Journal ArticleDOI
GAN-based imbalanced data intrusion detection system
Joo-Hwa Lee,KeeHyun Park +1 more
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TLDR
The purpose of this study is to solve data imbalance by using the Generative Adversarial Networks (GAN) model, which is an unsupervised learning method of deep learning which generates new virtual data similar to the existing data.Abstract:
According to the development of deep learning technologies, a wide variety of research is being performed to detect intrusion data by using vast amounts of data. Although deep learning performs more accurately than machine learning algorithms when learning large amounts of data, the performance declines significantly in the case of learning from imbalanced data. And, while there are many studies on imbalanced data, most have weaknesses that can result in data loss or overfitting. The purpose of this study is to solve data imbalance by using the Generative Adversarial Networks (GAN) model, which is an unsupervised learning method of deep learning which generates new virtual data similar to the existing data. It also proposed a model that would be classified as Random Forest to identify detection performance after addressing data imbalances based on a GAN. The results of the experiment showed that the performance of the model proposed in this paper was better than the model classified without addressing the imbalance of data. In addition, it was found that the performance of the model proposed in this paper was excellent when compared with other models that were previously used widely for the data imbalance problem.read more
Citations
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Journal ArticleDOI
Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset
Ziadoon Kamil Maseer,Robiah Yusof,Nazrulazhar Bahaman,Salama A. Mostafa,Cik Feresa Mohd Foozy +4 more
TL;DR: 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML–AIDS of networks and computers are applied and the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models are evaluated.
Journal ArticleDOI
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review
Nivedita Mishra,Sharnil Pandya +1 more
TL;DR: In this article, a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks.
Journal ArticleDOI
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles
TL;DR: In this article, a multi-tiered hybrid IDS was proposed to detect both known and unknown attacks on vehicular networks, which can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the intra-vehicle network data.
Journal ArticleDOI
I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems
TL;DR: Improved Siam-IDS (I-SiamIDS) as discussed by the authors uses an ensemble of binary eXtreme Gradient Boosting (b-XGBoost), Siamese Neural Network (Siamese-NN) and deep neural network (DNN) for handling class imbalance problem.
Journal ArticleDOI
A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks
Imtiaz Ullah,Qusay H. Mahmoud +1 more
TL;DR: A two-level anomalous activity detection model for intrusion detection system in IoT networks will provide a robust framework for the development of malicious activity detection system for IoT networks and would be of interest to researchers in academia and industry.
References
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Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Journal ArticleDOI
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Journal ArticleDOI
SMOTE: Synthetic Minority Over-sampling Technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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.
Journal ArticleDOI
The relationship between recall and precision
TL;DR: Examination of the mathematical relationship between Precision and Recall shows that a quadratic Recall curve can resemble empirical Recall–Precision behavior if transformed into a tangent parabola.
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