The degree of sophistication of modern cyber-attacks has increased in recent years, and in the future these attacks will more and more target cyber-physical systems (CPS). Unfortunately, today’s security solutions that are used for enterprise information technology (IT) infrastructures are not sufficient to protect CPS, which have largely different properties, involve heterogeneous technologies, and have an architecture that is tailored to specific physical processes. The objective of the synERGY project was to develop new methods, tools and processes for cross-layer anomaly detection (AD) to enable the early discovery of both cyber- and physical-attacks with impact on CPS. To this end, synERGY developed novel machine learning approaches to understand a system’s normal behaviour and detect consequences of security issues as deviations from the norm. The solution proposed by synERGY are flexibly adaptable to specific CPS layers, thus improving the detection capabilities. Moreover, synERGY interfaces with various organizational data sources, such as asset databases, configuration management, and risk data to facilitate the semi-automatic interpretation of detected anomalies. The synERGY approach was evaluated in a utility provider’s environment. This paper reports on the general architecture and the specific pitfalls that needed to be solved, during the design, implementation and deployment of the synERGY system. We foresee this work to be of benefit for researchers and practitioners, who design and implement security systems that correlate massive data from computer logs, the network or organizational context sources, to timely detect cyber attacks.

synERGY: Cross-correlation of operational and contextual data to timely detect and mitigate attacks to cyber-physical systems

Modern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F 1 - score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (

https://doi.org/10.1016/j.eswa.2023.119984

Ticket automation: An insight into current research with applications to multi-level classification scenarios

The adoption of innovative technologies confronts IT-Service-Management (ITSM) with an increasing volume and variety of requests. Artificial intelligence (AI) possesses the potential to augment customer service employees. However, the training data for AI systems are annotated by domain experts with little interest in labeling correctly due to their limited perceived value. Ultimately, insufficient labeled data leads to diminishing returns in AI performance. Following a design science research approach, we provide a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures. We develop a functional prototype based on 17,120 support tickets provided by a pilot partner as an instantiation and evaluate the design through accuracy metrics and user evaluations. Our evaluation revealed that recommendations after label improvement showed increased user ratings, and users are willing to contribute their domain knowledge. The improved labels can be utilized to continuously enhance the AI system as rewards showed increasing growth with decreasing marginal returns. Overall, our results emphasize agents' need for value-in-use by providing better results if they improve the labeling of support tickets pre-labeled by AI. Thus, we provide prescriptive knowledge of a novel HIL design that enables efficient and interactive labeling in the context of diverse applications of reinforcement learning systems. 

A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study

In our digital world, all companies need IT support. IT support staff are under increasing pressure solving increasingly heterogeneous user tickets. Hybrid intelligence could solve many issues due to the combination of machine power and individual strengths of humans. As a part of a bigger design science research project, this paper derives requirements for an IT support system based on hybrid intelligence (ISSHI): In total 17 literature-based requirements and 21 interview-based requirements with IT-support managers and support agents from three different companies were consolidated into 24 requirements to inform an ISSHI system architecture. This architecture serves as a foundation for future research regarding hybrid intelligence in IT support.

http://www.alexandria.unisg.ch/264313/1/JML_854.pdf

Requirements for an IT Support System based on Hybrid Intelligence

As more and more business processes are based on IT services the high availability of these processes is dependent on the IT-Support. Thus, making the IT-Support a critical success factor of companies. This paper presents how this department can be supported by providing the staff with domain-specific and high-quality solution material to help employees faster when errors occur. The solution material is based on previously solved tickets because these contain precise domain-specific solutions narrowed down to e.g., specific versions and configurations of hard-/software used in the company. To retrieve the solution material ontologies are used that contain the domain-specific vocabulary needed. Because not all previously solved tickets contain high-quality solution material that helps the staff to fix issues the de-signed IT-Support system separates low- from high-quality solution material. This paper presents (a) theory- and practical-motivated design requirements that describe the need for automatically retrieved solution material, (b) develops two major design principles to retrieve domain-specific and high-quality solution material, and (c) evaluates the instantiations of them as a prototype with organic real-world data. The results show that previously solved tickets of a company can be pre-processed and retrieved to IT-Support staff based on their current queries.

Knowledge is Power: Provide your IT-Support with Domain-Specific High-Quality Solution Material

High availability of information technology (IT)-applications and –infrastructure components is a significant factor for the success of organizations because more and more business processes depend entirely on IT-services. The result is that the complexity of IT-environments is permanently increasing and therefore cost intensive maintenance processes become more difficult. Several frameworks for IT-service-management like IT Infrastructure Library bundle established best practices to support the task of IT-service operating. This paper provides an overview of data-driven techniques, which can be used in addition to the standards of best practice frameworks to improve reactive and proactive maintenance tasks. We present a selection of different machine learning and statistical methods from a theoretical and practical perspective, which enable detailed insights into the IT-environment status. Furthermore, we present different approaches for the analysis of IT-service tickets, which are the primary communication path between the customer and service provider in case of issues. These methods enhance the resolution-finding process by a higher automatization level or by improvement of the situational understanding in case of root cause analysis and problem determination. Moreover, prediction of the future status allows the initiation of proactive actions to possibly prevent critical situations before their occurrence; existing methods, their potentials, and shortcomings as well are in the center of this paper. We close our overview with a systematic guide for practitioners to select the proper method for their setting, based on a series of questions about the application at hand.

An Overview of Data-Driven Techniques for IT-Service-Management

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An Overview of Data-Driven Techniques for IT-Service-Management