A Markov-Based Intrusion Tolerance Finite Automaton

It is inevitable for networks to be invaded during operation. The intrusion tolerance technology comes into being to enable invaded networks to provide the necessary network services. This paper introduces an automatic learning mechanism of the intrusion tolerance system to update network security strategy, and derives an intrusion tolerance finite automaton model from an existing intrusion tolerance model. The proposed model was quantified by the Markov theory to compute the stable probability of each state. The calculated stable probabilities provide the theoretical guidance and basis for administrators to better safeguard network security. Verification results show that it is feasible, effective, and convenient to integrate the Markov model to the intrusion tolerance finite automaton.

Toward Trustworthy Web Services Coordination

In this chapter, the authors present an overview of recent works on enhancing the trustworthiness of web services coordination for business activities and transactions. The approach is based on what they call application-aware Byzantine fault tolerance. They argue that it is impractical to apply general-purpose Byzantine fault tolerance algorithms for such systems in a straightforward manner. Instead, by exploiting the application semantics, much lighter weight solutions can be designed to enhance intrusion tolerance and, hence, the trustworthiness of systems that require web services coordination.

Intrusion Tolerance Techniques

The authors believe that the research and development of intrusion tolerant systems will gain more momentum as more and more services are offered online. The expectation of such services is high, considering their essential roles in everyday operations of businesses and individuals as well. The impact of service unavailability and security breaches will only grow more serious. In this chapter, the authors survey the state-of-the-art techniques for building intrusion-tolerant systems. They also illustrate a few of the most urgent open issues for future research. Finally, they point out that to build secure and dependable systems we need a concerted effort in intrusion prevention, intrusion detection, and intrusion tolerance.

Life Cycle and Intrusion Tolerance Optimization Topology Models for Wireless Sensor Networks

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">Wireless sensor networks have such disadvantages as upper limit of node energy and poor intrusion tolerance, etc. In light of these disadvantages, by analyzing such key parameters as residual energy, load, node degree, this paper proposes a wireless sensor network (WSN) life-cycle model, which fully considers node energy consumption and load fault tolerance, and a scale-free intrusion tolerance and targeted attacks optimization topology model. Then it verifies their feasibility through simulation test. The results show that the WSN life cycle model takes into account the impacts of residual energy and load capacity on the life cycle and fault tolerance of the system and improves the connectivity probability of high energy consumption nodes and small load nodes, leading to more uniform energy consumption of the wireless sensor network. Through the load adjustment coefficient, the life cycle of the network model is significantly increased. The simulation results show that the fault tolerance and survival time of the proposed model are both improved to some extent compared with those of other models. The proposed scale-free intrusion tolerance and targeted attacks optimization topology model optimizes the power exponent of the network using the structure entropy, and the established scale-free topology structure can make the model more tolerant to intrusion. The simulation results show that the intrusion tolerance of the algorithm proposed in this paper is 2.5 times that of the traditional network model, and the average life cycle is also significantly increased compared to those of other models.</span>