# Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges
## 5 Related Existing Solutions in Cloud Levels
### Solutions at Application Level  
To mitigate risks at the application level, several solutions have been proposed by researchers. For instance, a study in [62] provided a novel \"Scale Inside Out\" technique that decreases the Resource Utilization Factor to a minimum value during attacks to rapidly absorb the DDoS attack. In addition to other co-located facilities, the recommended solution sacrifices victim service resources and provides certain resources to the prevention service to assess the availability during the attack. According to the study, the experimental evaluation indicates a decrease of up to 95% in total attack downtime of the victim's service in addition to significant improvements in attack detection and reporting time and downtime of co-located facilities. A contribution in [63] suggested a method to limit the effects of economic denial of service (EDoS) attacks on cloud applications. This method was dependent on the adoption of the service level agreement (SLA) supplemented by an intrusion prevention scheme (IPS). Another effort in [64] suggested a new method that used an artificial neural network (ANN) along with the genetic algorithm (GA) for EDoS attack detection in the cloud. The classification was carried out using an ANN that classifies the customer of the cloud server and minimizes the EDoS attacks in the cloud environment, while the GA was used to optimize the attributes of each server using appropriate fitness functions. Researchers in [5] proposed an approach to mitigate EDoS attacks in the SDN-based cloud computing environment. An unsupervised deep learning technique called long short-term memory (LSTM) was used as a multivariate time series anomaly detection model. The main concept was to try to predict the values of a cloud customer's resource use (memory use, CPU load, etc.). The experiments were conducted with different EDoS attack levels and proved that the proposed approach was an effective and innovative solution for SDN-based cloud defense of EDoS attacks according to the authors. Another study in [37] designed a technique for identifying cloud computing DDoS attacks. This technique employs machine learning algorithms such as support vector machine (SVM), naive Bayes (NB), and random forest (RF) for classification. The study was carried out using Tor Hammer as an attacking tool on a cloud environment, and a new dataset for the intrusion detection technique was developed. A study in [65] discussed various authentication schemes used in cloud computing and proposed a framework for using passphrase-based multifactor authentication to make cloud resources more secure. The primary comparison of authentication models reinforced the level of security and the disadvantages of corresponding schemes in the cloud computing environment. The passphrase in the proposed scheme ensures secure passwords and provides extra security for the SSH key pair. In [66], the authors proposed an authentication-based AES and MD5 technique for data encryption to protect the data and the login of the users over the cloud at the time of login. The authors in [67] proposed a novel security model for authentication-supported cloud computing. The model introduced a new idea for a biometric security system based on fingerprint recognition. The proposed method automated the verification process to match human fingerprints, where fingerprints are used to identify the individuals and verify their identity. Users are authenticated based on the fingerprint templates, which must be given based on random numbers generated each time. The experimental results showed that the proposed system outperforms the single-fingerprint authentication system. The researchers in [68] recently proposed a novel hash-based, multi-factor, secure mutual authentication scheme that includes mathematical hashing properties, certificates, nonce values, traditional user IDs and password mechanisms. The strength of the proposed authentication procedure was evaluated using the GNY belief logic and the Scyther method.