In the paper, we demonstrate a neuromorphic cognitive computing approach for Network
Intrusion Detection System (IDS) for cyber security using Deep Learning (DL). The …

The protection of modern distributed information networks from external and internal
intruders continues to be of great importance due to the development of data transmission …

In recent years, the growth of internet of things (IoT) is immense, and the observations of
their evolution need to be carried out effectively. The development of the IoT has been …

The paper suggests an approach to construction of the system for collecting, storing and
processing of data and security events on the basis of aggregation of instruments provided …

With the advancement of the internet over the years, the number of attacks over the internet
has also increased. A powerful intrusion detection system (IDS) is required to ensure the …

Machine learning techniques play a crucial part in intrusion detection and greatly change
the original intrusion detection methods. How to use machine learning technologies to …

Intrusion detection is a relevant layer of cybersecurity to prevent hacking and illegal activities
from happening on the assets of corporations. Anomaly-based Intrusion Detection Systems …

摘要网络空间拟态防御系统(Cyberspace Mimic Defense System, CMDS)
采用动态异构冗余架构以及多模表决机制将不确定威胁转化为概率可控的事件 …

This study examines previous research works in applying feature selection to Intrusion
Detection. Feature selection has proven to improve or maintain similar classification …

We propose a data-driven distributed machine learning approach to scalably predict the
trustworthiness of homogeneous IoT services in heterogeneous Mobile Edge Computing …