Abstract
Computer simulations, which are performed at a single wavelength at a time, have been traditionally used to estimate the optical properties of tissues. The results of these simulations need to be interpolated. For a broadband estimation of tissue optical properties, the use of computer simulations becomes time consuming and computer demanding. When spectral measurements are available for a tissue, the use of the photon diffusion approximation can be done to perform simple and direct calculations to obtain the broadband spectra of some optical properties. The additional estimation of the reduced scattering coefficient at a small number of discrete wavelengths allows to perform further calculations to obtain the spectra of other optical properties. This study used spectral measurements from the heart muscle to explain the calculation pipeline to obtain a complete set of the spectral optical properties and to show its versatility for use with other tissues for various biophotonics applications.

Abstract
The government and industry have given the recent development of the Internet of Things in the healthcare sector significant respect. Health service providers retain data gathered from many sources and are useful for patient diagnostics and research for pivotal analysis. However, sensitive personal information about a person is contained in healthcare data, which must be protected. Individual privacy protection is a crucial concern for both people and organizations, particularly when those firms must send user data to data centers due to data mining. This article investigated two general states of increasing entropy by changing the entropy of the class set of characteristics based on artificial intelligence and the k-anonymity model in privacy in context, and also three different strategies have been investigated, i.e., the strategy of selecting the feature with the lowest number of distinct values, selecting the feature with the lowest entropy, and selecting the feature with the highest entropy. For future tasks, we can find an optimal strategy that can help us to achieve optimal entropy in the least possible repetition. The results of our work have been compared by lightweight and MH-Internet of Things, FRUIT methods and shown that the proposed method has high efficiency in entropy criteria.

Abstract
Information systems depend on security mechanisms to detect and respond to cyber-attacks. One of the most frequent attacks is the Distributed Denial of Service (DDoS): it impairs the performance of systems and, in the worst case, leads to prolonged periods of downtime that prevent business processes from running normally. To detect this attack, several supervised Machine Learning (ML) algorithms have been developed and companies use them to protect their servers. A key stage in these algorithms is feature pre-processing, in which, input data features are assessed and selected to obtain the best results in the subsequent stages that are required to implement supervised ML algorithms. In this article, an innovative approach for feature selection is proposed: the use of Visibility Graphs (VGs) to select features for supervised machine learning algorithms used to detect distributed DoS attacks. The results show that VG can be quickly implemented and can compete with other methods to select ML features, as they require low computational resources and they offer satisfactory results, at least in our example based on the early detection of distributed DoS. The size of the processed data appears as the main implementation constraint for this novel feature selection method.

Abstract
In an era marked by escalating cyber threats, the need for robust cybersecurity measures is paramount, especially for Higher Education Institutions (HEIs). As custodians of sensitive information, HEIs must ensure secure channels for data transmission to protect their stakeholders. These institutions should increase their cyber resilience, recognizing the heightened risk they face from cybercriminal activities. A breach in an HEI’s cybersecurity can have severe consequences, ranging from data confidentiality breaches to operational disruptions and damage to institutional reputation. This paper conducts a comprehensive evaluation of the cybersecurity mechanisms in HEIs within Poland. The focus is on assessing the adoption of important web security protocols—Hyper Text Transfer Protocol Secure (HTTPS) and Domain Name System Security Extensions (DNSSEC)—and the implementation of security headers on HEI websites. This study aims to provide a snapshot of the current cyber defense maturity in HEIs and to offer actionable insights for enhancing web security practices. The findings indicate a high adoption rate of HTTPS among HEIs, yet reveal significant gaps in web security practices. Also, there is a low adherence to security headers and an absence regarding DNSSEC implementation across the surveyed institutions. These results highlight crucial areas for improvement and underscore the need for HEIs in Poland to strengthen their web security measures, safeguarding their data and enhancing the overall cybersecurity resilience. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract
Nowadays ransomware attacks have become one of the main problems organizations face. The threat of ransomware attacks, with their capacity to paralyze entire organizations, creates the need to develop a ransomware recovery utility function to help further prepare for the impact of such attacks and enhance the organization's knowledge and perception of risk. This work proposes a ransomware recovery utility function that aims to estimate the impact of a ransomware attack measured in manpower hours till recovery and taking into account different devices and different scenarios.

Abstract
Cyberattacks exploit deceptions involving the Domain Name Service (DNS) to direct users to fake websites, such as typosquatting attacks, which exploit natural typographical errors, and homograph attacks, where different Unicode characters resemble the legitimate ones. The deception attacks may also exploit the confusion between DNS domain names, specifically Top-Level Domains (TLDs), and file extensions. Recently, two new TLDs were added, zip and mov, sharing names with certain file types. This overlapping can be explored by malicious actors in a range of threat scenarios to compromise user security. This paper provides an overview of threats originating from the confusion between specific TLDs and file extensions, such as the recent zip and mov. The threats are grouped into 6 threat scenarios that are described and discussed. This research can be part of a more comprehensive strategy that includes addressing the risks associated with these threats and designing future strategies to address the threats associated with exploiting this ambiguity.