<p>Program anomaly detection — modeling normal program executions to detect deviations at runtime as cues for possible exploits — has become a popular approach for software security. To leverage high performance modeling and complete tracing, existing techniques however focus on subsets of applications, e.g., on system calls or calls to predefined libraries. Due to limited scope, it is insufficient to detect subtle control-oriented and data-oriented attacks that introduces new illegal call relationships at the application level. Also such techniques are hard to apply on devices that lack a clear separation between OS and the application layer. This dissertation advances the design and implementation of program anomaly detection techniques by providing application context for library and system calls making it powerful for detecting advanced attacks targeted at manipulating intra- and inter-procedural control-flow and decision variables. </p> <p><br></p> <p>This dissertation has two main parts. The first part describes a statically initialized generic calling context program anomaly detection technique LANCET based on Hidden Markov Modeling to provide security against control-oriented attacks at program runtime. It also establishes an efficient execution tracing mechanism facilitated through source code instrumentation of applications. The second part describes a program anomaly detection framework EDISON to provide security against data-oriented attacks using graph representation learning and language models for intra and inter-procedural behavioral modeling respectively.</p> <p><br> This dissertation makes three high-level contributions. First, the concise descriptions demonstrates the design, implementation and extensive evaluation of an aggregation-based anomaly detection technique using fine-grained generic calling context-sensitive modeling that allows for scaling the detection over entire applications. Second, the precise descriptions show the design, implementation, and extensive evaluation of a detection technique that maps runtime traces to the program’s control-flow graph and leverages graphical feature representation to learn dynamic program behavior. Finally, this dissertation provides details and experience for designing program anomaly detection frameworks from high-level concepts, design, to low-level implementation techniques.</p>

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessment

The conference proceedings contain the abstracts of the 2nd edition of the Advanced Manufacturing Student Conference (AMSC). The second edition of the conference (AMSC22) was again jointly organised by the Faculty of Mechanical Engineering at Chemnitz University of Technology and the Fraunhofer Institute for Machine Tools and Forming Technology. The AMSC is an educational format designed to promote the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings during the conference. The conference itself is intended to serve as a platform for networking at an early career stage. Thus, the AMSC22 provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. The conference proceedings provide a broad overview of the field of advanced manufacturing.:# Foreword # Scientific Committee & Board of Reviewers # Session 1 - A study of the contribution of Additive Manufacturing towards Environmental Sustainability - An analysis about the role of Inkjet devices in Additive Manufacturing - The impact of selected process parameters on dimensional accuracy of fused deposition modeling manufactured parts - Part Cooling Techniques in Additive Manufacturing Process - Effects of Cooling Techniques in WAAM - A Review on Wear Resistance of High-Entropy Alloy Coatings - Importance of Augmented Reality in Automotive Industry - Additive Manufacturing in Aerospace Industry - An Overview of 3D Printing in the Healthcare Industry # Session 2 - Comparison of Additive Manufacturing and Traditional Manufacturing - An analysis on multi-material printing using methods of Additive Manufacturing - A Review on Use of Life Cycle Assessment in Decision-Making - A review of feasibility of diffusion bonding in additive manufacturing and its applications - The Life Cycle Engineering Analysis of Additive Manufacturing Technology - Enterprise Resource Planning - An Industry 4.0 Technology for Operational Efficiency - A review of process and application limits in the field of powder based Additive Manufacturing technologies - 3D Printing processes, materials in Industry 4.0 - A review on efficiency and productivity analysis of human-machine interaction in industry 4.0 # Session 3 - A review of Ultrasonic Additive Manufacturing Technology and Application - The effectiveness of combining rolling deformation with Wire–Arc Additive Manufacturing - A Review of Quality-Related Cost Accounting or Appraisal Methods Using the Example of Additive Manufacturing - Implementing visualization techniques to show the intended motion of Automated Guided Vehicles (AGVs) on the real environment: A review - Discussion on Additive Manufacturing Technology from an Environmental Perspective - Overview of cognitive manufacturing technology in production industries - A review on high velocity oxygen fuel thermal spraying in respective applications - Recognition of Additive section through Diffusion Bonding - Review of lattice structure defects in components manufactured by powder bed fusion process # Session 4 - Biomaterials used for Prosthetic/Implant 3d Printing - Comparison of Conventional and Additive Manufacturing Techniques from an Economic Perspective - Life Cycle Assessment on Natural Fiber Composites - A Review on Design Optimization for Additive Manufacturing - Industry 4.0: Impact on Environmental Sustainability - Review of cyber physical system integrated with augmented reality in various aspects of additive manufacturing - A review of scenarios in selection of milk Packaging by environmental impacts - Review of the integration of Life Cycle Assessment, Environmental Risk Assessment, and Ecosystem Services Assessment about human activities and environmental impact - A review on how Da Vinci surgical system is changing the health care # Session 5 - Integration of Digital twin in manufacturing industries, principal concept, and comparison - An overview of Powder-bed Fusion based Manufacturing process - A review on integration of carbon fiber materials in automotive industry - Review of generating the technologies of organic composite phase change materials in energy storage - Integration of Lean Manufacturing Principles and Industry 4.0 - Pultrusion of carbon/epoxy composites for aerospace applications - new developments and modelling studies - A Review on Stitching Methods for the Inkjet Nozzles - A Review of Digital Twin and its Potential Role in Technical System Development and Operations - Customization using additive manufacturing in industry 4.0 # Session 6 - Effect of Al2O3 particles and mustard oil as lubrication on machining process and a review - A Process-Oriented Review of Supply Chain Management in Automobile Industry - Environmental Impact Assessment on Recycling of Lithium-ion Battery - Methods and Applications of Multi-Material Additive Manufacturing - Fundamental analysis of 3D printing in Dentistry - A review of production methods of Carbon Nanotubes - A review of the environmental compatibility of superconductors - Pros and Cons of applying Fourth Industrial Revolution in a Production Plant: A Review - Intelligence Amplification as a cutting-edge forecasting method: investigation of AI applications, their impacts on our society and their path toward hybrid intelligent systems # Session 7 - Quality Engineering Challenges in Additive Manufacturing - Additive Manufacturing Techniques of 3D Model Hollowing Objects - Implementing Cyber-Physical Systems in Autonomous Micro/Nano Assembly Operations - Producing edge welded bellows seals by using laser welding - An Overview of Additive Manufacturing in the Automotive Industry: study cases and viability - A Review of Digital Twin in Manufacturing

