Journal ArticleDOI
A deep learning method with wrapper based feature extraction for wireless intrusion detection system
Sydney Mambwe Kasongo,Yanxia Sun +1 more
TLDR
The results suggested that the proposed Feed-Forward Deep Neural Network (FFDNN) wireless IDS system using a Wrapper Based Feature Extraction Unit (WFEU) has greater detection accuracy than other approaches.About:
This article is published in Computers & Security.The article was published on 2020-05-01. It has received 161 citations till now. The article focuses on the topics: Multiclass classification & Intrusion detection system.read more
Citations
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction
Rizgar R. Zebari,Adnan Mohsin Abdulazeez,Diyar Qader Zeebaree,Dilovan Asaad Zebari,Jwan Najeeb Saeed +4 more
TL;DR: This paper offers a comprehensive approach to FS and FE in the scope of dimensionality reduction, which significantly reduced computational time, and selecting the most accurate classifiers.
Journal ArticleDOI
Machine learning methods for cyber security intrusion detection: Datasets and comparative study
TL;DR: In this paper, the authors reviewed the literature studies using CSE-CIC IDS-2018, UNSW-NB15, ISCX-2012, NSL-KDD and CIDDS-001 data sets, which are widely used to develop IDS systems.
Journal ArticleDOI
Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0
TL;DR: A deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural Networks, and recurrent neural networks are proposed.
Journal ArticleDOI
Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review
Mujaheed Abdullahi,Yahia Baashar,Hitham Alhussian,Ayed Alwadain,NorShakirah Bt A Aziz,Luiz Fernando Capretz,Said Jadid Abdulkadir +6 more
TL;DR: A systematic literature review that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment and provides an insight into the AI roadmap to detect threats based on attack categories is presented.
References
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Wrappers for feature subset selection
Ron Kohavi,George H. John +1 more
TL;DR: The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain and compares the wrapper approach to induction without feature subset selection and to Relief, a filter approach tofeature subset selection.
Journal ArticleDOI
Extremely randomized trees
TL;DR: A new tree-based ensemble method for supervised classification and regression problems that consists of randomizing strongly both attribute and cut-point choice while splitting a tree node and builds totally randomized trees whose structures are independent of the output values of the learning sample.
Proceedings ArticleDOI
A detailed analysis of the KDD CUP 99 data set
TL;DR: A new data set is proposed, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.
Journal ArticleDOI
Artificial neural networks: fundamentals, computing, design, and application
I.A Basheer,M Hajmeer +1 more
TL;DR: A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation ANNs theory and design, and a generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation is described.
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UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
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