Smart community networks bring great comfort and convenience for people, but also
increase security risks of exposing system vulnerabilities and private data to network …

Abstract The Machine Learning-based Intrusion Detection System (ML-IDS) becomes more
popular because it doesn't need to manually update the rules and can recognize variants …

The rapid growth in Internet of Things (IoT) technology has become an integral part of
today's industries forming the Industrial IoT (IIoT) initiative, where industries are leveraging …

Intrusion detection in the Internet of Things is becoming increasingly important as the
number of connected devices grows. Machine learning algorithms can be applied to detect …

With the continuous occurrence of cybersecurity incidents, network intrusion detection has
become one of the most critical issues in cyber ecosystems. Although previous machine …

Nowadays, in machine learning based intrusion detection systems, ensemble learning is a
commonly adopted method to improve the detection accuracy. Unfortunately, the existing …

An ensemble learning approach for classification in intrusion detection is proposed. Its
application to the KDD Cup 99 and NSL-KDD datasets consistently increases the …

Cybercrime is growing at a rapid pace, and its techniques are becoming more sophisticated.
In order to actively cope with such threats, new approaches based on machine learning and …

Network intrusion detection systems (NIDSs) play an important role in computer network
security. There are several detection mechanisms where anomaly-based automated …

The increasing popularity and widespread use of Internet of Things (IoT) and Cyber-Physical
Systems (CPS) technologies have produced a significant need for the integration of cloud …