Enhanced Approach of Risk Assessment for Insider Threats Detection in Smart Manufacturing

Abstract

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.

Development of a Theoretical Model for Digital Risks Arising from the Implementation of Industry 4.0 (TMR-I4.0)

“…This type of attack occurs due to the invasion of computers and systems, often by hackers. Through this invasion, valuable data and information can be leaked or even modified, all due to system vulnerabilities [2,11,19,24,[28][29][30][31][32]35,39,41,45,51,52,59,61,69,70,[75][76][77]82,84,97,111,125,[127][128][129][130].…”