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HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System
2021
Processes
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An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. ...
To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic ...
The proposed HCRNNIDS outperforms the state-of-the-art techniques in terms of accuracy and FAR. This is due to the deep learning approach's execution. ...
doi:10.3390/pr9050834
fatcat:kjvtl35b2zffhafouvzlpqn3oe
A Deep Learning Approach to Network Intrusion Detection
2018
IEEE Transactions on Emerging Topics in Computational Intelligence
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Furthermore, we also propose our novel deep learning classification model constructed using stacked NDAEs. ...
This paper presents a novel deep learning technique for intrusion detection, which addresses these concerns. ...
ACKNOWLEDGEMENTS The authors would like to thank the Royal Academy of Engineering for their support provided through the Newton Research Collaboration Programme. ...
doi:10.1109/tetci.2017.2772792
dblp:journals/tetci/ShoneTPS18
fatcat:jze522fwdbbb3mpaxeiudunvdm
Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey
[article]
2017
arXiv
pre-print
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Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems. ...
Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. ...
The second objective of the paper is to present a survey and the classification of Intrusion Detection Systems, taxonomy of Machine Learning IDS and a survey on shallow and deep networks IDS. ...
arXiv:1701.02145v1
fatcat:dv7kxw6o25epdcpkhfawygqdcq
TSDL: A TwoStage Deep Learning Model for Efficient Network Intrusion Detection
2019
IEEE Access
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This is then used in the final decision stage as an additional feature, for detecting the normal state and other classes of attacks. ...
The network intrusion detection system is an important tool for protecting computer networks against threats and malicious attacks. ...
State-of-the-art deep learning approaches and methods that have been used for unsupervised feature learning for network intrusion detection, include deep belief networks (DBNs), deep neural networks (DNNs ...
doi:10.1109/access.2019.2899721
fatcat:llvh7bevxnem7elkjkxw5tlr5i
Ensemble Models for Intrusion Detection System Classification
2022
International Journal of Smart Sensor and Adhoc Network.
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In this scope, we evaluated the usage of state-of-the-art ensemble learning models in improving the performance and efficiency of IDS/IPS. ...
Using data analytics in the problem of Intrusion Detection and Prevention Systems (IDS/IPS) is a continuous research problem due to the evolutionary nature of the problem and the changes in major influencing ...
A Deep Neural Network (DNN) model is built for an intrusion detection system. The DDN is applied and used for the NIDS model in the context of the SDN. ...
doi:10.47893/ijssan.2022.1209
fatcat:m5hflzegz5es3kbscv4lpu5khq
Towards Effective Network Intrusion Detection: From Concept to Creation on Azure Cloud
2021
IEEE Access
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Network Intrusion Detection is one of the most researched topics in the field of computer security. ...
To extend the functionalities further, we have automated the proposed model that can be a reliable candidate for real-time network intrusion detection. ...
We have composed this article in the following manner. Section II reviews some state of the art methods in the field of network intrusion detection. ...
doi:10.1109/access.2021.3054688
fatcat:bp3g5iqds5fuxokzwup2o5cvmm
A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network
2019
Symmetry
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Evaluations of several baseline models in the ISCX-UNB dataset show that our hybrid IDS can identify network misuses accurately in 97.29% of cases and outperforms state-of-the-art approaches during 10- ...
Existing ID systems that are typically used in traditional network intrusion detection system often fail and cannot detect many known and new security threats, largely because those approaches are based ...
Table 1 . 1 Overview of the state-of-the-art approaches. ...
doi:10.3390/sym11040583
fatcat:ehljqrjsmrajdkl2u2sm2lzbqa
Feature extraction based on word embedding models for intrusion detection in network traffic
2020
Journal of Surveillance, Security and Safety
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machine learning algorithms that are applied to intrusion detection, and it is competitive with known feature extraction baselines in the state-of-the-art. ...
