Journal ArticleDOI
Multi-dimensional feature fusion and stacking ensemble mechanism for network intrusion detection
Reads0
Chats0
TLDR
This paper proposes a multi-dimensional feature fusion and stacking ensemble mechanism (MFFSEM), which can detect abnormal behaviors effectively and significantly outperforms the basic and meta classifiers adopted in the method.About:
This article is published in Future Generation Computer Systems.The article was published on 2021-09-01. It has received 84 citations till now. The article focuses on the topics: Ensemble learning & Feature (computer vision).read more
Citations
More filters
Journal ArticleDOI
A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning
TL;DR: Wang et al. as discussed by the authors proposed an intrusion detection model that combines machine learning with deep learning, and distributed computing of these algorithms is implemented on the Spark platform to quickly classify normal events and attack events.
Journal ArticleDOI
Analysis of Autoencoders for Network Intrusion Detection.
TL;DR: In this article, the authors rigorously study autoencoders using the benchmark datasets, NSL-KDD, IoTID20, and N-BaIoT, using a simple autoencoder model.
Journal ArticleDOI
Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
TL;DR: In this article, a UAV-and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks is proposed, where the solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways.
Journal ArticleDOI
Network Intrusion Detection Model Based on CNN and GRU
TL;DR: In this model, a hybrid sampling algorithm combining Adaptive Synthetic Sampling (ADASYN) and Repeated Edited nearest neighbors (RENN) is used for sample processing to solve the problem of positive and negative sample imbalance in the original dataset, and the experimental results show that the classification accuracy reaches 86.25%, 99.65%, and can solve the problems of low classification accuracy and class imbalance well.
Journal ArticleDOI
Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning
TL;DR: The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data and helping educators to make informed decisions by proactively notifying the students.
References
More filters
Journal ArticleDOI
Anomaly detection: A survey
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Proceedings ArticleDOI
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
TL;DR: A reliable dataset is produced that contains benign and seven common attack network flows, which meets real world criteria and is publicly avaliable and evaluates the performance of a comprehensive set of network traffic features and machine learning algorithms to indicate the best set of features for detecting the certain attack categories.
Proceedings ArticleDOI
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
Nour Moustafa,Jill Slay +1 more
TL;DR: Countering the unavailability of network benchmark data set challenges, this paper examines a UNSW-NB15 data set creation which has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic.
Journal ArticleDOI
A survey of network anomaly detection techniques
TL;DR: This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering and evaluates effectiveness of different categories of techniques.
Journal ArticleDOI
The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set
Nour Moustafa,Jill Slay +1 more
TL;DR: The experimental results show that UNSW-NB15 is more complex than KDD99 and is considered as a new benchmark data set for evaluating NIDSs.