Secret key generation based on channel characteristics is an effective physical-layer security method for 5G wireless networks. The issues of how to ensure the high key generation rate and correlation of the secret key under active attack are needed to be addressed. In this paper, a new practical secret key generation scheme with high rate and correlation is proposed. In our proposed scheme, Alice and Bob transmit independent random sequences instead of known training sequences or probing signals; neither Alice nor Bob can decode these random sequences or estimate the channel. User's random sequences together with the channel effects are used as common random source to generate the secret key. With this solution, legitimate users are able to share secret keys with sufficient length and high security under active attack. We evaluate the proposed scheme through both analytic and simulation studies. The results show that our proposed scheme achieves high key generation rate and key security, and is suitable for 5G wireless networks with resource-constrained devices.

Current Public Key Infrastructures suffer from a scaling problem, and some may have security problems, even given the topological simplification of bridge certification authorities. This paper analyzes the security problems in Bridge Certificate Authorities (BCA) model by using the concept of "impersonation risk," and proposes a new modified BCA model, which enhances its security, but is a bit more complex incertification path building and implementation than the existing one.

Those host-based intrusion detection models like VPStatic first construct a model of acceptable behaviors for each monitored program via static analysis, and then perform intrusion detection by comparing them with programs' runtime behaviors. These models usually share the highly desirable feature that they do not produce false alarms but face the conflicts between accuracy and efficiency. For instance, the high accuracy of the VPStatic model is at the cost of high space complexity. In this paper, we use a statically-constructed state transition table (STT), which records expected transitions among system calls as well as their stack states (return address lists), as a behavior model to perform context-sensitive intrusion detection. According to our analysis, our STT model improves the space efficiency of the VPStatic model without decreasing its high precision and time efficiency. Experiments show that for three test programs, memory uses of our STT models are all much less than half of the VPStatic models'. Thereby, we alleviate the conflicts between the accuracy and the efficiency.

A novel adaptive reputation-based virtual organization formation is proposed. It restrains the bad performers effectively based on the consideration of the global experience of the evaluator and evaluates the direct trust relation between two grid nodes accurately by consulting the previous trust value rationally. It also consults and improves the reputation evaluation process in PathTrust model by taking account of the inter-organizational trust relationship and combines it with direct and recommended trust in a weighted way, which makes the algorithm more robust against collusion attacks. Additionally, the proposed algorithm considers the perspective of the VO creator and takes required VO services as one of the most important fine-grained evaluation criterion, which makes the algorithm more suitable for constructing VOs in grid environments that include autonomous organizations. Simulation results show that our algorithm restrains the bad performers and resists against fake transaction attacks and badmouth attacks effectively. It provides a clear advantage in the design of a VO infrastructure.

The recommend system has been widely used in many web application areas such as e-commerce services. With the development of the recommend system, the HIN modeling method replaces the traditional bipartite graph modeling method to represent the recommend system. But several studies have already showed that recommend system is vulnerable to shilling attack (injecting attack). However, the effectiveness of how traditional shilling attack has rarely been studied directly in the HIN model. Moreover, no study has focused on how to enhance shilling attacks against HIN recommend system by using the high-level semantic information. This work analyzes the relationship between the high-level semantic information and the attacking effects in HIN recommend system. This work proves that attack results are proportional to the high-level semantic information. Therefore, we propose a heuristic attack method based on high-level semantic information, named Semantic Shilling Attack (SSA) on a HIN recommend system (HERec). This method injects a specific score into each selected item related to the target in semantics. It ensures transmitting the misleading information towards target items and normal users, and attempts to interfere with the effect of the recommend system. The experiment is dependent on two real-world datasets, and proves that the attacking effect is positively correlate with the number of meta-paths. The result shows that our method is more effective when compared with existing baseline algorithms.