| 1. |
- Bomström, Henri, et al.
(författare)
-
Digital Twins About Humans—Design Objectives From Three Projects
- 2022
-
Ingår i: Journal of Computing and Information Science in Engineering. - : The American Society of Mechanical Engineers. - 1530-9827 .- 1944-7078. ; 22:5
-
Tidskriftsartikel (refereegranskat)abstract
- Digital twin (DT) emerges as a key concept of the Industry 4.0 paradigm and beyond. However, the current literature lacks focus on humans and human activities as a part of complex system DTs. Acknowledging human aspects in DTs can enhance work performance, well-being, motivation, and personal development of professionals. This study examines emerging requirements for human digital twins (HDTs) in three use cases of industry–academia collaboration on complex systems. The results draw together the overall design problem and four design objectives for HDTs. We propose to combine the machine and human-related aspects of DTs and highlight the need for virtual-to-virtual interoperability between HDTs and machines alike. Furthermore, we outline differences between humans and machines regarding digital twinning by addressing human activities and knowledge-based behavior on systems. Design of HDTs requires understanding of individual professional characteristics, such as skills and information preferences, together with twinning between the physical and digital machine entities and interactions between the human and machine DTs. As the field moves toward including humans as a part of the DT concept, incorporating HDTs in complex systems emerges as an increasingly significant issue.
|
|
| 2. |
- Booth, Todd, Senior Research Engineer, 1959-
(författare)
-
Design Principles for Network Distributed Denial of Service Defense
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- People, organizations and society are become more and more dependent upon access to Information Systems. Most Information Systems are accessible via the Internet. It is becoming easier and easier to perform successful network attacks against these Information Systems, which causes the system to become unavailable for its intended users. It is also very inexpensive to launch a successful network DRDoS attack against an organization’s servers. One type of distributed denial of service (DDoS) network attack sends a very large volume of traffic towards the victim’s servers. The most common of these volumetric DDoS attacks are described as reflective DDoS service (DRDoS) attacks and the DRDoS defense is the main contribution of this thesis. For years, you have been able to even rent network attack services from criminal organizations, which are often in the form of DRDoS network attacks.The Design Science Research (DSR) approach was used for my research. Included are the DSR cycles performed, including the artifact evaluations. The relationship between the DSR cycles and the published research papers is presented in the paper summary section. The first two papers formed the DSR problem definition. The next three papers used a variety of information hiding techniques to mitigate network attacks. The last paper proposed a different design principle, based on filtering traffic before it reached the public cloud providers. This proposed DRDoS defense approach is to have the public cloud provider request their IP neighbors to filter or drop certain traffic for a big IP block of IP addresses. Then the provider gives IP addresses to their customers, who want this protection, from the big IP block. This way the provider can provide DRDoS protection for hundreds of thousands of customers, with a few firewall rules and the filtering of malicious traffic occurs at the network edge. This solution prevents most of the DRDoS attack traffic from even reaching the public cloud provider. This last research is focused on protecting servers from DRDoS attacks, where the servers are accessible via the Internet and where the servers are or can be hosted via a public cloud provider. This public cloud provider hosting includes accessibility via cloud offerings, such as with Amazon’s Web Services (AWS), Google’s Compute Cloud (GCP), and Microsoft’s Azure. To simplify the discussion, this thesis will focus on Web servers, as the example.The research has been generalized into the following two research design principal contributions. My thesis, including the design principles, contributes to the state of the art network DDoS defense in the following ways:1. Divide and Search for Malicious Network Traffic. After the attack is detected, the IP, Web, and/or DNS address information is changed This mitigates the attacks since the attacker will not be able to quickly learn the new DNS, Web, or IP connectivity information. This has the effect to reduce or mitigate the effect of the DDoS attacks.2. Ask IPX Neighbors to Pre-process Network Traffic. With this design principle, we have two types of features. One feature is to stop malicious traffic. This mitigates the attacks at the public cloud provider’s neighbors, so that most of the malicious traffic never even arrives to the cloud provider. This way, the cloud provider no longer needs to process the malicious traffic to filter it out. The other feature is to provide a different quality of service (QoS) for incoming traffic. This allows the public cloud provider’s neighbor to treat the traffic as higher or lower priority traffic.In this thesis, the contributions are how to improve the state of the art DDoS defense solutions, concerning network attacks against Internet accessible servers. We believe that our DRDoS defense contribution is better, more efficient, and/or more effective than the current state of the art DDRoS solutions. Our contributions are focused on network layer attacks as opposed to application, presentation, or transport layer attacks.
|
|
| 3. |
- Elgendy, Nada, et al.
(författare)
-
DECAS : A Modern Data-Driven Decision Theory for Big Data and Analytics
- 2022
-
Ingår i: Journal of Decision Systems. - : Taylor & Francis. - 1246-0125 .- 2116-7052. ; 31:4, s. 337-373
-
Tidskriftsartikel (refereegranskat)abstract
- Decisions continue to be an essential topic of utmost importance in every research field and era. However, while decision research has extensively offered a wide range of theories, it remains delved in the past, and needs robustness to sustain the future of data-driven decision-making, encompassing topics and technologies such as big data, analytics, machine learning, and automated decisions. Nowadays, decision processes have evolved, the role of humans as decision makers has changed and become inevitably intertwined with the support of machines, rationalities are no longer limited in the same way, data has become an abundant commodity, and the optimizing of decisions is not so far-fetched a tale as it once was in classical times. Accordingly, there is a dire need for new theories to support new phenomena. This paper aims to propose a modern data-driven decision theory, DECAS, to support the new elements of today’s decisions. Our theory extends upon classical decision theory by proposing three main claims: the (big) data and analytics should be considered as separate elements along with the decision-making process, the decision maker, and the decision; the appropriate collaboration between the decision maker and the analytics (machine) can result in a “collaborative rationality,” extending beyond the bounded rationality which decision makers were classically characterized by; and finally, the proper integration of the five elements, and the correct selection of data and analytics, can lead to more informed, and possibly better, decisions. Hence, the theory is elaborated in the paper, and introduced to some data-driven decision examples.
|
|
| 4. |
- Elgendy, Nada, et al.
(författare)
-
Ex-Post Evaluation of Data-Driven Decisions: Conceptualizing Design Objectives
- 2022
-
Ingår i: Perspectives in Business Informatics Research. - Cham : Springer Nature. ; , s. 18-34
-
Konferensbidrag (refereegranskat)abstract
- This paper addresses a need for developing ex-post evaluation for data-driven decisions resulting in collaboration between humans and machines. As a first step of a design science project, we propose four design objectives for an ex-post evaluation solution, from the perspectives of both theory (concepts from the literature) and practice (through a case of industrial production planning): (1) incorporate multi-faceted decision evaluation criteria across the levels of environment, organization, and decision itself and (2) acknowledge temporal requirements of the decision contexts at hand, (3) define applicable mode(s) of collaboration between humans and machines to pursue collaborative rationality, and (4) enable a (potentially automated) feedback loop for learning from the (discrete or continuous) evaluations of past decisions. The design objectives contribute by supporting development of solutions for the observed lack of ex-post methods for evaluating data-driven decisions to enhance human-machine collaboration in decision making. Our future research involves design and implementation efforts through on-going industry-academia cooperation.
|
|
