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Network Intrusion Detection Systems: A Systematic Literature Review of Hybrid Deep Learning Approaches
(2022-06)
Network Intrusion Detection Systems (NIDSs) have become standard security solutions that endeavours to discover unauthorized access to an organizational computer network by scrutinizing incoming and outgoing network traffic ...
SSH-Brute Force Attack Detection Model based on Deep Learning
(2021-01)
The rising number of malicious threats on computer networks and Internet services owing to a large number of attacks makes the network security be at incessant risk. One of the predominant network attacks that poses ...
Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher Education Institutions
(2016-11-11)
Educational data mining is the process of applying data mining tools and techniques to analyze data at educational
institutions. In this paper, educational data mining was used to predict enrollment of students in Science, ...
Improving Student Enrollment Prediction Using Ensemble Classifiers
(2018)
In the recent years, data mining has been utilized in education settings for extracting and manipulating data, and for establishing patterns in order to produce useful information for decision making. There is a growing ...
Evaluating Linear and Non-linear Dimensionality Reduction Approaches for Deep Learning-based Network Intrusion Detection Systems
(2023-08-08)
Dimensionality reduction is an essential ingredient of machine learning modelling that seeks to improve the performance of such models by extracting better quality features from data while removing irrelevant and redundant ...
Discriminative spatial-temporal feature learning for modeling network intrusion detection systems
(Journal of Computer Security, 2023-02)
Increasing interest and advancement of internet and communication technologies have made network security rise as a vibrant research domain. Network intrusion detection systems (NIDSs) have developed as indispensable defense ...