Conclusion: This study shows that word embedding models can be used to carry out intrusion detection tasks accurately. ...
Analytical workflow for machine learning-based intrusion detection in network traffic. ...
doi:10.20517/jsss.2020.15
fatcat:uj7d3glkijfdri3pxvsnibn7e4
Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine
2020
Computers Materials & Continua
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There in this article in order to achieve this objective, we propose an intrusion detection system focused on a Deep extreme learning machine (DELM) which first establishes the assessment of safety features ...
Artificial intelligence, particularly machine learning methods can be used to develop an intelligent intrusion detection framework. ...
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study. ...
doi:10.32604/cmc.2020.013121
fatcat:ixnv7aehmjcwpkkl4rzjam6jcu
Intrusion Detection System Using Recurrent Neural Networks and Attention Mechanism
2019
International Journal of Emerging Trends in Engineering Research
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To optimize the model to focus on the most relevant features and to make the model train considerably faster, we are proposing an attention based recurrent neural network for intrusion detection in large ...
These measures are compared among state-of-the-art classification algorithms to determine how our proposed model performs on par with the current approaches. ...
With the wide adoption of machine learning algorithms have paved way for new algorithms intrusion detection that can learn from data using various statistical analysis techniques. ...
doi:10.30534/ijeter/2019/12782019
fatcat:dfjyizkpebhxrkol4shxbs6s64
A Review on Intrusion Detection System Based on Various Learning Techniques
2021
Indian Journal of Artificial Intelligence and Neural Networking
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And to know the current state of machine learning approaches to address the issue of intrusion detection. ...
This research assesses the creation of a deep neural network (DNN), a form of deep learning model as well as ELM to detect unpredictable and unpredictable cyber-attacks ...
For only Fifty iterations, that is state of the art associated with an existing technique for intrusion detection, the proposed system achieved detection accuracy of nearly 97.5% [22] J. ...
doi:10.35940/ijainn.b1013.041221
fatcat:6ngpwsdezjedzntsazrsi3gloi
Watching Smartly from the Bottom: Intrusion Detection revamped through Programmable Networks and Artificial Intelligence
[article]
2021
arXiv
pre-print
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In this paper, we discuss how Programmable Data Planes might complement different stages of an Intrusion Detection System based on Machine Learning. ...
The advent of Programmable Data Planes represents an outstanding evolution and complete revolution of the Software- Defined Networking paradigm. ...
However, more complex and state-of-the-art solutions such as Deep Neural Networks can also leverage PDPs. ...
arXiv:2106.00239v1
fatcat:rcljik2bjfdsbpgdh5jydto4ai
G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System
[article]
2020
arXiv
pre-print
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We model a network security dataset for an emerging CPS using NSL KDD-99 dataset and evaluate our proposed model's performance using different metrics. ...
In this paper, we propose a generative adversarial network (GAN) based intrusion detection system (G-IDS), where GAN generates synthetic samples, and IDS gets trained on them along with the original ones ...
Different machine learning and deep learningbased algorithms are showing high detection accuracy [18] - [21] . ...
arXiv:2006.00676v1
fatcat:5vrd3jrdxzej7gmyv4trjtmqay
MIND: A Multi-Source Data Fusion Scheme for Intrusion Detection in Networks
2021
Sensors
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Moreover, the results indicate that the proposed model is significantly effective in intrusion detection compared to other state-of-the-art methods. ...
Moreover, most of the earlier intrusion detection models suffer from overfitting problems and lack optimal detection of intrusions. ...
A light-weight algorithm is proposed in [15] for a real-time intrusion detection model based on a deep belief network (DBN) and support vector machine (SVM). ...
doi:10.3390/s21144941
fatcat:cbfifkgbefgl7cakhdjxidybxu
An Ensemble of a Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments
2021
Sustainability
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This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. ...
The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/su131810057
fatcat:qiohqpars5ajjnbqgn7zhckov4
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