Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020,
indicating an increase of more than 50% since 2018. With the average cyber insurance …

Near-infrared (NIR) spectroscopy as a low-cost technique with its non-destructive fast
nature, precision, control, accuracy, repeatability, and reproducibility has been extensively …

The development of an anomaly-based intrusion detection system (IDS) is a primary
research direction in the field of intrusion detection. An IDS learns normal and anomalous …

High-speed networks are becoming common nowadays. Naturally, a challenge that arises is
that the intrusion detection system (IDS) should timely detect attacks in huge volumes of …

The pursuit to spot abnormal behaviors in and out of a network system is what led to a
system known as intrusion detection systems for soft computing besides many researchers …

Network intrusion detection remains a challenging research area as it involves learning from
large-scale imbalanced multiclass datasets. While machine learning algorithms have been …

The continued ability to detect malicious network intrusions has become an exercise in
scalability, in which data mining techniques are playing an increasingly important role. We …

The 5G network provides higher bandwidth and lower latency for edge IoT devices to access
the core business network. But at the same time, it also expands the attack surface of the …

Intrusion detection systems (IDSs) are one of the promising tools for protecting data and
networks; many classification algorithms, such as neural network (NN), Naive Bayes (NB) …

As an important means to ensure network safety, intrusion detection technology can be
much more efficient by introducing machine learning. The present paper proposes a …