This is a survey of neural network applications in the real-world scenario. It provides a
taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of …

Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …

Incremental learning refers to learning from streaming data, which arrive over time, with
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …

A computerized technique is provided to analyze a message for malware by determining
context information from attributes of the message. The attributes are determined by …

Network data constantly evolves with new network applications and protocols. There is a
need for robust techniques to detect anomalous behaviour. Offline models trained with static …

Data-driven methods have been widely used in network intrusion detection (NID) systems.
However, there are currently a number of challenges derived from how the datasets are …

Traffic anomalies arise from network problems, and so detection and diagnosis are useful
tools for network managers. A great deal of progress has been made on this problem so far …

(57) ABSTRACT A computerized technique is provided to analyze a message for malware
by determining context information from attri butes of the message. The attributes are …

With the advancement in technology, normal network traffic is becoming more
heterogeneous. In this scenario, the problem of detecting anomalies is intensified. In the …

The real-time analysis of network data is of great significance to network security.
Visualization technology and machine learning can assist in network data analysis from …