# Secure Cloud Infrastructure: A Survey on Issues, Current Solutions, and Open Challenges
## 5 Related Existing Solutions in Cloud Levels
### Solutions at Network Level  
In [80], the authors proposed SNORT as an intrusion detection system to defend against DoS and DDoS attacks in cloud computing. The DDoS attack floods the server with a huge number of needless packets and makes it unavailable to legal consumers. The proposed system depends on some written rules to detect and prevent DDoS attacks. Similarly, the authors in [81] proposed an approach to identify and filter a variety of DDoSattacks in cloud environments. This approach is based on the GARCH model and an artificial neural network (ANN). GARCH is used to estimate the value of variances and to figure out any possible anomalies in the real traffic relative to the actual value of variances. The ANN is used to identify the traffic after discarding values that are less than a certain threshold into regular traffic and anomalous traffic. A recent study in [6] presented a technique for the consumers to encrypt and push the data blocks randomly in the P2P network based on blockchain. There are several data centers and multiple users in a distributed cloud, and this may sometimes pose a problem in file block replica placement. Therefore, the blockchain approach seems to be the perfect technique in terms of file security and network transmission delay. Another study in [82] proposed a dynamic proof to communication-efficient recovery and supporting public audibility from data corruptions through irretrievability schemes. In this study, the data were divided into two parts: coding operation and data block which are performed for all blocks individually. The proposed approach can be applied to storage to minimize the update impact on the remote data. Any attempt to update will therefore impact only on small codeword symbols. In addition, an efficient data reform strategy is proposed in case of a server breakdown. The authors in [83] summarized all types of attacks on DNS that exploit the DNS infrastructure. According to the authors, the most used DNS technique is the use of firewalls, which are considered one of the best practices in setting up DNS servers. Furthermore, the dynamic DNS firewall and appropriate signatures protect against whole potential attack surfaces. The study [84] designed a software-as-a-service (SaaS) model that was called OpenPipe. The OpenPipe model adopted a hybrid control mode that was applied with two hierarchical control levels, in which a software-defined networks (SDN) controller forms the higher level, and the local controllers comprise the lower level. The SDN worked to separate the control plane from the data plane to provide network virtualization and programmability. A lab demo was performed to verify the effectiveness of openPipe. The study [85] presented several security approaches that were used to prevent unauthorized access to cloud computing environments, such as certificates (e.g., Public Key Infrastructure), a high level of authentication and authorization, and different encryption methods (e.g., symmetric and asymmetric key algorithms). The authors in [86] proposed a Bayesian network-based weighted attack path modeling technique to model attack paths. They also proposed an optimized algorithm to find the shortest attack path from multiple sources based on key nodes and key edges. Not only does the algorithm find the shortest path, but it also resolves any existing ties between equal weight paths. A study in [87] proposed a Hypervisor Level Distributed Network Security (HLDNS) framework to be deployed on each cloud server. Each server monitors the network traffic between the VMs and the other components such as the virtual network, the internal network, and the external network for intrusion detection. The study tested the proposed HLDNS framework on a cloud-tested NIT Goa by performing various attacks in real-time using recent intrusion detection datasets such as UNSW-NB15 and CICIDS-2017. The results of the experiments were encouraging. The authors in [88] focused on detecting the DDoS attack by developing a deep learning classifier.