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The dependence on the use of information systems for nearly every activity and functions in the internet is increasingly high. This form of interconnectedness has bolstered national economies, enhanced how governments interact with their citizens and how ordinary people connect with friends and family. However, this dependence has equally resulted to a high rise in vulnerability, threat and risk associated with more use of information and communication technology. Cyber-attacks that have the potential to disrupt or damage information system infrastructure are getting more complex with some level of sophistication. Traditional protection of information system infrastructure is no longer sufficient; systems have proven to be immune to failure or incidents. This paper aims to ensure that there is a continuous availability of services through a fail-safe proof.

MYSQL replication technique was used to develop a model based on three-tier layers using the principle of network interdependency and the replication techniques. Tier 1 depicts a Telecom organization serving as service provider that provides internet service to Tier 2 organization – a Bank; Tier 3 is the financial App that can be used by bank staff and customers. The fail-safe mode integrated mechanism enables Tier 3 to continue to render its services in the event of an attack on Tier 1 such as DDoS without disruption.

This technique succeeded in mitigating the loss of data if cyber incident occurred or reception of uninterrupted services is countered, which give rise to future master-to-master architecture.

The study conducted is limited to the design and development of a fail-safe system for interdependent networks or systems using MYSQL replication technique.

In an interdependent environment such as the cyberspace, the sectors are interdependent for optimal results. The originality of the work ensures that there is availability of services which is sustained and that data integrity is assured using the fail-safe technique based on MySQL replication method.

Given how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system.

This research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed.

The tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results.

Cyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophisticated malware attacks on key infrastructure containing sensitive data. The process of detecting the presence of malware requires expertise in malware analysis. Applying intelligent methods to this process can aid practitioners in identifying malware’s behaviour and features. This is especially expedient where the malware is stealthy, hiding IOC.

Literature repeatedly complains about the lack of empirical data on the costs of cyber incidents within organizations. Simultaneously, managers urgently require transparent and reliable data in order to make well-informed and cost-benefit optimized decisions. The purpose of this paper is to (1) provide managers with differentiated empirical data on costs, and (2) derive an activity plan for organizations, the government and academia to improve the information base on the costs of cyber incidents.

The authors analyze the benchmark potential of costs within existing literature and conduct a large-scale interview survey with 5,000 German organizations. These costs are directly assignable to the most severe incident within the last 12 months, further categorized into attack types, cost items, employee classes and industry types. Based on previous literature, expert interviews and the empirical results, the authors draft an activity plan containing further research questions and action items.

The findings indicate that the majority of organizations suffer little to no costs, whereas only a small proportion suffers high costs. However, organizations are not affected equally since prevalence rates and costs according to attack types, employee classes, and other variables tend to vary. Moreover, the findings indicate that board members and IS/IT-managers show partly different response behaviors.

The authors present differentiated insights into the direct costs of cyber incidents, based on the authors' knowledge, this is the largest empirical survey in continental Europe and one of the first surveys providing in-depth cost information on German organizations.

The purpose of the study is to identify cybersecurity threats that hinder the adoption of digital banking and provide sustainable strategies to combat cybersecurity risks in the banking industry.

Systematic literature review guidelines were used to conduct a quantitative synthesis of empirical evidence regarding the impact of cybersecurity threats and risks on the adoption of digital banking.

A total of 84 studies were initially examined, and after applying the selection and eligibility criteria for this systematic review, 58 studies were included. These selected articles consistently identified identity theft, malware attacks, phishing and vishing as significant cybersecurity threats that hinder the adoption of digital banking.

With the country’s banking sector being new in this area, this study contributes to the scant literature on cyber security, which is mostly in need due to the myriad breaches that the industry has already suffered thus far.

The issue of cybersecurity has been cast as the focal point of a fight between two conflicting governance models: the nation-state model of national security and the global governance model of multi-stakeholder collaboration, as seen in forums like IGF, IETF, ICANN, etc. There is a strange disconnect, however, between this supposed fight and the actual control over cybersecurity “on the ground”. This paper aims to reconnect discourse and control via a property rights approach, where control is located first and foremost in ownership.

