Adversarial machine learning for network intrusion detection systems: A comprehensive survey

K He, DD Kim, MR Asghar - IEEE Communications Surveys & …, 2023 - ieeexplore.ieee.org
Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …

Machine learning for anomaly detection: A systematic review

AB Nassif, MA Talib, Q Nasir, FM Dakalbab - Ieee Access, 2021 - ieeexplore.ieee.org
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

A survey of network-based intrusion detection data sets

M Ring, S Wunderlich, D Scheuring, D Landes… - Computers & …, 2019 - Elsevier
Labeled data sets are necessary to train and evaluate anomaly-based network intrusion
detection systems. This work provides a focused literature survey of data sets for network …

Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset

G Karatas, O Demir, OK Sahingoz - IEEE access, 2020 - ieeexplore.ieee.org
In recent years, due to the extensive use of the Internet, the number of networked computers
has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude …

Adversarial machine learning attacks against intrusion detection systems: A survey on strategies and defense

A Alotaibi, MA Rassam - Future Internet, 2023 - mdpi.com
Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …

Troubleshooting an intrusion detection dataset: the CICIDS2017 case study

G Engelen, V Rimmer, W Joosen - 2021 IEEE Security and …, 2021 - ieeexplore.ieee.org
Numerous studies have demonstrated the effectiveness of machine learning techniques in
application to network intrusion detection. And yet, the adoption of machine learning for …

Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost

A Shahraki, M Abbasi, Ø Haugen - Engineering Applications of Artificial …, 2020 - Elsevier
Computer networks have been experienced ever-increasing growth since they play a critical
role in different aspects of human life. Regarding the vulnerabilities of computer networks …

Modeling realistic adversarial attacks against network intrusion detection systems

G Apruzzese, M Andreolini, L Ferretti… - … Threats: Research and …, 2022 - dl.acm.org
The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …

An end-to-end framework for machine learning-based network intrusion detection system

GDC Bertoli, LAP Júnior, O Saotome… - IEEE …, 2021 - ieeexplore.ieee.org
The increase of connected devices and the constantly evolving methods and techniques by
attackers pose a challenge for network intrusion detection systems from conception to …

A taxonomy of network threats and the effect of current datasets on intrusion detection systems

H Hindy, D Brosset, E Bayne, AK Seeam… - IEEE …, 2020 - ieeexplore.ieee.org
As the world moves towards being increasingly dependent on computers and automation,
building secure applications, systems and networks are some of the main challenges faced …