Towards a standard feature set for network intrusion detection system datasets

M Sarhan, S Layeghy, M Portmann - Mobile networks and applications, 2022 - Springer
Abstract Network Intrusion Detection Systems (NIDSs) are important tools for the protection
of computer networks against increasingly frequent and sophisticated cyber attacks …

Machine learning for misuse-based network intrusion detection: overview, unified evaluation and feature choice comparison framework

L Le Jeune, T Goedeme, N Mentens - Ieee Access, 2021 - ieeexplore.ieee.org
Network Intrusion detection systems are essential for the protection of advanced
communication networks. Originally, these systems were hard-coded to identify specific …

[HTML][HTML] Anomaly-based intrusion detection system for IoT application

M Bhavsar, K Roy, J Kelly, O Olusola - Discover Internet of Things, 2023 - Springer
Abstract Internet-of-Things (IoT) connects various physical objects through the Internet and it
has a wide application, such as in transportation, military, healthcare, agriculture, and many …

An unsupervised deep learning model for early network traffic anomaly detection

RH Hwang, MC Peng, CW Huang, PC Lin… - IEEE …, 2020 - ieeexplore.ieee.org
Various attacks have emerged as the major threats to the success of a connected world like
the Internet of Things (IoT), in which billions of devices interact with each other to facilitate …

MFFusion: A multi-level features fusion model for malicious traffic detection based on deep learning

K Lin, X Xu, F Xiao - Computer Networks, 2022 - Elsevier
Network malicious traffic detection is one of the essential tasks of computer networks, which
has become an obstacle to network development as networks are expanding in size and …

A multi-level intrusion detection method for abnormal network behaviors

SY Ji, BK Jeong, S Choi, DH Jeong - Journal of Network and Computer …, 2016 - Elsevier
Abnormal network traffic analysis has become an increasingly important research topic to
protect computing infrastructures from intruders. Yet, it is challenging to accurately discover …

Analysis of multi-types of flow features based on hybrid neural network for improving network anomaly detection

C Ma, X Du, L Cao - IEEE Access, 2019 - ieeexplore.ieee.org
Security issues of large-scale local area network are becoming more prominent and the
anomaly detection for the network traffic is the key means to solve this problem. On the other …

Feature analysis for machine learning-based IoT intrusion detection

M Sarhan, S Layeghy, M Portmann - arXiv preprint arXiv:2108.12732, 2021 - arxiv.org
Internet of Things (IoT) networks have become an increasingly attractive target of
cyberattacks. Powerful Machine Learning (ML) models have recently been adopted to …

A survey of public IoT datasets for network security research

F De Keersmaeker, Y Cao… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
Publicly available datasets are an indispensable tool for researchers, as they allow testing
new algorithms on a wide range of different scenarios and making scientific experiments …

LuNET: a deep neural network for network intrusion detection

P Wu, H Guo - 2019 IEEE symposium series on computational …, 2019 - ieeexplore.ieee.org
Network attack is a significant security issue for modern society. From small mobile devices
to large cloud platforms, almost all computing products, used in our daily life, are networked …