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With the development of cloud storage technology, data storage security has become increasingly serious. Aiming at the problem that existing attributebased encryption schemes do not consider hierarchical authorities and the weight of attribute. A hierarchical authority based weighted attribute encryption scheme is proposed. This scheme will introduce hierarchical authorities and the weight of attribute into the encryption scheme, so that the authorities have a hierarchical relationship and different attributes have different importance. At the same time, the introduction of the concept of weight makes this scheme more flexible in the cloud storage environment and enables fine-grained access control. In addition, this scheme implements an online/offline encryption mechanism to improve the security of stored data. Security proof and performance analysis show that the scheme is safe and effective, and it can resist collusion attacks by many malicious users and authorization centers. It is more suitable for cloud storage environments than other schemes.

Intrusion detection is a hot topic in network security. This paper proposes an intrusion detection method based on improved artificial bee colony algorithm with elite-guided search equations (IABC elite) and Backprogation (BP) neural networks. The IABC elite algorithm is based on the depth first search framework and the elite-guided search equations, which enhance the exploitation ability of artificial bee colony algorithm and accelerate the convergence. The IABC elite algorithm is used to optimize the initial weight and threshold value of the BP neural networks, avoiding the BP neural networks falling into a local optimum during the training process and improving the training speed. In this paper, the BP neural networks optimized by IABC elite algorithm is applied to intrusion detection. The simulation on the NSL-KDD dataset shows that the intrusion detection system based on the IABC elite algorithm and the BP neural networks has good classification and high intrusion detection ability.

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One of the important successes of optical fiber sensor established for the security system is the detection and the recognition of any type of events. The performance parameters (event recognition, event detection position, and time of detection) are unavoidable and describe the validity of any perimeter detection system. An event recognition is any signal detected within the protected area, and it is related to a non-intrusion event and an intrusion event. To achieve the detection and the recognition events at the real time, an effective two-level vibration recognition method and a technique are proposed and presented in this article. The signal characteristics (short-term energy and short-time overthreshold) have been used and compared to the dynamic threshold to judge the type of event. Then the extraction of the power distribution features on the frequency domain through power spectral estimation on the suspected intrusion signal samples is carried out and finally combined with the time-domain characteristics as feature vector through Support Vector Machine to determine the efficiency and effectiveness of the proposed vibration recognition method. The experimental simulation results show that the proposed method is effective and reliable. With collected data, it can detect and recognize the type of event in real time.

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