Spark Configurations to Optimize Decision Tree Classification on UNSW-NB15

Bagui, Sikha; Walauskis, Mary; DeRush, Robert; Praviset, Huyen; Boucugnani, Shaunda

doi:10.3390/bdcc6020038

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…Fortynine network traffic features were extracted, and the Argus and Bro-IDS programs were employed. There are 2,540,044 streams in the dataset, including 2,218,761 benign and 321,283 aggressive streams [17,28]. We split it into 20% for tests and 80% for training, with a stratification option to keep these percentages static for all classes.…”

Section: Unsw-nb15mentioning

confidence: 99%

Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT

et al. 2022

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The IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the Edge-IIoT dataset with the Inception Time approach, the best result was an accuracy of 94.94%. The attacks were also assessed in the UNSW-NB15 database using the Inception Time approach, which had an accuracy rate of 98.4%. Using window sequences for the sliding window approach and a six-window size to start training the Inception Time model yielded a slight improvement, with an accuracy rate of 98.6% in the multicategory classification.

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Section: Unsw-nb15mentioning

confidence: 99%

Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT

et al. 2022

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“…The lower right part shows the number of connection records correctly classified as normal class. The accuracy rate of each algorithm can be calculated from formula (6), the regression rate of each algorithm can be calculated from formula (7), and the F1 score of each algorithm can be calculated from formula (8).…”

Section: Analysis Of Binary Classification Experimental Resultsmentioning

confidence: 99%

“…Moreover, this paper visualizes and analyzes the network traffic data after detection and classification, which visually verifies the effectiveness of the algorithm more intuitively. The dataset used in the experiments of this paper is UNSW-NB15 [6]. Compared with KD-DCUP99 [7] and NSL-KDD [8], the dataset UNSW-NB15 has more attack types, can better simulate the network traffic on the internet nowadays and has a more practical reference value.…”

Section: Introductionmentioning

confidence: 99%

Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF

Cheng

Wang

et al. 2022

Algorithms

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Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffic data are dimensioned down with UMAP algorithm. The random forest algorithm is improved based on parameter optimization, and the improved random forest algorithm is used to classify the network traffic data, distinguishing normal data from abnormal data and classifying nine different types of network attacks from the abnormal data. Experimental results on the UNSW-NB15 dataset, which are significant improvements compared to traditional machine-learning methods, show that the UMAP-RF hybrid model can perform network attack traffic detection effectively, with accuracy and recall rates of 92.6% and 91%, respectively.

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“…The analysis provides information, investigates challenges, fundamental analyses of data in terms of security, and forecasts future opportunities for machine learning in networking. From all the proposed classifiers, the random forest outperformed with 86.99% accuracy while Ada boost performed the least with 83.67% test accuracy [ 19 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Botnet Attack Detection in IoT Using Machine Learning

Alissa

Alyas

Zafar

et al. 2022

Computational Intelligence and Neuroscience

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There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This study proposes machine learning methods for classifying binary classes. This purpose is served by using the publicly available dataset UNSW-NB15. This dataset resolved a class imbalance problem using the SMOTE-OverSampling technique. A complete machine learning pipeline was proposed, including exploratory data analysis, which provides detailed insights into the data, followed by preprocessing. During this process, the data passes through six fundamental steps. A decision tree, an XgBoost model, and a logistic regression model are proposed, trained, tested, and evaluated on the dataset. In addition to model accuracy, F1-score, recall, and precision are also considered. Based on all experiments, it is concluded that the decision tree outperformed with 94% test accuracy.

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Spark Configurations to Optimize Decision Tree Classification on UNSW-NB15

Cited by 10 publications

References 15 publications

Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT

Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT

Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF

Botnet Attack Detection in IoT Using Machine Learning

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