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A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …
between different communicating devices. The data should be communicated securely …
A detailed investigation and analysis of using machine learning techniques for intrusion detection
Intrusion detection is one of the important security problems in todays cyber world. A
significant number of techniques have been developed which are based on machine …
significant number of techniques have been developed which are based on machine …
A survey on machine learning techniques for cyber security in the last decade
Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …
Machine learning methods for cyber security intrusion detection: Datasets and comparative study
The increase in internet usage brings security problems with it. Malicious software can affect
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
the operation of the systems and disrupt data confidentiality due to the security gaps in the …
Anomaly-based intrusion detection system for IoT application
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 …
has a wide application, such as in transportation, military, healthcare, agriculture, and many …
[HTML][HTML] CNN-based network intrusion detection against denial-of-service attacks
As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a
variety of fields including industry, national defense, and healthcare. Traditional intrusion …
variety of fields including industry, national defense, and healthcare. Traditional intrusion …
HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection
W Wang, Y Sheng, J Wang, X Zeng, X Ye… - IEEE …, 2017 - ieeexplore.ieee.org
The development of an anomaly-based intrusion detection system (IDS) is a primary
research direction in the field of intrusion detection. An IDS learns normal and anomalous …
research direction in the field of intrusion detection. An IDS learns normal and anomalous …
The DDoS attacks detection through machine learning and statistical methods in SDN
The distributed denial-of-service (DDoS) attack is a security challenge for the software-
defined network (SDN). The different limitations of the existing DDoS detection methods …
defined network (SDN). The different limitations of the existing DDoS detection methods …
A comprehensive systematic literature review on intrusion detection systems
Effectively detecting intrusions in the computer networks still remains problematic. This is
because cyber attackers are changing packet contents to disguise the intrusion detection …
because cyber attackers are changing packet contents to disguise the intrusion detection …
Improving adaboost-based intrusion detection system (IDS) performance on CIC IDS 2017 dataset
A Yulianto, P Sukarno… - Journal of Physics …, 2019 - iopscience.iop.org
This paper considers the use of Synthetic Minority Oversampling Technique (SMOTE),
Principal Component Analysis (PCA), and Ensemble Feature Selection (EFS) to improve the …
Principal Component Analysis (PCA), and Ensemble Feature Selection (EFS) to improve the …