Intrusion detection (ID) is critical in securing computer networks against various malicious
attacks. Recent advancements in machine learning (ML), deep learning (DL), federated …

IoT devices generate enormous amounts of data, which deep learning algorithms can learn
from more effectively than shallow learning algorithms. The approach for threat detection …

Network forensics focuses on the identification and investigation of internal and external
network attacks, the reverse engineering of network protocols, and the uninstrumented …

With the rapid growth of social media networks and internet accessibility, most businesses
are becoming vulnerable to a wide range of threats and attacks. Thus, intrusion detection …

Network forensics aids in the identification of distinct network-based attacks through packet-
level analysis of collected network traffic. It also unveils the attacker's intentions and …

In today's interconnected world, network security is of utmost importance for individuals,
corporations, and governments. Traditional security measures are insufficient to counter …

Network intrusion detection systems (NIDS) play a critical role in maintaining the security
and integrity of computer networks. These systems are designed to detect and respond to …

Embedded systems, including the Internet of things (IoT), play a crucial role in the
functioning of critical infrastructure. However, these devices face significant challenges such …

It is crucial to protect digital systems from cyberattacks in our ever-evolving digital world.
Malicious behavior, viruses, and intrusions provide serious threats to both persons and …

The emergence of Machine Learning (ML) strategies within the scope of community security
has led to principal advances in improving clever Artificial Intelligence (AI) primarily based …