Adversarial machine learning for network intrusion detection systems: A comprehensive survey
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 …
network attacks that compromise the security of the data, systems, and networks. In recent …
Machine learning for anomaly detection: A systematic review
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 …
components from data. Many techniques have been used to detect anomalies. One of the …
A survey of network-based intrusion detection data sets
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 …
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
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 …
has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude …
Troubleshooting an intrusion detection dataset: the CICIDS2017 case study
Numerous studies have demonstrated the effectiveness of machine learning techniques in
application to network intrusion detection. And yet, the adoption of machine learning for …
application to network intrusion detection. And yet, the adoption of machine learning for …
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) …
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …
Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost
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 …
role in different aspects of human life. Regarding the vulnerabilities of computer networks …
Modeling realistic adversarial attacks against network intrusion detection systems
The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …
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
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 …
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
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 …
building secure applications, systems and networks are some of the main challenges faced …