Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Z Ahmad, A Shahid Khan, C Wai Shiang… - Transactions on …, 2021 - Wiley Online Library
The rapid advances in the internet and communication fields have resulted in a huge
increase in the network size and the corresponding data. As a result, many novel attacks are …

Internet of things applications, security challenges, attacks, intrusion detection, and future visions: A systematic review

N Mishra, S Pandya - IEEE Access, 2021 - ieeexplore.ieee.org
Internet of Things (IoT) technology is prospering and entering every part of our lives, be it
education, home, vehicles, or healthcare. With the increase in the number of connected …

Explainable artificial intelligence in cybersecurity: A survey

N Capuano, G Fenza, V Loia, C Stanzione - Ieee Access, 2022 - ieeexplore.ieee.org
Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being's daily
life. Despite the AI benefits, its application suffers from the opacity of complex internal …

Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset

SM Kasongo, Y Sun - Journal of Big Data, 2020 - Springer
Computer networks intrusion detection systems (IDSs) and intrusion prevention systems
(IPSs) are critical aspects that contribute to the success of an organization. Over the past …

Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection

H Ding, L Chen, L Dong, Z Fu, X Cui - Future Generation Computer Systems, 2022 - Elsevier
With the continuous emergence of various network attacks, it is becoming more and more
important to ensure the security of the network. Intrusion detection, as one of the important …

A deep learning model for network intrusion detection with imbalanced data

Y Fu, Y Du, Z Cao, Q Li, W Xiang - Electronics, 2022 - mdpi.com
With an increase in the number and types of network attacks, traditional firewalls and data
encryption methods can no longer meet the needs of current network security. As a result …

A fast network intrusion detection system using adaptive synthetic oversampling and LightGBM

J Liu, Y Gao, F Hu - Computers & Security, 2021 - Elsevier
Network intrusion detection systems play an important role in protecting the network from
attacks. However, Existing network intrusion data is imbalanced, which makes it difficult to …

Deep Q-learning based reinforcement learning approach for network intrusion detection

H Alavizadeh, H Alavizadeh, J Jang-Jaccard - Computers, 2022 - mdpi.com
The rise of the new generation of cyber threats demands more sophisticated and intelligent
cyber defense solutions equipped with autonomous agents capable of learning to make …

A survey on data-driven network intrusion detection

D Chou, M Jiang - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …

A feature selection based on the farmland fertility algorithm for improved intrusion detection systems

TS Naseri, FS Gharehchopogh - Journal of Network and Systems …, 2022 - Springer
The development and expansion of the Internet and cyberspace have increased computer
systems attacks; therefore, Intrusion Detection Systems (IDSs) are needed more than ever …