SHARKS: Smart hacking approaches for risk scanning in Internet-of-Things and cyber-physical systems based on machine learning
Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are increasingly being
deployed across multiple functionalities, ranging from healthcare devices and wearables to …
deployed across multiple functionalities, ranging from healthcare devices and wearables to …
Overview of security for smart cyber-physical systems
The tremendous growth of interconnectivity and dependencies of physical and cyber
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …
GRAVITAS: Graphical reticulated attack vectors for Internet-of-Things aggregate security
Internet-of-Things (IoT) and cyber-physical systems (CPSs) may consist of thousands of
devices connected in a complex network topology. The diversity and complexity of these …
devices connected in a complex network topology. The diversity and complexity of these …
Machine learning for security and the internet of things: the good, the bad, and the ugly
The advancement of the Internet of Things (IoT) has allowed for unprecedented data
collection, automation, and remote sensing and actuation, transforming autonomous …
collection, automation, and remote sensing and actuation, transforming autonomous …
Deep learning based attack detection for cyber-physical system cybersecurity: A survey
With the booming of cyber attacks and cyber criminals against cyber-physical systems
(CPSs), detecting these attacks remains challenging. It might be the worst of times, but it …
(CPSs), detecting these attacks remains challenging. It might be the worst of times, but it …
IoT network security: Threats, risks, and a data-driven defense framework
The recent surge in Internet of Things (IoT) deployment has increased the pace of
integration and extended the reach of the Internet from computers, tablets and phones to a …
integration and extended the reach of the Internet from computers, tablets and phones to a …
MEML: Resource-aware MQTT-based machine learning for network attacks detection on IoT edge devices
A Shalaginov, O Semeniuta, M Alazab - Proceedings of the 12th IEEE …, 2019 - dl.acm.org
Growing number of Smart Applications in recent years bring a completely new landscape of
cyber-attacks and exploitation scenario that have not been seen in wild before. Devices in …
cyber-attacks and exploitation scenario that have not been seen in wild before. Devices in …
Threatzoom: neural network for automated vulnerability mitigation
E Aghaei, E Al-Shaer - Proceedings of the 6th Annual Symposium on Hot …, 2019 - dl.acm.org
Increasing the variety and quantity of cyber threats becoming the evident that traditional
human-in-loop approaches are no longer sufficient to keep systems safe. To address this …
human-in-loop approaches are no longer sufficient to keep systems safe. To address this …
Threat hunting architecture using a machine learning approach for critical infrastructures protection
M Aragonés Lozano, I Pérez Llopis… - Big data and cognitive …, 2023 - mdpi.com
The number and the diversity in nature of daily cyber-attacks have increased in the last few
years, and trends show that both will grow exponentially in the near future. Critical …
years, and trends show that both will grow exponentially in the near future. Critical …
Machine learning-based network vulnerability analysis of industrial Internet of Things
It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially
devastating consequences in case of an attack. Machine learning (ML) and big data …
devastating consequences in case of an attack. Machine learning (ML) and big data …