Biblio

Because of the high-value data of an enterprise, sophisticated cyber-attacks targeted at enterprise networks have become prominent. Attack graphs are useful tools that facilitate a scalable security analysis of enterprise networks. However, the administrators face difficulties in effectively modelling security problems and making right decisions when constructing attack graphs as their risk assessment experience is often limited. In this paper, we propose an innovative method of security assessment through an ontology- and graph-based approach. An ontology is designed to represent security knowledge such as assets, vulnerabilities, attacks, countermeasures, and relationships between them in a common vocabulary. An efficient algorithm is proposed to generate an attack graph based on the inference ability of the security ontology. The proposed algorithm is evaluated with different sizes and topologies of test networks; the results show that our proposed algorithm facilitates a scalable security analysis of enterprise networks.

Recently, several health care wearable devices which can intervene in health and collect personal health data have emerged in the medical market. Although health care wearable devices promote the integration of multi-layer medical resources and bring new ways of health applications for users, it is inevitable that some problems will be brought. This is mainly manifested in the safety protection of medical and health data and the protection of user's privacy. From the users' point of view, the irrational use of medical and health data may bring psychological and physical negative effects to users. From the government's perspective, it may be sold by private businesses in the international arena and threaten national security. The most direct precaution against the problem is users' initiative. For better understanding, a research model is designed by the following five aspects: Security knowledge (SK), Security attitude (SAT), Security practice (SP), Security awareness (SAW) and Security conduct (SC). To verify the model, structural equation analysis which is an empirical approach was applied to examine the validity and all the results showed that SK, SAT, SP, SAW and SC are important factors affecting users' data security and privacy protection awareness.

There is widening chasm between the ease of creating software and difficulty of "building security in". This paper reviews the approach, the findings and recent experiments from a seven-year effort to enable consistency across a large, diverse development organization and software portfolio via policies, guidance, automated tools and services. Experience shows that developing secure software is an elusive goal for most. It requires every team to know and apply a wide range of security knowledge in the context of what software is being built, how the software will be used, and the projected threats in the environment where the software will operate. The drive for better outcomes for secure development and increased developer productivity led to experiments to augment developer knowledge and eventually realize the goal of "building the right security in".

Over the last decade, a globalization of the software industry took place, which facilitated the sharing and reuse of code across existing project boundaries. At the same time, such global reuse also introduces new challenges to the software engineering community, with not only components but also their problems and vulnerabilities being now shared. For example, vulnerabilities found in APIs no longer affect only individual projects but instead might spread across projects and even global software ecosystem borders. Tracing these vulnerabilities at a global scale becomes an inherently difficult task since many of the existing resources required for such analysis still rely on proprietary knowledge representation. In this research, we introduce an ontology-based knowledge modeling approach that can eliminate such information silos. More specifically, we focus on linking security knowledge with other software knowledge to improve traceability and trust in software products (APIs). Our approach takes advantage of the Semantic Web and its reasoning services, to trace and assess the impact of security vulnerabilities across project boundaries. We present a case study, to illustrate the applicability and flexibility of our ontological modeling approach by tracing vulnerabilities across project and resource boundaries.

Despite the abundance of information security guidelines, system developers have difficulties implementing technical solutions that are reasonably secure. Security patterns are one possible solution to help developers reuse security knowledge. The challenge is that it takes experts to develop security patterns. To address this challenge, we need a framework to identify and assess patterns and pattern application practices that are accessible to non-experts. In this paper, we narrowly define what we mean by patterns by focusing on requirements patterns and the considerations that may inform how we identify and validate patterns for knowledge reuse. We motivate this discussion using examples from the requirements pattern literature and theory in cognitive psychology.