A Comparative Study of Class Rebalancing Methods for Security Bug Report Classification

Zheng, Wei; Xun, Yuxing; Wu, Xiaoxue; Deng, Zong Quan; Chen, Xiang; Sui, Yulei

doi:10.1109/tr.2021.3118026

Cited by 72 publications

(35 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The malicious samples (positive samples) come from the XSSed database and the tested payload (Payload) in the penetration test. Additionally, 75,428 pieces of standard data are obtained to ensure a balanced selection of samples ( Zheng et al, 2021 ; Cai et al, 2022 ). In the experiment, the training set and the test set are randomly selected from the samples at a ratio of 7:3.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Cross-site scripting attack detection based on a modified convolution neural network

Yan

Feng

You³

et al. 2022

Front. Comput. Neurosci.

View full text Add to dashboard Cite

Cross-site scripting (XSS) attacks are currently one of the most threatening network attack methods. Effectively detecting and intercepting XSS attacks is an important research topic in the network security field. This manuscript proposes a convolutional neural network based on a modified ResNet block and NiN model (MRBN-CNN) to address this problem. The main innovations of this model are to preprocess the URL according to the syntax and semantic characteristics of XSS attack script encoding, improve the ResNet residual module, extract features from three different angles, and replace the full connection layer in combination with the 1*1 convolution characteristics. Compared with the traditional machine learning and deep learning detection models, it is found that this model has better performance and convergence time. In addition, the proposed method has a detection rate compared to a baseline of approximately 75% of up to 99.23% accuracy, 99.94 precision, and a 98.53% recall value.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Cross-site scripting attack detection based on a modified convolution neural network

Yan

Feng

You³

et al. 2022

Front. Comput. Neurosci.

View full text Add to dashboard Cite

show abstract

“…The testing findings reveal that the suggested method's detection accuracy is 98.98 percent, indicating that it can successfully identify DDoS attack traffic in an SDN context [1]. Based on their analysis of the impact of class imbalance on SBR prediction, Zheng et al [32] found that it had a negative impact on prediction accuracy. A random forest classifier was used by Zhang et al [33] to train a Just-in-Time defect prediction model based on six open source projects.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 96%

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Esmaeili

Goki

Masjidi

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The Internet of Things (IoT) is a complicated security feature in which datagrams are protected by integrity, confidentiality, and authentication services. The network is protected from external interruptions and intrusions. Because IoT devices run with a range of heterogeneous technologies and process data over time, standard solutions may not be practical. It is necessary to develop intelligent procedures that can be used for multiple levels of data flow in the system. This study examines metainnovations using deep learning-based IDS. Per the findings of the earlier tests, BiLSTMs are better for binary (regular/attacker) classification; however, sequential models (LSTM or BiLSTM) are better for detecting some brutal attacks in multiclass classifiers. According to experts, deep learning-based intrusion detection systems can now recognize and select the best structure for each category. However, specific difficulties will need to be solved in the future. Two topics should be studied further in future attempts. One of the researchers’ concerns is the impact of various data processing techniques, such as artificial intelligence or metamethods, on IDS. The BiLSTM approach has chosen the safest instances with the highest accuracy among the models. According to the findings, the most reliable and suitable solution for evaluating DDoS attacks in IoT is the BiLSTM design.

show abstract

“…First, the GBDT algorithm is introduced to verify XGBoost results. e advantage of this algorithm is that it can deal with nonlinear data and is flexible [76][77][78]. In addition, it uses some robust loss functions, such as the Huber loss function and the Quantile loss function, which are very robust to outliers.…”

Section: Robust Testmentioning

confidence: 99%

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach

Fan

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing this, we employ the BP neural network algorithm combined with the DEA model to measure the urban inclusive growth efficiency (IGE). Furthermore, a nest of machine learning (ML) algorithms are introduced to explore the drivers of urban IGE, which overcomes the defects of endogeneity and multicollinearity of traditional econometric methods. We find for the overall sample that entrepreneurship and innovation contribute the most to IGE, accounting for about 35%, respectively, and they are the most critical drivers, while the heterogeneity test results reveal that the contribution of influencing factors has changed for different regions such as the eastern region, the central region, and the western region. Based on the experimental results of the ML model, we provide some policy suggestions for China and similar developing countries and emerging economies to promote IG.

show abstract

A Comparative Study of Class Rebalancing Methods for Security Bug Report Classification

Cited by 72 publications

References 43 publications

Cross-site scripting attack detection based on a modified convolution neural network

Cross-site scripting attack detection based on a modified convolution neural network

ML-DDoSnet: IoT Intrusion Detection Based on Denial-of-Service Attacks Using Machine Learning Methods and NSL-KDD

Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach

Contact Info

Product

Resources

About