Publication search
with Intrusion detection as keyword

Research on Deep Learning Method Based on Blockchain and Intrusion Detection Model

Published in Journal of Physics: Conference Series

The openness of network data makes it vulnerable to hackers, viruses and other attacks. These attacks not only become more and more complex, but also seriously threaten users’ privacy and property security. In order to improve the accuracy of testing, intrusion detection is a detection method of network security communication based on traditional i...

Security intrusion detection using quantum machine learning techniques

Published in Journal of Computer Virology and Hacking Techniques

Conventional machine learning approaches applied for the security intrusion detection degrades in case of big data input ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document...

Detecting APT through graph anomaly detection

Despite fruitful achievements made by unsupervised machine learning-based anomaly detection for network intrusion detection systems, they are still prone to the issue of high false alarm rates, and it is still difficult to reach very high recalls. In 2020, Leichtnam et al. proposed Sec2graph, an unsupervised approach applied to security objects gra...

Towards generic quality assessment of synthetic traffic for evaluating intrusion detection systems

Network Intrusion Detection Systems (NIDSes) evaluation requires background traffic. However, real background traffic is hard to collect. We hence rely on synthetic traffic generated especially for this task. The quality of the generated traffic has to be evaluated according to some clearly defined criteria. In this paper, we show how to adapt the ...

Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification

Published in PeerJ Computer Science

The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and fal...

Design and Implementation of a Quantitative Network Health Monitoring and Recovery System.

Published in Wireless personal communications

A network health monitoring system focuses on the quantification of the network's health by taking into account various security flaws, leaks, and vulnerabilities. A plethora of propriety tools and patents are available for network health quantification. However, there is a paucity of available research and literature in this field. Thus, in this s...

Data set and machine learning models for the classification of network traffic originators

Published in Data in Brief

The widespread adoption of encryption in computer network traffic is increasing the difficulty of analyzing such traffic for security purposes. The data set presented in this data article is composed of network statistics computed on captures of TCP flows, originated by executing various network stress and web crawling tools, along with statistics ...

A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas p...

Published in Cluster Computing

Presently, Supervisory Control and Data Acquisition (SCADA) systems are broadly adopted in remote monitoring large-scale production systems and modern power grids. However, SCADA systems are continuously exposed to various heterogeneous cyberattacks, making the detection task using the conventional intrusion detection systems (IDSs) very challengin...