DIGITAL TRANSFORMATION VULNERABILITIES: ASSESSING THE RISKS AND STRENGTHENING CYBER SECURITY

Digital infrastructure impacts teaching practices and school reform, leading to better access and use of educational resources. This study investigates the teachers' practices on digital infrastructure toward schools’ digital capacity and transformation aimed to evaluate respondents' digital infrastructure, schools' capacity, teachers' practices' impact on digital infrastructure, and the relationship between these factors and their characteristics among teachers of Initao District, during the School Year 2023-2024. A stratified random sampling of 200 elementary and secondary school teachers/respondents employed a patterned and modified questionnaire using descriptive statistics, such as frequency, percentage, mean, and standard deviation, to describe the variable in the study. Furthermore, Pearson's correlation (r) and t-test were also employed to determine the relationships and each difference in the variables used in the study. This study applied a quantitative research method on numerical data and statistical analysis to investigate the relationships between variables and describe the characteristics of the sample population. The result shows that most respondents were 26–35 years old, female, married, with MA units, 6-15 years of teaching experience, teacher 1 in position, and attended training at the district/school level. The study indicates high practices in digital capacity for teaching effectiveness, while the school's digital capacity and transformation are highly regarded in terms of teachers' attributes and professional development. Access to digital infrastructure, digital pedagogical skills, technical support, infrastructure maintenance, and teaching effectiveness significantly affect digital capacity and transformation. At the same time, the highest educational attainment and training/seminars attended on digital infrastructure showed a significant relationship towards teaching effectiveness and their profiles. The study concludes that there is a direct link between effective teaching and appropriate use of digital resources; hence, there is a vital importance of teacher training and continuous support in fully harnessing the benefits of digital infrastructure in education. It is recommended that schools prioritize teacher training, increased funding, improved digital literacy, and collaborative curriculum alignment for effective technology integration.

Internet of Things (IoT) has been thriving in recent years, playing an important role in a multitude of various domains, including industry 4.0, smart transportation, home automation, and healthcare. As a result, a massive number of IoT devices are deployed to collect data from our surrounding environment and transfer these data to other systems over the Internet. This may lead to cybersecurity threats, such as denial of service attacks, brute‐force attacks, and unauthorized accesses. Unfortunately, many IoT devices lack solid security mechanisms and hardware security supports because of their limitations in computational capability. In addition, the heterogeneity of devices in IoT networks causes nontrivial challenges in detecting security threats. In this article, we present a collaborative intrusion detection system (IDS), namely, MidSiot, deployed at both Internet gateways and IoT local gateways. Our proposed IDS consists of three stages: (1) classifying the type of each IoT device in the IoT network; (2) differentiating between benign and malicious network traffic; and (3) identifying the type of attacks targeting IoT devices. The last two stages are handled by the Internet gateways, whereas the first stage is on the local gateway to leverage the computational resources from edge devices. The evaluation results on three popular IDS datasets (IoTID20, CIC‐IDS‐2017, and BOT‐IoT) indicate our proposal could detect seven common cyberattacks targeting IoT devices with an average accuracy of 99.68% and outperforms state‐of‐the‐art IDSs. This demonstrates that MidSiot could be an effective and practical IDS to protect IoT networks.

Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization’s network security. This is because IDSs serve as the organization’s first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innovative methods used to improve IDSs’ performance in network traffic analysis are urgently needed to overcome these drawbacks. In this study, we introduced a model called a deep neural decision forest (DNDF), which allows the enhancement of classification trees with the power of deep networks to learn data representations. We essentially utilized the CICIDS 2017 dataset for network traffic analysis and extended our experiments to evaluate the DNDF model’s performance on two additional datasets: CICIDS 2018 and a custom network traffic dataset. Our findings showed that DNDF, a combination of deep neural networks and decision forests, outperformed reference approaches with a remarkable precision of 99.96% by using the CICIDS 2017 dataset while creating latent representations in deep layers. This success can be attributed to improved feature representation, model optimization, and resilience to noisy and unbalanced input data, emphasizing DNDF’s capabilities in intrusion detection and network security solutions.

