The Chapter One presents the issue of how to consider anonymization methods for datasets that contain publicly available information, such as SNS data and confidential information. Based on the experimental results, it has been confirmed that the nonanonymized attributes include those that render the narrowing down of data easy and those that make it otherwise. This indicates that there are attributes that cannot be anonymized to increase the degree of security. Since attributes that make it easy to narrow down data contain data with few distributions, it is safer to delete such attributes as identifiers when using the anonymization approach. As a conclusion of this study, the anonymization approach can be applied to partially anonymized data, such as SNS data. However, it is necessary to consider the deletion of the attribute as an identifier, depending on the data distribution.

The Chapter Two explores the psychological models of fear and how they have led to insights that cybersecurity has leveraged to great effect, how repeated exposure to fear appeals can lead to threat fatigue, how corporations have begun to adopt an attack, rather than defense posture, how a derivative of a military war game exercise involving red teaming can be beneficial and how the ethical considerations of exploiting fear appeals need careful consideration. While security threats and security measures are ever present, fears may be evaluated in radically different ways, inducing feelings and actions that are not necessarily productive. It is important that cybersecurity professionals present a realistic appraisal of a cyberthreat but also the knowledge that the threat can be remediated. Ideally, developing a culture of security that harnesses the benefits of protection may enable corporations and people to take rational steps to alleviate their own fears. As an effective form of coercion, fear and terror impair decision-making processes as well as imposing an emotional weight of living with terror, particularly in war. We need to get beyond the tendency in cyber-doom rhetoric to conflate very different threats into one increasingly frightening cyber threat in an attempt to raise awareness and motivate a response which considers the ethics of using fear as a model for cybersecurity adoption.

The Chapter Three focuses on the mathematical models of convex envelopes in LoRaWAN networks. The latter are rarely used for reliability solutions and that is why the development of a new proposal of this type of mathematical modeling will help in the reliability in low power networks, which is part of the LoRa technology and its LoRaWAN protocol that this research proposes. This proposal to be implemented would improve the communication between the nodes, their energy consumption and the delivery of packages. Therefore, LoRa is that type of technology that came to stay and provide new forms of communication between devices or things and the human being. Thus, having a technology that can be trusted is of high importance for these times, since reliability and availability when it comes to demands in certain fields of telecommunications between devices, is essential for this new wave of wireless technology to which we are constantly exposed. Since, it becomes the main complement to understand and know the values to calculate the status of our devices and understand the MTBF, Physical Availability, Failure Rate, Reliability, etc., in order to project future scenarios for the reliability of our electronic equipment. A significant part of the success of maintenance management is due to good information management. All information should be considered important when it comes to reliability in communication networks, especially if this is the information of a telecommunications network such as LoRaWAN, which is being implemented throughout the world due to its versatility for communications.

The Chapter Four assesses the factors influencing usage of debit cards on retail payments a case of Dar es Salaam city. In order to achieve the general objective of the study, the specific objectives had to be formulated which included to identify factors influencing the usage of debit cards on retail payments, to examine the extent to which these factors could affect the influence of using debit cards on retail payments and lastly, to propose a research model showing the factors influencing the usage of debit cards on retail payments. In response to specific objective 1, the study addressed specific objective by first developing several hypotheses with the help of theoretical modal of UTAUT2 which was then tested. The results showed that among nine proposed factors, only seven factors were found significant enough to influence consumers’ behavioural intention towards usage of debit card in retail payments and consumer’s actual usage of debit cards in retail payments, the factors are performance expectancy, effort expectancy, price value, habit and perceived trust which were found to influence consumer’s behavioural intention towards debit card usage on retail payments while habit and behavioural intention were found to influence consumer’s towards the actual usage of debit card on retail payments. In response to specific objective 2, the factors were analysed by statistical tool SPSS by using multiple linear regression, habit was found to have the most predictive power in influencing consumers’ behavioural intention towards the usage of debit cards in retail payments followed by perceived trust, effort expectancy, performance expectancy and price value. Also, habit and behaviour intention were found to have high predictive power on the actual usage of debit cards on retail payments.

The Chapter Five presents a base for further research and discussions on how an IDS model implemented for some “A” attacks can be utilized using Inductive Transfer Learning to learn about other “B” attacks considering feature space for both “A” and “B” attacks is same. To evaluate Inductive Transfer Learning in the field of Information Security. For Information Security, Network Intrusion Detection System is considered. Using IDS, organizations protect their network against attacks like Denial of Service, PortScan, Heartbleed, and other cyberattacks. As a solution, we are proposing a fine-tuning-based transfer learning approach. The proposed inductive learning method can assist in developing a new IDS model from pre-developed IDS for other attacks. Using this approach, the problem of implementing a new IDS from scratch can be avoided because implementing a new IDS from scratch can be difficult sometimes. For instance, if any Deep Learning algorithm such as DNN is considered, then to implement a new DNN from scratch, several things need to be considered like deciding the number of hidden layers, the number of nodes in each hidden layer, optimization function, learning rates, and other important parameters for DNN model. By applying Inductive TL, the questions like deciding the number of hidden layers or number of nodes in hidden layers can be answered by transferring a pre-developed source model to learn about the target task.

The Chapter Six addresses the challenges that are encountered with the common routing protocols and the criteria that are derived by a ROLL to satisfy the routing requirements in LLNs. This Chapter provides an overview to IPv6 over low-power wireless personal area network (6LoWPAN) and IoT protocol stack. It is also noted that the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), is also used to support the traffic flow in a LLNs. LLNs are constrained networks, and the nodes or the routers operate with constraint condition such as limited processing power, memory and energy, and the interconnection links are lossy. However, the Routing Over Low-Power and Lossy (ROLL) Working Group (WG) conclude that the existing IETF routing protocols do not meet the requirements of the constraints devices such as very limited memory, little processing power, and long sleep periods. Hence, these protocols may not fully address the Low-power wireless devices, therefore, the WG drives five criteria (routing state, loss response, control cost, link cost and node cost) for routing in LLNs. The RPL is based on graph construction directed acyclic graph (DAG) to form Destination-Oriented DAG (DODAG). The Rank of a node is a scalar representation of the location of that node within a DODAG Version. Therefore, the Rank is used to avoid and detect loops. Moreover, the Rank value feeds into DODAG parent selection according to the RPL loop avoidance strategy.

The Chapter Seven presents the authors previous work, a method for improving neural network estimation accuracy using attractor behaviour. Existing neural network models mainly aim for accurate estimations of temperature transitions, we have focused on incorporating the attractor bursts of these transitions, finding strong correlations between estimation errors and attractor bursts. Our new method, which combines a simple neural network and temperature attractor behaviours, managed to predict accurate temperatures in Japan. To confirm our method’s scope of application, we performed additional experiments in five cities in Japan in 2019 and in seven cities in the United States, Canada, and Israel in 2016. The Japanese cities’ MAPEs in 2019 were comparable with their MAPEs in 2016, but the MAPEs of seven cities outside Japan were slightly worse than those in Japan. These results validated the usefulness of our method. We plotted the comparisons between estimation errors and attractor bursts for Toronto, Eilat, and Miami in Canada, Israel, and the United States, respectively, in 2016, and found clear correlations between estimation errors and attractor bursts except for Toronto. However, one challenge in our future work would be to explain the lack of correlation between the estimation errors and attractor bursts in Toronto. Approximating specific burst dates remains a challenge as well, but attractor training may be one possible solution to this. In this chapter, meanwhile, we verified the scope of our method’s application by performing additional experiments in 12 cities worldwide. The results endorse the usefulness of our method.