Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals’ access to cloud-based patient-sensitive data more securely. The experiment’s findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective.

Managing data integrity is a challenging task for any expert or a researcher. This study attempts to collate a Systematic Literature Review of the research efforts done in the domain of healthcare data integrity. The paper highlights the criticalness of data integrity issues in healthcare through attack statistics in the first section. The second section of the paper systematically reviews the previous studies discussing the healthcare related Systematic literature reviews and data integrity techniques in healthcare sector. The third section of this study examines the collated literature through various analysis methodologies and discusses about the most prioritized technique as well as its challenges in healthcare data security. The fourth section illustrates about the various challenges and future directions to take while constructing a roadmap for the future research endeavors in healthcare data integrity management techniques. The concluding segment of the paper presents an objective assessment and sensitivity analysis for finding the implications and difficulties in the studies while outlining feasible solutions. Furthermore, this research endeavour also conducts a Scientometric analysis of all the studies for better understanding of the literature reviewed. Ranking or the Priority analysis part of the paper is totally dedicated to the previously used techniques in healthcare. The paper also discusses about data integrity techniques and postulates that the most prioritized data integrity technique is the blockchain.

Security and usability are often thought of as being contradictive in nature. One affects the other negatively. The relation and trade-offs between usability and security must be detected during developing web application to satisfy the user’s requirements with security perspective. Current approaches of usable-security emphasizes on building systems that are easy to use and secure as well. Hence, this paper is recognizing usability-security as a problem with different attributes contributing towards it. Further, there is a need to assess this problem for the satisfaction of the end user. In this context, this study proposes the track of Fuzzy AHP-TOPSIS (Analytic Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution) technique to assess the usable-security of web application and also identifies the most prioritized attribute contributing towards building usable-security of web application. Moreover, to corroborate the efficacy of the proposed technique, the authors have tested the results on the institutional web applications. The results of the assessment undertaken in this study and the findings tabulated thereafter will be a helpful reckoner for the developers while designing web applications that afford optimum usable-security.