Semi-supervised based Unknown Attack Detection in EDR Environment

Two Stage Deep Learning Based Stacked Ensemble Model for Web Application Security

“…The advantages, disadvantages, and challenges of the WAF approaches are presented in Table 1. Failing in detecting newly emerging attacks; Bypassing using encoding techniques [13] Updating the signature database Anomalybased WAFs [9][10][11][12][13] Effective in detecting known and newly emerging attacks High FP rate; high variance (overfitting) and high bias (underfitting) [16] Feature extraction and selection; Generalization for real-world Policybased WAFs [14] Elimination of the disadvantages of signature and anomalybased systems Vulnerabilities due to incorrect sequencing of policies [14] Requirement for domain experts, Difficulties in the operation of policies at large-scale systems Hybrid WAF systems [15] Combining the power of signature-based and anomaly-based systems The disadvantages of signature-based and anomaly-based systems Requirement for domain experts to the management of security rules [15] Two different web security studies have been carried out by Nguyen et al [9,10], one of which is based on feature selection, and the other is based on an ensemble model, using different traditional machine learning algorithms. The authors [10] developed WAF models based on generic feature selection (GeFS) and different machine learning.…”

A Review of Convolutional Neural Network Development in Computer Vision

“…As the first layer of image processing, the convolutional layer aims to learn the feature representation of the input image. The convolutional layer consists of multiple filters that map different features [16,17]. In a convolutional neural network, an element in the output of a certain layer is determined when the region size of the corresponding input layer is called the receptive field.…”