The term Industry 4.0, sometimes referred to as a buzzword, is today on everyone’s tongue and the benefits undeniably seem to be promising and have potential to revolutionize the manufacturing industry. But what does it really mean? From a high-level business perspective, the concept of Industry 4.0 most often demonstrates operational efficiency and promising business models but studies show that many companies either lack understanding for the concept and how it should be implemented or are dissatisfied with progress of already implemented solutions. Further, there is a perception that it is difficult to implement the concept without interference with the current production system.The purpose of this study is to interpret and outline the main characteristics and key components of the concept Industry 4.0 and further break down and conclude the potential benefits and enablers for a manufacturing company within the heavy automotive industry. In order to succeed, a case study has been performed at a manual final assembly production unit within the heavy automotive industry. Accordingly, the study intends to give a deeper understanding of the concept and specifically how manual assembly within an already existing manual production system will be affected. Thus outline the crucial enablers in order to successfully implement the concept of Industry 4.0 and be prepared to adapt to the future challenges of the industry. The case study, performed through observations and interviews, attacks the issue from two perspectives; current state and desired state. A theoretical framework is then used as a basis for analysis of the result in order to be able to further present the findings and conclusion of the study. Lastly, two proof of concept are performed to exemplify and support the findings. The study shows that succeeding with implementation of Industry 4.0 is not only about the related technology itself. Equally important parts to be considered and understood are the integration into the existing production system and design and purpose of the manual assembly process. Lastly the study shows that creating understanding and commitment in the organization by strategy, leadership, culture and competence is of greatest importance to succeed. / Begreppet Industri 4.0, ibland benämnt som modeord, är idag på allas tungor och fördelarna verkar onekligen lovande och tros ha potential att revolutionera tillverkningsindustrin. Men vad betyder det egentligen? Ur ett affärsperspektiv påvisar begreppet Industri 4.0 oftast ökad operativ effektivitet och lovande affärsmodeller men flera studier visar att många företag antingen saknar förståelse för konceptet och hur det ska implementeras eller är missnöjda med framstegen med redan implementerade lösningar. Vidare finns det en uppfattning att det är svårt att implementera konceptet utan störningar i det nuvarande produktionssystemet. Syftet med denna studie är att tolka och beskriva huvudegenskaperna och nyckelkomponenterna i konceptet Industri 4.0 och ytterligare bryta ner och konkludera de potentiella fördelarna och möjliggörarna för ett tillverkande företag inom den tunga bilindustrin. För att lyckas har en fallstudie utförts vid en manuell slutmonteringsenhet inom den tunga lastbilsindustrin. Studien avser på så sätt att ge en djupare förståelse för konceptet och specifikt hur manuell montering inom ett redan existerande manuellt produktionssystem kommer att påverkas. Alltså att kartlägga viktiga möjliggörare för att framgångsrikt kunna implementera konceptet Industri 4.0 och på så sätt vara beredd att ta sig an industrins framtida utmaningar. Fallstudien, utförd genom observationer och intervjuer, angriper frågan från två perspektiv; nuläge och önskat läge. Ett teoretiskt ramverk används sedan som underlag för analys av resultatet för att vidare kunna presentera rön och slutsats från studien. Slutligen utförs två experiment för att exemplifiera och stödja resultatet. Studien visar att en framgångsrik implementering av Industri 4.0 troligtvis inte bara handlar om den relaterade tekniken i sig. Lika viktiga delar som ska beaktas och förstås är integrationen i det befintliga produktionssystemet och utformningen och syftet med den manuella monteringsprocessen. Slutligen visar studien att det är av största vikt att skapa förståelse och engagemang i organisationen genom strategi, ledarskap, kultur och kompetens.

Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.