Digital transformation along the supply chain

In today’s digital world, cyber security has become a top priority for companies, particularly in Jordan. Owing to the fast assimilation of technology and the growing dependence on digital platforms, enterprises in Jordan encounter an extensive array of cyber hazards that have the potential to jeopardize their information, functions, and standing. The purpose of this article is to present a thorough study of the common cyber threats and cyber security strategies in the context of Jordanian company. This research outlines the main cyber threats that Jordanian firms face, including malware, phishing attacks, distributed denial-of-service (DDoS) assaults, and advanced persistent threats (APTs), through a thorough analysis of the literature that has already been published and industry reports. Because these risks have the potential to cause data breaches, financial losses, and operational interruptions, it is crucial that businesses put strong cyber security measures. The paper delves deeper into the cyber security tactics and best practices that Jordanian companies have used to lessen these risks. Implementing intrusion detection and prevention systems, firewalls, encrypting critical data, carrying out frequent vulnerability assessments, and raising employee understanding of cybersecurity through training and educational initiatives are some of these precautions. Through an analysis of the consequences of cyberattacks and the efficacy of different cyber security strategies, this research offers important information that Jordanian companies may use to improve their cyber resilience. It also emphasizes how crucial it is for the public and commercial sectors to work together with cybersecurity specialists to create a thorough cybersecurity framework that protects the country’s digital infrastructure and promotes a safe business climate.

Edge security for SIP-enabled IoT devices with P4

With the advancement of 5G mobile telecommunication, various IoT (Internet of Things) devices communicate massive amounts of data by being connected to wireless networks. Since this wireless communication is vulnerable to hackers via data leakage during communication, the transmitted data should be encrypted through block ciphers to protect the data during communication. In addition, in order to encrypt the massive amounts of data securely, it is essential to apply one of secure mode of operation. Among them, CTR (CounTeR) mode is the most widely used in industrial applications. However, these IoT devices have limited resources of computing and memory compared to typical computers, so that it is challenging to process cryptographic algorithms that have computation-intensive tasks in IoT devices at high speed. Thus, it is required that cryptographic algorithms are optimized in IoT devices. In other words, optimizing cryptographic operations on these IoT devices is not only basic but also an essential effort in order to build secure IoT-based service systems. For efficient encryption on IoT devices, even though several ARX (Add-Rotate-XOR)-based ciphers have been proposed, it still necessary to improve the performance of encryption for smooth and secure IoT services. In this article, we propose the first parallel implementations of CTR mode of ARX-based ciphers: LEA (Lightweight Encryption Algorithm), HIGHT (high security and light weight), and revised CHAM on the ARMv8 platform, a popular microcontroller in various IoT applications. For the parallel implementation, we propose an efficient data parallelism technique and register scheduling, which maximizes the usage of vector registers. Through proposed techniques, we process the maximum amount of encryption simultaneously by utilizing all vector registers. Namely, in the case of HIGHT and revised CHAM-64/128 (resp. LEA, revised CHAM-128/128, and CHAM-128/256), we can execute 48 (resp. 24) encryptions simultaneously. In addition, we optimize the process of CTR mode by pre-computing and using the intermediate value of some initial rounds by utilizing the property that the nonce part of CTR mode input is fixed during encryptions. Through the pre-computation table, CTR mode is optimized up until round 4 in LEA, round 5 in HIGHT, and round 7 in revised CHAM. With the proposed parallel processing technique, our software provides 3.09%, 5.26%, and 9.52% of improved performance in LEA, HIGHT, and revised CHAM-64/128, respectively, compared to the existing parallel works in ARM-based MCU. Furthermore, with the proposed CTR mode optimization technique, our software provides the most improved performance with 8.76%, 8.62%, and 15.87% in LEA-CTR, HIGHT-CTR, and revised CHAM-CTR, respectively. This work is the fastest implementation of CTR mode on ARMv8 architecture to the best of our knowledge.

