A novel online Compromised Device Identification System (CDIS) is presented to identify IoT
devices and/or IP addresses that are compromised by a Botnet attack, within a set of sources …

This paper proposes a method to assess the security of an device, or IP address, IoT
network by simultaneously identifying all the compromised IoT devices and IP addresses. It …

This paper presents several novel algorithms for real-time cyberattack detection using the
Auto-Associative Deep Random Neural Network. Some of these algorithms require offline …

Cyberattacks are increasingly threatening net-worked systems, often with the emergence of
new types of unknown (zero-day) attacks and the rise of vulnerable devices. uch attacks can …

This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which
enables a fully online Machine Learning (ML) based Intrusion Detection System (IDS) that …

The IoT is vulnerable to network attacks, and Intrusion Detection Systems (IDS) can provide
high attack detection accuracy and are easily installed in IoT Servers. However, IDS are …

Projects that have addressed cybersecurity using techniques based on Machine Learning,
including the security of Mobile Networks and the Internet of Things (IoT). These research …

This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which
enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that …

This article summarizes some of the research contributions of several EU Projects including
NEMESYS, GHOST, KONFIDO, SDK4ED, SerIoT and IoTAC, primarily with a cybersecurity …