Software in the Manufacturing Industry: A Review of Security Challenges and Implications

Software defines digital infrastructures in the manufacturing industry, connecting services and computation resources to machines and devices. These infrastructures aim at increased flexibility, scalability, and a wider application portfolio for automated manufacturing processes. At the same time, the complexity of securing software increases dramatically. Threats to confidentiality, integrity, and availability of software can result in critical losses for automated industrial production and impact manufacturing companies. In order to map existing and emerging security challenges, we present the results of a hermeneutic literature review structured along abstraction levels and vertical integration of software. Based on this structure, we derive implications for academia and practice focused on operators, developers, and security auditors of digital infrastructures. Thereby, we discuss courses of action mapped to software security black boxes, infrastructure heterogeneity, and the adaptation of security for operational usage

Fault management of web services

The use of service-oriented (SO) distributed systems is increasing. Within service orientation web services (WS) are the de facto standard for implementing service-oriented systems. The consumers of WS want to get uninterrupted and reliable service from the service providers. But WS providers cannot always provide services in the expected level due to faults and failures in the system. As a result the fault management of these systems is becoming crucial. This work presents a distributed event-driven architecture for fault management of Web Services. According to the architecture the managed WS report different events to the event databases. From event databases these events are sent to the event processors. The event processors are distributed over the network. They process the events, detect fault scenarios in the event stream and manage faults in the WS

Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study