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 …
Internet attacks and intrusion detection system: A review of the literature
Purpose The paper addresses various cyber threats and their effects on the internet. A
review of the literature on intrusion detection systems (IDSs) as a means of mitigating …
review of the literature on intrusion detection systems (IDSs) as a means of mitigating …
Adaboost-based algorithm for network intrusion detection
Network intrusion detection aims at distinguishing the attacks on the Internet from normal
use of the Internet. It is an indispensable part of the information security system. Due to the …
use of the Internet. It is an indispensable part of the information security system. Due to the …
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Learning with imbalanced data is one of the recent challenges in machine learning. Various
solutions have been proposed in order to find a treatment for this problem, such as …
solutions have been proposed in order to find a treatment for this problem, such as …
Applying long short-term memory recurrent neural networks to intrusion detection
RC Staudemeyer - South African Computer Journal, 2015 - journals.co.za
We claim that modelling network traffic as a time series with a supervised learning approach,
using known genuine and malicious behaviour, improves intrusion detection. To …
using known genuine and malicious behaviour, improves intrusion detection. To …
Decision tree based intrusion detection system for NSL-KDD dataset
Abstract In this paper, Decision Tree (DT) based IDS is proposed for NSL-KDD dataset. The
proposed work uses Correlation Feature Selection (CFS) subset evaluation method for …
proposed work uses Correlation Feature Selection (CFS) subset evaluation method for …
Evolving diverse ensembles using genetic programming for classification with unbalanced data
In classification, machine learning algorithms can suffer a performance bias when data sets
are unbalanced. Data sets are unbalanced when at least one class is represented by only a …
are unbalanced. Data sets are unbalanced when at least one class is represented by only a …
A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers
An intrusion detection system's main goal is to classify activities of a system into two major
categories: normal and suspicious (intrusive) activities. Intrusion detection systems usually …
categories: normal and suspicious (intrusive) activities. Intrusion detection systems usually …
Online adaboost-based parameterized methods for dynamic distributed network intrusion detection
W Hu, J Gao, Y Wang, O Wu… - IEEE Transactions on …, 2013 - ieeexplore.ieee.org
Current network intrusion detection systems lack adaptability to the frequently changing
network environments. Furthermore, intrusion detection in the new distributed architectures …
network environments. Furthermore, intrusion detection in the new distributed architectures …
Exploring feature normalization and temporal information for machine learning based insider threat detection
Insider threat is one of the most damaging cyber security attacks to companies and
organizations. In this paper, we explore different techniques to leverage spatial and …
organizations. In this paper, we explore different techniques to leverage spatial and …