This paper first conceptualizes current governance mechanisms through ownership and property rights. These concepts locate control over internet resources. They also help us understand ongoing shifts in control. Such shifts in governance are actually happening, security governance is being patched left and right, but these arrangements bear little resemblance to either the national security model of states or the global model of multi-stakeholder collaboration. With the conceptualization in hand, the paper then presents case studies of governance that have emerged around specific security externalities.

While not all mechanisms are equally effective, in each of the studied areas, the author found evidence of private actors partially internalizing the externalities, mostly on a voluntary basis and through network governance mechanisms. No one thinks that this is enough, but it is a starting point. Future research is needed to identify how these mechanisms can be extended or supplemented to further improve the governance of cybersecurity.

This paper bridges together the disconnected research communities on governance and (technical) cybersecurity.

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.

The methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).

Machine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.

All machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.

The study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.

The research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

Cyberattacks are becoming increasingly widespread, and cybersecurity is therefore increasingly important. Although the technological aspects of cybersecurity are its best-known characteristics, the cybersecurity phenomenon goes beyond the detection of technological impacts, and encompasses all the dimensions of an organization. This study thus focusses on an additional set of organizational elements. The key elements of cybersecurity organizational readiness depicted here are cybersecurity awareness, cybersecurity culture and cybersecurity organizational resilience (OR). This study aims to qualitatively assess small and medium enterprises’ (SMEs) overall level of organizational cybersecurity readiness.

This study focused on conducting a cybersecurity organizational readiness assessment using a sample of 53 Italian SMEs from the information and communication technology sector. Informed mixed method research, this study was conducted consistent with the principles of the explanatory sequential mixed method design, and adopting a quanti-qualitative methodology. The quantitative data were collected through a questionnaire. Qualitative data were subsequently collected through semi-structured interviews.

Although many elements of the technical aspects of cybersecurity OR have yielded very encouraging results, there are still some areas that require improvement. These include those facets that constitute the foundation of cybersecurity awareness, and, thus, a cybersecurity culture. This result highlights that the areas in need of improvement are exactly those that are most important in fighting against cyber threats via organizational cybersecurity readiness.

Although the importance of SMEs is obvious, evidence of such organizations’ attitudes to cybersecurity are still limited. This research is an attempt to depict the organizational issue related to cybersecurity, i.e. overall cybersecurity organizational readiness.

The Cloud of Things (IoT) that refers to the integration of the Cloud Computing (CC) and the Internet of Things (IoT), has dramatically changed the way treatments are done in the ubiquitous computing world. This integration has become imperative because the important amount of data generated by IoT devices needs the CC as a storage and processing infrastructure. Unfortunately, security issues in CoT remain more critical since users and IoT devices continue to share computing as well as networking resources remotely. Moreover, preserving data privacy in such an environment is also a critical concern. Therefore, the CoT is continuously growing up security and privacy issues. This paper focused on security and privacy considerations by analyzing some potential challenges and risks that need to be resolved. To achieve that, the CoT architecture and existing applications have been investigated. Furthermore, a number of security as well as privacy concerns and issues as well as open challenges, are discussed in this work.

This paper proposes a holistic, proactive and adaptive approach to cybersecurity from a service lens, given the continuously evolving cyber-attack techniques, threat and vulnerability landscape that often overshadow existing cybersecurity approaches.

Through an extensive literature review of relevant concepts and analysis of existing cybersecurity frameworks, standards and best practices, a logical argument is made to produce a dynamic end-to-end cybersecurity service system model.

Cyberspace has provided great value for businesses and individuals. The COVID-19 pandemic has significantly motivated the move to cyberspace by organizations. However, the extension to cyberspace comes with additional risks as traditional protection techniques are insufficient and isolated, generally focused on an organization's perimeter with little attention to what is out there. More so, cyberattacks continue to grow in complexity creating overwhelming consequences. Existing cybersecurity approaches and best practices are limited in scope, and implementation strategies, differing in strength and focus, at different levels of granularity. Nevertheless, the need for a proactive, adaptive and responsive cybersecurity solution is recognized.

This paper presents a model that promises proactive, adaptive and responsive end-to-end cybersecurity. The proposed cybersecurity continuity and management model premised on a service system, leveraging on lessons learned from existing solutions, takes a holistic analytical view of service activities from source (service provider) to destination (Customer) to ensure end-to-end security, whether internally (within an organization) or externally.