Intrusion detection and big heterogeneous data: a survey
Intrusion Detection has been heavily studied in both industry and academia, but
cybersecurity analysts still desire much more alert accuracy and overall threat analysis in …
cybersecurity analysts still desire much more alert accuracy and overall threat analysis in …
Intrusion detection by machine learning: A review
CF Tsai, YF Hsu, CY Lin, WY Lin - expert systems with applications, 2009 - Elsevier
The popularity of using Internet contains some risks of network attacks. Intrusion detection is
one major research problem in network security, whose aim is to identify unusual access or …
one major research problem in network security, whose aim is to identify unusual access or …
Building an intrusion detection system using a filter-based feature selection algorithm
Redundant and irrelevant features in data have caused a long-term problem in network
traffic classification. These features not only slow down the process of classification but also …
traffic classification. These features not only slow down the process of classification but also …
Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system
Intrusion detection has become essential to network security because of the increasing
connectivity between computers. Several intrusion detection systems have been developed …
connectivity between computers. Several intrusion detection systems have been developed …
An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine
Realization of the importance for advanced tool and techniques to secure the network
infrastructure from the security risks has led to the development of many machine learning …
infrastructure from the security risks has led to the development of many machine learning …
A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems
This paper presents a new feature-selection approach based on the cuttlefish optimization
algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a …
algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a …
The use of computational intelligence in intrusion detection systems: A review
SX Wu, W Banzhaf - Applied soft computing, 2010 - Elsevier
Intrusion detection based upon computational intelligence is currently attracting
considerable interest from the research community. Characteristics of computational …
considerable interest from the research community. Characteristics of computational …
An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization
Many organizations recognize the necessities of utilizing sophisticated tools and systems to
protect their computer networks and reduce the risk of compromising their information …
protect their computer networks and reduce the risk of compromising their information …
An efficient intrusion detection system based on support vector machines and gradually feature removal method
Y Li, J Xia, S Zhang, J Yan, X Ai, K Dai - Expert systems with applications, 2012 - Elsevier
The efficiency of the intrusion detection is mainly depended on the dimension of data
features. By using the gradually feature removal method, 19 critical features are chosen to …
features. By using the gradually feature removal method, 19 critical features are chosen to …
A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine
L Lv, W Wang, Z Zhang, X Liu - Knowledge-based systems, 2020 - Elsevier
Intrusion detection is a challenging technology in the area of cyberspace security for
protecting a system from malicious attacks. A novel accurate and effective misuse intrusion …
protecting a system from malicious attacks. A novel accurate and effective misuse intrusion …