In order to improve the security and reliability of computer network, especially the three
aspects of fault network diagnosis overall model, computer network link fault detection and …

In the field of intrusion detection, existing deep learning algorithms have limited capability to
effectively represent network data features, making it challenging to model the complex …

Researching new methods of detecting network threats, eg, malware-related, requires large
and diverse sets of data. In recent years, a variety of network traffic datasets have been …

Intrusion detection is a key topic in computer security. Due to the ever-increasing number of
network attacks, several accurate anomaly-based techniques have been proposed for …

Internet of things (IoT) systems were becoming increasingly complex due to advancements
in open innovation, especially in the realms of intelligent automation and artificial …

The exponential growth of intrusions on networked systems inspires new research directions
on developing artificial intelligence (AI) techniques for intrusion detection systems (IDS). In …

Owing to the exponential proliferation of internet services and the sophistication of
intrusions, traditional intrusion detection algorithms are unable to handle complex invasions …

The ever-growing occurrence of computer security incidents calls for advanced intrusion
detection techniques. A wide body of literature dealing with Intrusion Detection Systems …

In this work, we applied 8 machine learning (ML) techniques to detect intrusions, namely,
neural networks, kNN, SVM, random forest, trees, AdaBoost, naive Bayes, and stochastic …

The number of papers on machine learning and deep neural networks applied to intrusion
detection systems (IDS) is ever-increasing. Differently from existing work on the topic, this …