Supervised feature selection techniques in network intrusion detection: A critical review
Abstract Machine Learning (ML) techniques are becoming an invaluable support for network
intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats …
intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats …
Data mining techniques in intrusion detection systems: A systematic literature review
The continued ability to detect malicious network intrusions has become an exercise in
scalability, in which data mining techniques are playing an increasingly important role. We …
scalability, in which data mining techniques are playing an increasingly important role. We …
Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks
Open wireless sensor networks (WSNs) in Internet of things (IoT) has led to many zero-day
security attacks. Since intrusion detection is a key security solution, this paper presents a …
security attacks. Since intrusion detection is a key security solution, this paper presents a …
An edge based hybrid intrusion detection framework for mobile edge computing
Abstract The Mobile Edge Computing (MEC) model attracts more users to its services due to
its characteristics and rapid delivery approach. This network architecture capability enables …
its characteristics and rapid delivery approach. This network architecture capability enables …
[PDF][PDF] A lightweight Anomaly detection model using SVM for WSNs in IoT through a hybrid feature selection algorithm based on GA and GWO
As a result of an incredibly fast growth of the number and diversity of smart devices
connectable to the internet, commonly through open wireless sensor networks (WSNs) in …
connectable to the internet, commonly through open wireless sensor networks (WSNs) in …
[PDF][PDF] Genetic algorithm to solve the problem of small disjunct in the decision tree based intrusion detection system
Intrusion detection system is the most important part of the network security system because
the volume of unauthorized access to the network resources and services increase day by …
the volume of unauthorized access to the network resources and services increase day by …
Intrusion-detection system based on fast learning network in cloud computing
MH Ali, MF Zolkipli - Advanced Science Letters, 2018 - ingentaconnect.com
Detection of attacks in the computers and networks keeps being the pertinent and
challenging area of researchers. Intrusion-Detection System is an essential technology of …
challenging area of researchers. Intrusion-Detection System is an essential technology of …
A study on intrusion detection using centroid-based classification
The ultimate goal of intrusion detection system (IDS) development is to accomplish the best
possible accuracy for detection attacks. Various hybrid machine learning techniques were …
possible accuracy for detection attacks. Various hybrid machine learning techniques were …
Decision tree and genetic algorithm based intrusion detection system
Today's computer network security systems like IDS, firewall, access control, etc., are not yet
100% trusted, Still they are suffering from the high classification error. Therefore, there is …
100% trusted, Still they are suffering from the high classification error. Therefore, there is …
K-strings algorithm, a new approach based on Kmeans
VH Le, SR Kim - Proceedings of the 2015 Conference on research in …, 2015 - dl.acm.org
K-means is a popular clustering algorithm which is widely used in anomaly-based intrusion
detection. It tries to classify a given data set into k (a predefined number) categories …
detection. It tries to classify a given data set into k (a predefined number) categories …