose a fully connected...More>>
Domains: Automotive, Artificial intelligence, Neural network, Machine learning, Deep learning, Safety
Network Intrusion Detection XGBoost(ing)
Arnaldo Gouveia, Miguel Correia
XGBoost is a recent machine learning method that has been getting increasing attention. It won Kaggle’s Higgs Machine Learning Challenge, among several other Kaggle competitions, due to its performance. In this , we explore the use of XGBoost in the context of anomaly-based network intrusion detection, an area in which...More>>
Domains: Machine learning
MobHide: App-level Runtime Data Anonymization on Mobile
Davide Caputo, Luca Verderam, Alessio Merlo
Developers of mobile apps gather a lot of user’s personal information at runtime by exploiting third-party analytics libraries, without keeping the owner (i.e., the user) of such information in the loop. We argue that this is somehow paradoxical. To overcome this limitation, in this paper, we discuss a methodology (i.e.,...More>>
Domains: Android privacy, Analytics libraries, Data anonymization
Minimal Virtual Machines on IoT Microcontrollers: The Case of Berkeley Packet Filters with rBPF
K. Zandberg, E. Baccelli
Virtual machines (VM) are widely used to host and isolate software modules. However, extremely small memory and low-energy budgets have so far prevented wide use of VMs on typical microcontroller-based IoT devices. In this paper, we explore the potential of two minimal VM approaches on such low-power hardware. We design...More>>
Domains: Networking and Internet Architecture, Operating Systems
Malicious Collusion Detection in Mobile Environment by means of Model Checking
Rosangela Casolare, Fabio Martinelli, Francesco Mercaldo, Antonella Santone
Everyday born a new cyberattack and among these an emerging attack is represent by the so-called colluding. The application collusion attack is a new form of threat that is becoming widespread in mobile environment, especially in Android platform. This technique requires that two or more apps cooperate in some way...More>>
Domains: Colluding, Model checking, Formal methods, Android, Security
Low-Power IoT Communication Security: On the Performance of DTLS and TLS 1.3
G. Restuccia, H.Tschofenig, E. Baccelli
Similarly to elsewhere on the Internet, practical security in the Internet of Things (IoT) is achieved by combining an array of mechanisms, at work at all layers of the protocol stack, in system software, and in hardware. Standard protocols such as Datagram Transport Layer Security (DTLS 1.2) and Transport Layer...More>>
Domains: Cryptography and Security, Networking and Internet Architecture
Image-based Malware Family Detection: An Assessment between Feature Extraction and Classification Techniques
Giacomo Iadarola, Fabio Martinelli, Francesco Mercaldo, Antonella Santone
The increasing number of malware in mobile environment follows the continuous growth of the app stores, which required constant research in new malware detection approaches, considering also the weaknesses of signature-based anti-malware software. Fortunately, most of the malware are composed of well-known pieces of code, thus can be grouped into...More>>
Domains: Machine Learning, Mobile Security, Android, Malware Classification, Image Texture Analysis