Today's in manufacturing major challenge is to manage large scale of cybersecurity system, which is potentially exposed to a multitude of threats. The utmost risky threats are insider threats. An insider threat arises when a person authorized to perform certain movements in an organization decides to mishandle the trust and harm the organization. Therefore, to overcome these risks, this study evaluates various risk assessment method to assess the impact of insider threats and analyses the current gaps in risk assessment method. Based on the literature search done manually, we compare four methods which are NIST, FRAP, OCTAVE, and CRAMM. The result of the study shows that the most used by an organization is the NIST method. It is because NIST is a method that combines the involvement between human and system in term of collection data. The significance of this study contributes to developing a new method in analyzing the threats that can be used in any organization.
In the real network, there must be a large and complex network. The solution to understand that kind of network structure is using the community detection algorithms. There are a lot of other algorithms out there to perform community detection. Each of the algorithms has its own advantages and disadvantages with different types and scale of complex network. The Louvain has been experimented that shows bad connected in community and disconnected when running the algorithm iteratively. In this paper, two algorithm based on agglomerative method (Louvain and Leiden) are introduced and reviewed. The concept and benefit are summarized in detail by comparison. Finally, the Leiden algorithm’s property is considered the latest and fastest algorithm than the Louvain algorithm. For the future, the comparison can help in choosing the best community detection algorithms even though these algorithms have different definitions of community.
The smart manufacturing integrates Internet of Things (IoT), cloud systems, data analytics and machine learning for autonomous implementation particularly in the factory’s supply chain. The additional components from Information Technology (IT) gives convenience accessibility in spike of welcomed threats from inside or outside the boundary to do harmful events. The valuable information in smart manufacturing consists of intellectual property (IP), assets (hardware and software) data and human talent that lead to data theft and illegal activities due to its monetary values. However, little work is done on assessing risks invaluable information particularly threats from inside. In fact, studies on risk assessments are focusing on financial profit, supply chain production and IT assets. Therefore, an enhanced approach of risk assessment for insider threat detection (RAFITD) is introduced. The proposed approach contributes to a log monitoring of the log on and log out activities, based on the role of employee on a software dashboard. From the result, the log monitoring produces two scenarios, which is the ideal and non-ideal. For the ideal scenario, a balanced ratio between log on and log out is accomplished. Meanwhile, a non-ideal scenario performs an imbalance ratio. The impact of the study brings an insider threat detection for better analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.