Trace malicious source to guarantee cyber security for mass monitor critical infrastructure

The proposed traceback scheme does not take into account the trust of node which leads to the low effectiveness. A trust-aware probability marking (TAPM) traceback scheme is proposed to locate malicious source quickly. In TAPM scheme, the node is marked with difference marking probability according to its trust which is deduced by trust evaluation. The high marking probability for low trust node can locate malicious source quickly, and the low marking probability for high trust node can reduce the number of marking to improve the network lifetime, so the security and the network lifetime can be improved in TAPM scheme

A Trust-Based Model for Security Cooperating in Vehicular Cloud Computing

VCC is a computing paradigm which consists of vehicles cooperating with each other to realize a lot of practical applications, such as delivering packages. Security cooperation is a fundamental research topic in Vehicular Cloud Computing (VCC). Because of the existence of malicious vehicles, the security cooperation has become a challenging issue in VCC. In this paper, a trust-based model for security cooperating, named DBTEC, is proposed to promote vehicles’ security cooperation in VCC. DBTEC combines the indirect trust estimation in Public board and the direct trust estimation in Private board to compute the trust value of vehicles when choosing cooperative partners; a trustworthy cooperation path generating scheme is proposed to ensure the safety of cooperation and increase the cooperation completion rates in VCC. Extensive experiments show that our scheme improves the overall cooperation completion rates by 6~7%

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC