Machine learning is rapidly changing the cybersecu-rity landscape. The use of predictive
models to detect malicious activity and identify inscrutable attack patterns is providing levels …

Abstract The Internet of Things (IoT) connects daily use devices to the Internet, such as
home appliances, health care equipment, sensors, and industrial devices. Concurrently …

The Internet of Things (IoT) devices, networks, and applications have become an integral
part of modern societies. Despite their social, economic, and industrial benefits, these …

Cyber attackers exploit a network of compromised computing devices, known as a botnet, to
attack Internet-of-Things (IoT) networks. Recent research works have recommended the use …

Botnet attacks are a major threat to networked systems because of their ability to turn the
network nodes that they compromise into additional attackers, leading to the spread of high …

The Internet of Things (IoT) connects different sensors, devices, applications, databases,
services, and people, bringing improvements to various aspects of our lives, such as cities …

With the growth of internet of things (IoT) systems, they have become the target of malicious
third parties. In order to counter this issue, realistic investigation and protection …

In recent years, anomaly detection and machine learning for intrusion detection systems
have been used to detect anomalies on Internet of Things networks. These systems rely on …

The rapidly increasing use of the internet has led to an increase in new devices and
technologies; however, attack and security violations have grown exponentially as well. In …

In this work we seek to determine the feasibility of implementing deep learning-based
intrusion detection on higher-capacity embedded devices, by evaluating the performance …