The rapid proliferation of Internet of Things (IoT) devices has transformed home automation, enabling seamless control and monitoring through centralized applications. This paper explores the secure integration of Oracle Application Express (APEX) with various IoT devices, including Ring doorbells, Ring Chime, Google Nest thermostats and cameras, Kwikset door locks, MyQ garage doors, iRobot Roomba vacuums, Rachio sprinkler systems, and Amazon Alexa, for the development of the "My Smart Home" app. Oracle APEX, a low-code platform, facilitates RESTful API integrations with these devices, ensuring data retrieval and action execution while prioritizing security through OAuth2 authentication, HTTPS encryption, and role-based access controls. The methodology details step-by-step integration processes, highlighting API endpoints, authentication mechanisms, and secure data handling in APEX 22.2. Discussions address potential vulnerabilities, such as unauthorized access and data breaches, which are mitigated by best practices like token management and encryption. Future trends emphasize AI-driven automation and blockchain for enhanced security. This work provides a comprehensive framework for building scalable, secure smart home applications.

This paper explores various security and privacy issues inherent in IoT devices, ranging from vulnerabilities in device firmware and software to data breaches and unauthorized access. We delve into the challenges of securing IoT devices due to their resource constraints, diverse communication protocols, and often lax security practices during development. Additionally, we discuss privacy implications stemming from the collection and sharing of sensitive personal data by IoT devices, as well as the potential for surveillance and data misuse. Furthermore, we examine the implications of IoT devices in critical infrastructure and industrial systems, where security breaches can have severe consequences. Finally, we propose potential solutions and best practices to address these challenges, including robust encryption methods, regular security updates, and improved authentication mechanisms, to ensure the security and privacy of IoT devices in an increasingly connected world. The exponential growth of IoT devices across various sectors such as smart homes, healthcare, transportation, and industrial automation underscores the importance of ensuring their security and privacy. As these devices become more integrated into daily life and critical infrastructure, any vulnerability can have widespread and severe consequences. The evaluation of alternative performances through Complex Proportionality Assessment (COPRAS) requires an understanding of key criteria, exploration of options, and comparison of relevant facts. Meeting the decision-makers' desire for comparing grades involves choosing among multiple options based on predetermined competing requirements. COPRAS offer a method for such assessments in real-world scenarios, where criteria are nuanced and values cannot be quantified numerically. From the result smart thermostat got the first rank whereas wearable fitness tracker is having the lowest rank.

Digital innovation and transformation: An institutional perspective

As the first line of defense, an Intrusion Detection System (IDS) plays a crucial role in early cyber threat detection and successful mitigation operations. IDSs are increasingly adopting machine learning (ML)-based methods to enhance their detection engines by learning network traffic characteristics and improving the scalability. Although ML-based IDSs have proven effective in detecting diverse cyber attacks, their success is highly dependent on the input data. The scarcity of labeled benign/malicious traffic samples affects various aspects of ML model operations, e.g., benchmarking, explaining and interpreting the results, and continuous model adaptation/improvement. In addition, network traffic sample labeling is costly, complex, requires massive human effort, and is sometimes even impossible.This paper introduces a Flow-based Self-Active IDS (FSA-IDS), which is a novel framework adopting active learning (AL) into self-learning to reduce the labeling cost significantly and to realize an effective IDS. FSA-IDS improves the cyber attack detection performance while reducing false alarms. It employs a novel cluster-based sampling approach that facilitates the labeling automation process and minimizes expert involvement by up to 47% compared to various baselines. We evaluate FSA-IDS using two real-world network traffic datasets comprising a wide range of benign and malicious network traffic samples.

Intrusion detection systems have got the potential to provide the first line of defense against computer network attacks. However, this potential is far from being exploited considering the fact that most of commercial IDS in the market do not identify novel attacks and generate false alerts for legitimate user activities. They mainly deploy misuse detection and are fully dependant on human interaction. Neural networks can be applied successfully to tackle these issues and design better intrusion detection system. Neural networks have already been used to solve many problems related to pattern recognition, data mining, data compression and research is still underway with regards to intrusion detection systems. Unsupervised learning and fast network convergence are some features that can be integrated in the newly designed IDS system using neural networks. This study will aim to explore current applications of neural networks for intrusion detection systems and identify possible neural network algorithms suitable for the task in hand.KeywordsNeural NetworksIntrosion Detectionfalse positive anomally detectionmissuse detection

