Abstract
The global overview of the challenges faced in trying to minimise the risks of organisations in the face of cyber-attacks is arduous for any organisation. Defining an appropriate risk management model that proactively minimises cybersecurity incidents is a critical challenge. Many malicious attacks occur daily, and there is only sometimes an adequate response. There is a significant investment in research to identify the main factors that may cause such incidents, always trying to have the most appropriate response and, consequently, potentiating the response capacity and success. At the same time, several different methodologies evaluate risk management and the maturity level of organisations. Due to the lack of predictive models based on data (evidence), there is a significant investment in research to identify the main factors that may cause such incidents, starting to design models based on AI-Artificial Intelligence. This research will go in the direction of developing a user-friendly model supporting the assessment of the methodological aspects of an organisation. © 2023, IGI Global.

Abstract
In a competitive business landscape heavily reliant on information and communication technology, organisations must be prepared to address disruptions in their business operations. Business continuity management involves effective planning for the swift reestablishment of business processes in the short term. However, there are still obstacles to implementing business continuity plans, which can be justified by various factors. The purpose of this study is to present the perspectives and future research paths based on a systematic literature review from the peer-reviewed literature published from 1 January 2000 to 31 December 2021. This systematic literature review adheres to the guidelines established by evidence-based software engineering and leverages the Parsifal online tool. The primary research results identify and establish connections between the common components and activities of business continuity management as defined in international standards and frameworks to identify gaps in the existing knowledge. These findings will contribute to the development of a framework that provides a practical approach applicable to organisations of all sizes, taking into account each aspect of business continuity management, with a particular emphasis on information and communication technology systems. This paper's contribution lies in offering insights from a systematic literature review regarding the strategic principles for designing and implementing a business continuity plan, along with a comprehensive overview of related research. Furthermore, it presents a path forward to guide future research efforts aimed at addressing the gaps in the literature concerning continuity planning.

Abstract
The role of digital transformation (DT) in economic development is a vital and recurring point of research. It is particularly relevant if we consider the high percentage of digital transformation initiatives that fail to deliver the expected results, particularly in Small and Medium Enterprises (SMEs). This paper analyzes what is needed to make this transformation successful from an implementation perspective and, simultaneously, from the standpoint of obtaining the company’s expected results. This phenomenon is even more critical to decipher and understand when we look at the small and medium enterprises that face more significant challenges due to the scarcity of resources and needed skills. This work reviews a large variety of models through an extensive systematic literature review (SLR) that assess the readiness and maturity of the digital transformation of enterprises, with a focus on SMEs, with its primary objectives being (1) to review the existing studies and models that assess an organization’s maturity and readiness in the context of digital transformation, focusing on SMEs; (2) to identify if there are gaps considering the importance of the SMEs; and (3) to propose a standardized set of dimensions that should always be considered in a digital transformation assessment. The outcome of this research provides an essential contribution by identifying apparent gaps in the assessment of digital transformation in SMEs and proposing a scalable and standardized set of categories and subcategories that can be used across any future assessment model. These contributions are even more relevant when referencing minimal deep research in the context of SMEs and Digital Transformation.

Abstract
Social Engineering (SE) is a significant problem for enterprises. Cybercriminals continue developing new and sophisticated methods to trick individuals into disclosing confidential information or granting unauthorized access to infrastructure systems. These attacks remain a significant threat to enterprise systems despite significant investments in technical architecture and security measures. User awareness training and other behavioral interventions are critical for improving SE resilience. However, their effectiveness still needs to be determined, as personality traits may turn some individuals more susceptible to SE attacks. This paper aims to provide a comprehensive assessment of the state of knowledge in this field, identifying best practices for improving SE resilience in organizations and supporting the development of new research studies to address this issue. Its goal is to help enterprises of any size develop a framework to reduce the risk of successful SE attacks and create a culture of security awareness.

Abstract
Anomaly detection in the industrial context, identifying defective products and their categorization, is a prevalent task. It is aimed to acknowledge if training and testing multilabel classification models on textures to deploy on an MCU is possible. The focus is deploying lightweight models on MCUs, performing a multilabel classification on textures for industrial usage. For this purpose, a Systematic Literature Review was conducted, which allows knowing the commonly used machine learning models in industrial products anomaly detection and what methods are used to defect detection on textures. Through the Systematic Literature Review, was possible to understand the range of different and combined methods, the methods used in multilabel classification, the most common hyper-parametrizations and popular inferences engines to train machine-learning models to deploy on MCUs, and some techniques applied to overcome the restricted resources of memory and inference time associated with MCUs. © 2024 Elsevier B.V.. All rights reserved.

Abstract
Greenland ice core records display abrupt transitions, designated as Dansgaard-Oeschger (DO) events, characterised by episodes of rapid warming (typically decades) followed by a slower cooling. The identification of abrupt transitions is hindered by the typical low resolution and small size of paleoclimate records, and their significant temporal variability. Furthermore, the amplitude and duration of the DO events varies substantially along the last glacial period, which further hinders the objective identification of abrupt transitions from ice core records Automatic, purely data-driven methods, have the potential to foster the identification of abrupt transitions in palaeoclimate time series in an objective way, complementing the traditional identification of transitions by visual inspection of the time series.In this study we apply an algorithmic time series method, the Matrix Profile approach, to the analysis of the NGRIP Greenland ice core record, focusing on:- the ability of the method to retrieve in an automatic way abrupt transitions, by comparing the anomalies identified by the matrix profile method with the expert-based identification of DO events;- the characterisation of DO events, by classifying DO events in terms of shape and identifying events with similar warming/cooling temporal patternThe results for the NGRIP time series show that the matrix profile approach struggles to retrieve all the abrupt transitions that are identified by experts as DO events, the main limitation arising from the diversity in length of DO events and the method’s dependence on fixed-size sub-sequences within the time series. However, the matrix profile method is able to characterise the similarity of shape patterns between DO events in an objective and consistent way.