## 2 Literature Survey  
[PERSON] et al. [19] presented a web Attack Detection System (ADS) that used the URL analysis advantage. The system, which was implemented on edge devices, was intended to identify web attacks. The cloud was able to manage a variety of issues in the Edge of Things paradigm (EoT). Multiple concurrent deep models were employed to improve system stability while also making the update process easier. The experiment was carried out on a system with '2' parallel deep models, and numerous datasets were used to compare the system to existing systems. The trial findings showed that the system was competent in the detection of web attacks, with 99.41% accuracy, 98.91% True Positive Rate (TPR), and 99.55% Detection Rate of Normal Requests (DRN). The approach, however, was no longer effective in protecting computers from new cyber security threats. [PERSON] et al. [20] developed a P-estimation detection scheme that detected attacks effectively. Centered on the web server logs, this was executed by several trained DL-LSTM models. An estimate of the attack proportion was derived, which was then used to deploy a suitable detection model. This technique considered the dynamic nature of websites, where the popularity of web pages fluctuated over time due to the frequent updating of detection models. The FNR and FPR attained in this methodology were 0.0059% and 0.0%; respectively, together with attack detection capability as low as 2% intensity. Furthermore, this article included mitigation and attribution techniques for identifying and stopping such attacks. However, the technique necessitated many training samples. Thereby, high computational complexities were also identified for Advanced Persistent Threat (APT) attack identification; [PERSON] et al. [21] used an auto-encoder-based deep learning technique. By identifying complex relationships between features, the approach produced a high classification result. Furthermore, by reducing the data size in the encoder, the model eased the process of large-volumedata classification. Initially, the auto-encoder neural network was applied and then the informative features from network traffic data were studied in an unsupervised way. The SoftMax regression layer was then added to the top layer of the built auto-encoder network for APT attack classification. According to the findings, the approach was 98.32% accurate. However, physical tampering or theft by opponents was impossible to prevent.  
[PERSON] et al. [22] developed a dynamic and scalable virtual machine placement algorithm, where the set of moves' computation was limited by means of the Previously-Selected-Server-First (PSSF) policy. The greedy baseline design was built from the obtained set of moves chosen. When a new VM placement request was processed, the highest priority was given to the previously hosted servers or already hosted VM from a similar user in PSSF. This approach's performance comparison was executed with 20 different VM on four different servers. When analogized with other VM placement policies, the developed approach had better efficiency evaluations such as hit rate, and loss rate, coupled with resource loss. The precision of this procedure, however, was not satisfactory.  
[PERSON] et al. [23] presented the latest method for DDoS attacks detection in a CC environment which comprised a Voting Extreme Learning Machine (V-ELM). The NSL-KDD and ISCX intrusion detection datasets were used to evaluate the experiments. The system detected the assaults with an accuracy of 99.18% with the NSL-KDD dataset and 92.11% with the ISCX dataset, according to the tests. Centered on back-propagation ANN, the developed system's performance was analogized with other systems. Black hole optimization, extreme learning machine, random forest, and Adaboost were used to train the ANN. The method, however, was limited by an unacceptably high network overhead, particularly in terms of latency.