# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 6 Threats of the Scientific Workflows in Clouds  
Numerous scientific procedures are associated with significant computer activities, including atmospheric science, bioinformatics, high-energy physics, etc. Process intermediate data contains vital secrets in some fields, and its theft would result in significant losses. However, one of the major cloud risks is data leakage because of several tenants [12; 18]. Virtual machines (VMs) execute workflows in cloud-based scientific workflow systems. After completing each workflow sub-task, the intermediate data produced in the virtual machines is employed to execute future sub-tasks. These intermediate data are generally unencrypted as they are temporarily retained in the virtual machine (VM). These intermediary data can be readily appropriated if the attacker can access the virtual computers. In a scientific process, virtual machines (VMs) generally exist within the same tenant network due to the necessity of regular data transfer. The tunneling protocol segregates distinct tenant networks; a failure of this protocol results in the visibility of all tenant communications. Consequently, attackers can infiltrate the networks of other tenants by compromising the tunneling protocol. If an attacker has infiltrated a tenant network hosting numerous VMs conducting sub-tasks of scientific workflows, he can employ a scanning tool to  
Figure 7: Components of data security in cloud computing [32]. acquire information regarding the operating systems of these VMs. Three common threats could affect cloud workflow intermediate data: data loss, malicious media, and traffic eavesdropping [23; 34; 35]. The three threats are explained as follows [35]:  
1. **Data of Loss:** VMs typically store workflow intermediate data. The availability of the intermediate data will be jeopardized if these virtual machines fail, as the intermediate data will be lost. 2. **Traffic Eavesdropping**: This could make it easier for attackers to obtain information transferred over the network unlawfully. Numerous workflows are associated with significant scientific computing activities, including atmospheric science, bioinformatics, high-energy physics, and so forth [25]. Because process intermediate data frequently contains key secrets in some domains, data theft would result in significant losses. The confidentiality of intermediate data will be in jeopardy due to this assault. 3. **Malicious Medium:** This threat involves intercepting and modifying data while it is being transmitted across the network. In some cases, adversaries may introduce malicious content to compromise the security of the data. Such actions can corrupt the workflow by altering or injecting harmful elements into the intermediate data. As a result, the integrity of the data is compromised, leading to inaccurate results and potentially rendering the entire workflow unreliable. Before talking about the need for security, scientific workflow systems must know what challenges can be exposed. Four primary challenges exist [35; 36] and are presented as follows:  
1. To verify that each sub-task can be executed without any VM failures, the systems must assess the average earliest finish time of the virtual cluster about the subtask sub-deadline. 2. Systems must be capable of (i) evaluating the accuracy of sub-task results by analyzing the confidence of intermediate data across all copies and (ii) rectifying modified outputs to safeguard the system from the third type of assault by re-executing the current task. 3. The system must possess sufficient strength to endure the fourth type of attack by eliminating latent threats and purging executors through resource recycling. 4. Preserving system efficiency while implementing security measures, guaranteeing that the fault-intrusion-tolerant method does not adversely affect workflow performance. As discussed earlier, scientific workflows face numerous attacks and challenges that can compromise their progress.