## 4 Results and Discussion
### RQ9 and RQ10: What Is the Research Focus and Limitations According to the PS?
#### 4.9.5 Ensemble Classification Models  
Ref. [40] focused on detecting web attacks in HTTP requests sent by edge devices to the cloud using a weighted average ensemble model composed of two Residual Networks (ResNet) that use different feature representations of URLs. As with previous studies, the online updating of detection models and adversarial attacks are two problems that are not addressed by this work.  
Ref. [54] implemented a detection method of web attacks in heterogeneous and adversarial environments using an adaptive IDS, which is an ensemble classification model that aims at tracking the best classification model for each data instance. As future work, they plan to apply the proposed method to malware and network intrusion detection and compare it with other ensemble classification algorithms.  
Ref. [75] proposed a web attacks detection system for IoT networks, which consists of an ensemble classifier that identifies malicious URLs based on the predictions obtained from three sub-models--CNN, LSTM, and ResNet--and an update module that fine-tunes each sub-model in order to detect novel web attacks. Moreover, they showed that the proposed system outperforms each sub-model as well as other state-of-the-art classification models. However, they plan to explore other alternative Deep Learning models to improve the performance of the overall system as well as to detect web attacks other than XSS attacks and SQL injection attacks.  
In [89], the authors proposed a data-augmentation method based on self-adapting noise adding, which consists of adding noisy data to web attacks datasets in proportion with the web HTTP request length. The method aims at overcoming the problem of classification in unbalanced datasets. They evaluated the method by implementing different state-of-the-art DL models and comparing their classification performance with and without data augmentation on public datasets as well as on a synthetic dataset for both binary classification and multi-classification problems. The results showed that the Bi-LSTM model with the DA-SANA method has the best classification performance values. The method presents the limitations of time cost and computational resources required to train Bi-LSTM models. In addition, the DA-SANA method does not consider the files uploaded as part of the web HTTP request length.  
In [91], the authors propose a detection, mitigation, and attacker profiling system for securing web applications against web attacks. The system is composed of a Cookie Analysis Engine (CAE) and a DL classifier. First, the HTTP request is checked for forged cookies. Then, it is forwarded to the DL classifier. The decision of whether the request is normal or malicious is based on the combined outcome of the CAE and the DL classifier. Furthermore, if a user is profiled as suspicious or attacker, the subsequent requests will be blocked without calling neither the CAE nor the DL classifier. Then, the attacker's profiling option aims at reducing the processing time, which makes the proposed system suitable for real-time environments. A state-of-the art comparison showed that the proposed system has the best performance results on the private dataset as well as on the CSIC2010 dataset and in the real-time environment. They also mentioned that most web attacks are injected in the HTTP payloads rather than in the cookies. They plan to join the proposed system with a deception mechanism that is intended to analyze the characteristics of the attacks and the tactics of attackers.