Iman M. A. Helal

n/a

n/a

In a time of growing threats and advancing circumstances, receiving and keeping up a strong cybersecurity profile in the enterprises are crucial. Important data and resources must be protected. Nowadays, cybersecurity became a predominant issue facing most organizations. It is recognized by organizations as an enterprise-wide issue requiring protection and detection from possible and malicious attacks to protect enterprise information assets. Hence, enterprises are obligated to use multiple tools for covering most of the cybersecurity aspects through different operations and for supporting different levels of users. Information systems auditing is becoming more difficult due to the rapidly developing technological threats. Hence, having these audits and reviews performed by independent functions increase the likelihood of detecting control weaknesses and provides further checks. These control issues are typically not due to the failure of the technology. However, they are mostly the result of individuals not executing the process, or using a process that is poorly defended. The main purpose of this research is to make a comparative study of the capabilities of most of the available automated cybersecurity auditing tools for frontend cloud computing. The results of this comparative study lead to knowing how to secure the enterprise's assets by using automated tools and techniques. Also, it uses clear steps to gather the information to provide the evidence required in the final report of IS auditing.

Recommending the right resource to execute the next activity of a running process instance is of utmost importance for the overall performance of the business process, as well as the resource and for the whole organization. Several approaches have recommended a resource based on the task requirements and the resource capabilities. Moreover, the process execution history and the logs have been used to better recommend a resource based on different human-resource recommender criteria like frequency and speed of execution, etc. These approaches considered the recommendation based on the individual’s execution history of the task that will be allocated to the resource. In this paper, a novel approach based on the co-working history of resources has been proposed. This approach considers the resources that had executed the previous tasks in the current running process instances. Then, it recommends a resource that has the best harmony with the rest of the resources.

n/a

n/a