The emergence of new network architectures, protocols, and tools has made it easier for
cybercriminals to launch attacks using AI-based tools, presenting challenges in network …

The rapid advancement of network architectures, protocols, and tools poses significant
challenges to network security, especially due to the use of AI-based tools by cybercriminals …

Malicious traffic detection over the internet is one of the challenging areas for researchers to
protect network infrastructures from any malicious activity. Several shortcomings of a …

Identifying attack traffic is very important for the security of Internet of ings (IoT) in smart cities
by using machine learning (ML) algorithms. Recently, the IoT security research community …

Multi-environment networks, such as those in smart homes, handle both IoT and traditional
IP-based traffic. Weak security protocols in IoT devices and the diverse traffic flow make …

Cybersecurity is a common Internet of Things security challenge. The lack of security in IoT
devices has led to a great number of devices being compromised, with threats from both …

Over the last years, several works introduced network-based intrusion detection schemes
based on machine learning techniques for securing IoT devices. Despite the promising …

Identification of anomaly and malicious traffic in the Internet of things (IoT) network is
essential for IoT security. Tracking and blocking unwanted traffic flows in the IoT network is …

Publicly available datasets are an indispensable tool for researchers, as they allow testing
new algorithms on a wide range of different scenarios and making scientific experiments …

IoT devices are notoriously vulnerable even to trivial attacks and can be easily
compromised. In addition, resource constraints and heterogeneity of IoT devices make it …