SCoRMiner: Automated Discovery of Network Service and Application Dependencies using a graph mining approach

Fenrir: Blockchain-Based Inter-Company App-Store for the Automotive Industry

“…However, studies [33], [34], [35], [36] seek solutions to the same problem presented in our study. Among these solutions, [36] is notable for containing a fundamental definition of software components, in which a component is equivalent to a mono-functional service with a set of dependencies on other components.…”

“…A promising approach would be automatic parsing and wrapping mechanisms allowing for adapting and standardizing legacy packages, such as in [52]. Second, developer-declarative inter-software dependency potentially poses a threat when increasing the number of authorized services; thus, Fenrir will require some statistical model allowing potential failures that might be unnoticed in the development phase to be traced and detected [33]. Finally, we still need to work on adapting Fenrir for shared driving services and collaborative fleet management services, whose dynamicity may represent a problem for the system security, as well as for large multimedia files that could slow down system performance considerably, effectively blocking the blockchain agent.…”

“…Considering a similar scope, study [33] presents a fourphase process composed of: (1) collecting historical service network flow data set and pre-processing it, (2) computing dependencies and constructing dependency matrices, (3) compiling the constructed matrices into a graph database, and (4) mining this graph database to identify service dependencies. In study [34], the authors use declarative contracts to calculate the dependency graphs in a controlled environment, while in [35], an implementation is proposed for a dependency management mechanism for fully private blockchains to track service logs and collaboratively calculate unknown service dependencies.…”

Identification of Device Dependencies Using Link Prediction

“…Lange et al [9] used the time series-based analysis of network traffic to detect dependencies of network services. The topic became of utmost importance in cloud management, and thus, the SCoRMiner was proposed by Slimani et al [10] to detect dependencies between cloud services using both network traces and application-level data and graph mining.…”

“…The motivation is often network configuration management, e.g., analysis of potential impacts in case of failures, performance issues, and malicious attacks [4], [5], [1]. The previous research revealed dependencies using passively monitored network traffic [6], active approach [7], system logs instead of network traces [8], and applying time series or graph mining [9], [10]. Even though it provided valuable results, it focused on specific input data, had some limitations, or revealed only specific types of dependencies.…”