Data are being generated and used to support all aspects of healthcare provision, from policy formation to the delivery of primary care services. Particularly, with the change of emphasis from curative to preventive medicine, the importance of data-based research such as data mining and machine learning has emphasized the issues of class distributions in datasets. In typical predictive modeling, the inability to effectively address a class imbalance in a real-life dataset is an important shortcoming of the existing machine learning algorithms. Most algorithms assume a balanced class in their design, resulting in poor performance in predicting the minority target class. Ironically, the minority target class is usually the focus in predicting processes. The misclassification of the minority target class has resulted in serious consequences in detecting chronic diseases and detecting fraud and intrusion where positive cases are erroneously predicted as not positive. This paper presents a new attribute selection technique called variance ranking for handling imbalance class problems in a dataset. The results obtained were compared to two well-known attribute selection techniques: the Pearson correlation and information gain technique. This paper uses a novel similarity measurement technique ranked order similarity-ROS to evaluate the variance ranking attribute selection compared to the Pearson correlations and information gain. Further validation was carried out using three binary classifications: logistic regression, support vector machine, and decision tree. The proposed variance ranking and ranked order similarity techniques showed better results than the benchmarks. The ROS technique provided an excellent means of grading and measuring the similarities where other similarity measurement techniques were inadequate or not applicable.
Broadcast authentication is a fundamental security service in wireless sensor networks (WSNs). Although symmetric-key-based µTESLA-like schemes were employed due to their energy efficiency, they all suffer from DoS attacks resulting from the nature of delayed message authentication. Recently, several public-key-based schemes were proposed to achieve immediate broadcast authentication that may significantly improved security strength. However, while the public-key-based schemes obviate the security vulnerability inherent to symmetric-key-based µTESLA-like schemes, their signature verification is time-consuming. Thus, speeding up signature verification is a problem of considerable practical importance, especially in resource-constrained environments. This paper exploits the cooperation among sensor nodes to accelerate the signature verification of vBNN-IBS, a pairing-free identity-based signature with reduced signature size. We demonstrate through on extensive performance evaluation study that the accelerated vBNN-IBS achieves the longest network lifetime compared to both the traditional vBNN-IBS and the accelerated ECDSA schemes. The accelerated vBNN-IBS runs 66% faster than the traditional signature verification method. Results from theoretical analysis, simulation, and real-world experimentation on a MICAz platform are provided to validate our claims.
Access control is fundamental and prerequisite to govern and safeguard information assets within an organisation. Organisations generally use web enabled remote access coupled with applications access distributed on the various networks facing various challenges including increase operation burden, monitoring issues due to the dynamic and complex nature of security policies for access control. The increasingly dynamic nature of collaborations means that in one context a user should have access to sensitive information and not applicable for another context. The current access control models are static and lack of Dynamic Segregation of Duties (SoD), Task instance level of Segregation and decision making in real time. This paper addresses the limitations and supports access management in borderless network environment with dynamic SoD capability at real time access control decision making and policy enforcement. This research makes three contributions: i) Defining an Authorising Workflow Task Role Based Access Control using the existing task and workflow concepts. It integrates the dynamic SoD considering the task instance restriction to ensure overall access governance and accountability. It enhances the existing access control models such as RBAC by dynamically granting users access right and providing Access governance. ii) Extended the OASIS standard of XACML policy language to support the dynamic access control requirements and enforce the access control rules for real time decision making to mitigate risk relating to access control such as escalation of privilege in broken access control and insufficient logging and monitoring iii) The model is implemented using open source Balana policy engine to demonstrate its applicability to a real industrial use case from a financial institution. The results show that, AW-TRBAC is scalable consuming relatively large number of complex request and able to meet the requirements of dynamic access control characteristics.INDEX TERMS Identity and access management, role based access control, extensible access control markup language, attribute access control, dynamic segregation of duties.
Abstract-This research aims to examine the effectiveness and efficiency of fuzzing hashing algorithm in the identification of similarities in Malware Analysis. More precisely, it will present the benefit of using fuzzy hashing algorithms, such as ssdeep, sdhash, mvHash and mrsh v2, in identifying similarities in Malware domain. The obtained results will be compared with the traditional and most common Cryptographic Hashes, such as the MD5, SHA-1 and SHA-256. Furthermore, it will highlight the pros and cons of fuzzy and cryptographic hashing, as well as their adoption in real world applications.
Managing security is essential for organizations doing business in a globally networked environment and for organizations that are at the same time seeking to achieve their missions and goals. However, numerous technical advancements do not always produce a more secure environment. All kinds of human factors can deeply affect the management of security in an organizational context. Therefore, security is not solely a technical problem; rather, the authors need to understand human factors, which need adequate attention to achieve an effective information security management system practice. This paper identifies direct and indirect human factors that have impact on information security. These factors were analyzed through the study of two security incidents of the UK’s financial organizations using the SWOT (Strength, Weaknesses, Opportunities, and Threats) technique. The study’s results show that human factors are the main causes for these security incidents. Factors such as training, awareness, and security culture influence organizational strength and opportunity relating to information security. People’s irrational behavior and errors are the main weaknesses highlighted in security incidents, which pose threats such as poor reputation and high costs.
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