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Survey anomaly detection in network using big data analytics
2017
2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS)
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data mining and machine learning, deep learning, and Big Data analytics in network intrusion detection. ...
Current challenges of these methods in intrusion detection are also introduced. ...
Disclaimer Reference herein to any specific commercial company, product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement ...
doi:10.1109/icecds.2017.8390083
fatcat:6gij7behqjhezefwcxoflowtqe
An analytical survey on the role of machine learning algorithms in case of intrusion detection
2020
ACCENTS Transactions on Information Security
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In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. ...
This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). ...
Conflicts of interest The authors have no conflicts of interest to declare.
References [1] McLaughlin S, Konstantinou C, Wang X, Davi L, ...
doi:10.19101/tis.2020.517002
fatcat:p75fjk42gjcudkdq752n2wn5pu
Network intrusion detection system: machine learning approach
2022
Indonesian Journal of Electrical Engineering and Computer Science
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The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. ...
This work proposes a model for intrusion detection and classification using machine learning techniques. ...
[13] utilize deep learning approach through employing deep neural network (DNN) to dynamically detect cyberattacks. Sharafaldin et al. ...
doi:10.11591/ijeecs.v25.i2.pp1151-1158
fatcat:6e5wac23wnemvm5ixjz26crtje
Intrusion Detection System on Big data using Deep Learning Techniques
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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The number of attacks has been increased in computer networks. A powerful Intrusion Detection System (IDS) is required to ensure the security of a network. ...
In this paper, the detailed review has been done on intrusion detection on various fields using deep learning and gives an idea of applications of deep learning. ...
Limitation of this paper is that authors can extend the work from a selection for improvement to detect best deep learning model.
III. ...
doi:10.35940/ijitee.d2011.029420
fatcat:t4woonejwzd3njpqix5o42uf5q
A Survey of Intrusion Detection Using Deep Learning in Internet of Things
2022
Iraqi Journal for Computer Science and Mathematics
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The use of deep learning in various models is a powerful tool in detecting IoT attacks, identifying new types of intrusion to access a better secure network. ...
Need to developing an intrusion detection system to detect and classify attacks in appropriate time and automated manner increases especially due to the use of IoT and the nature of its data that causes ...
CONFLICTS OF INTEREST The authors declare no conflict of interest. ...
doi:10.52866/ijcsm.2022.01.01.009
fatcat:ttdhhdmqr5gzvo32j66ofiouua
Machine Learning and Deep Learning Approaches for CyberSecuriy: A Review
2022
IEEE Access
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As a result, an effective intrusion detection system was required to protect data, and the discovery of artificial intelligence's sub-fields, machine learning, and deep learning, was one of the most successful ...
It discusses recent machine learning and deep learning work with various network implementations, applications, algorithms, learning approaches, and datasets to develop an operational intrusion detection ...
First, a deep learning-based intrusion detection system for an IoT network was developed in [39] . ...
doi:10.1109/access.2022.3151248
fatcat:3h6qhrddkbfipodxapeevn344q
Cybersecurity data science: an overview from machine learning perspective
2020
Journal of Big Data
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Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. ...
Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. ...
The reviews are detailed and helpful to improve and finalize the manuscript. The authors are highly grateful to them. ...
doi:10.1186/s40537-020-00318-5
fatcat:i5qjz55m7fcudoxhstzlj3akzu
Deep Learning-Based Intrusion Detection Systems: A Systematic Review
2021
IEEE Access
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It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. ...
This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. ...
To be more specific, this work classifies the deep intrusion detection approaches based on the type of deep learning network applied in their various intrusion detection steps. ...
doi:10.1109/access.2021.3097247
fatcat:un54rxgjyvfx3pxberzpafioxm
Intrusion Detection using Recurrent Neural Networks
2020
International Journal for Research in Applied Science and Engineering Technology
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When it comes to large data sets, deep learning methodology plays a more important role in data science. In this paper we investigate attacks of intrusion detection. ...
Detection of intrusion Performs an important function in the protection of Privacy of knowledge and main technologies is to reliably Identification different network intrusion attacks. ...
CONCLUSION In this paper, the Deep Learning algorithm is used to keep updating the Recurrent Neural Network-based Intrusion Detection System Classifier. ...
doi:10.22214/ijraset.2020.6335
fatcat:wwkxo63vezhdhirhbkxlrtraum
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System
[article]
2021
arXiv
pre-print
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This is due to the excessive growth of the network and its exposure to a plethora of people. ...
promoted deep learning to take over the task with less time and great results. ...
We plan to hybridize this deep learning model to handle class imbalance problems in the near future with advanced GAN to make most effective use of our proposed network intrusion detection system. ...
arXiv:2112.03704v1
fatcat:qcseusv5mfajlpfac6g2n24vj4
Learning to Detect: A Data-driven Approach for Network Intrusion Detection
[article]
2021
arXiv
pre-print
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with a deep neural network as a base model. ...
Unlike previous shallow learning and deep learning models that use the single learning model approach for intrusion detection, we adopt a hierarchy strategy, in which the intrusion and normal behavior ...
Learning to Detect Network Intrusion In this paper, we adopt various learning models for binary and 4-class intrusion detection. ...
arXiv:2108.08394v1
fatcat:ljclxvquzbfgvpw4zkpuwtzcnm
Intelligent and Effective Intrusion Detection System using Machine Learning Algorithm
2020
International Journal of Engineering and Advanced Technology
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There is need of efficient Intrusion Detection system .The focus of IDS research is the application of machine Learning and Deep Learning techniques. ...
Intrusion Detection System observes the network traffic and identifies the attack and also inform the admin to corrective action. ...
The main focus of NIDS research has been the application of machine learning and Deep learning techniques which has given a great encouragement to large number of network attack In this paper, we have ...
doi:10.35940/ijeat.f1231.089620
fatcat:ulwcclmzqbblvir3rqlysjhhge
Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
2018
Applied Sciences
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We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. ...
The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app8122663
fatcat:22w3om3rwnbgjd7nuo3kdbjn3i
A Compendium on Network and Host based Intrusion Detection Systems
[article]
2019
arXiv
pre-print
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Deep learning is a subset and a natural extension of classical Machine learning and an evolved model of neural networks. ...
This paper contemplates and discusses all the methodologies related to the leading edge Deep learning and Neural network models purposing to the arena of Intrusion Detection Systems. ...
Prabharan Poornachandran of Centre for Cyber Security Systems and Networks, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India. ...
arXiv:1904.03491v1
fatcat:qiuup3aixnfelk732q36ozdi64
An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications
2022
Computers Materials & Continua
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Furthermore, this paper presents an SDN-based intrusion detection system using a deep learning (DL) model with the KDD (Knowledge Discovery in Databases) dataset. ...
Then, a deep learning method is projected for building an efficient SDN-based intrusion detection system. ...
This paper proposes a deep learning (DL) model for building an efficient software-defined network (SDN)-based intrusion detection system. ...
doi:10.32604/cmc.2022.025262
fatcat:hfykhscvg5bfxfmomckss7vepi
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