There are many factors in mucosal secretions that contribute to innate immunity and the 'first line of defence' at mucosal surfaces. Few studies, however, have investigated the effects of exercise on many of these 'defence' factors. The aim of the present study was to determine the acute effects of prolonged exercise on salivary levels of selected antimicrobial peptides (AMP) that have not yet been studied in response to exercise (HNP1-3 and LL-37) in addition to immunoglobulin A (IgA). A secondary objective was to assess the effects of exercise on saliva antibacterial capacity. Twelve active men exercised on a cycle ergometer for 2.5 h at approximately 60% of maximal oxygen uptake. Unstimulated whole saliva samples were obtained before and after exercise. There was a significant decrease (P < 0.05) in salivary IgA:osmolality ratio, following exercise, but IgA concentration and secretion rate were unaltered. Salivary HNP1-3 and LL-37 concentrations (P < 0.01 and P < 0.05, respectively), concentration:osmolality ratios (P < 0.01) and secretion rates (P < 0.01) all increased following exercise. Salivary antibacterial capacity (against E. coli) did not change. The increased concentration of AMPs in saliva may confer some benefit to the 'first line of defence' and could result from synergistic compensation within the mucosal immune system and/or airway inflammation and epithelial damage. Further study is required to determine the significance of such changes on the overall 'defence' capacity of saliva and how this influences the overall risk for infection.

Today, the proportion of elderly and senile people is steadily growing throughout the world. The most important factors in the first line of immune defense of the mucous membranes of the upper respiratory tract are β-defensins, which are a group of secretory proteins with antimicrobial activity. The aim of this study was to study the expression of genes for antimicrobial peptides β-defensins and the composition of the microbiome of the mucous membranes of the upper respiratory tract in elderly people and long-livers with various aging phenotypes.The main study group included 67 centenarians and 49 elderly people, who were further divided into two subgroups depending on the course of aging (pathological and successful aging). Nucleic acids were isolated from nasopharyngeal scrapings and the expression levels of the DEFB1 and DEFB4 genes were determined using real-time polymerase chain reaction. The composition of the microbiota in nasopharyngeal swabs was determined by MALDI-TOF mass spectrometry.In analyzing the expression of the DEFB1 gene in elderly people and centenarians with successful and pathological aging phenotypes, no difference was revealed between the groups. Expression of the DEFB4 gene was increased in centenarians with pathological aging compared to centenarians with successful aging and in the elderly group. Excessive production of antimicrobial peptides is dual in nature; on the one hand, they provide the first line of defense against microorganisms, and on the other, they are cytotoxic to their own cells. An increase in the expression of the DEFB4 gene during aging may be due to an increase in the number of pathogen-associated molecular patterns, which can be one’s own microbiota and/or components of microbial metabolism. Analysis of the microbiota composition showed an increase in biodiversity in individuals with a successful aging phenotype compared to a pathological phenotype. Particular attention is paid to Staphylococcus spp., the species composition of which depends on the aging phenotype. In the pathological aging group, the frequency of St. aureus colonization is significantly higher than in the successful aging group.Thus, overexpression of the DEFB4 gene and changes in the composition of the microbiota of the mucous membranes of the upper respiratory tract may be one of the mechanisms explaining the increased susceptibility to infections in various aging phenotypes.

This dissertation proposes a solution to the growing concern about the security of Internet of Things (IoT) devices in home or business environments. Entitled MAIOS (Multi Accountability IoT Oriented Switch), the system offers an SDN (Software Defined Networking) switch architecture, which provides effective segmentation for IoT devices, increasing their protection against cyber threats. The research begins by contextualizing the exponential increase in IoT devices and the security challenges associated with them. MAIOS addresses this issue through a centralized control layer, which allows efficient management of IoT devices and the implementation of dynamic security policies, which can be applied from an SDN control plane application layer. The system was designed considering functional, non-functional requirements and deployment scenarios, either in home or business environments. To increase usability, an intuitive interface was developed for users, facilitating the configuration and monitoring of IoT devices. The solution is based on open source technologies such as pfSense proxy/firewall and Home Assistant to provide functionalities such as firewall, IoT control and real-time traffic monitoring. The effectiveness of MAIOS is evaluated through security and performance tests in simulated environments, which demonstrated its ability to identify and mitigate cyber threats, providing robust security for IoT devices. This dissertation offers a contribution to the field of IoT security, presenting a practical, affordable and effective solution to protect devices in residential and business networks. MAIOS represents an important advance in ensuring digital security in an increasingly connected world.