Traditional deception-based cyber defenses (DCD) often adopt the static deployment policy that places the deception resources in some fixed positions in the target network. Unfortunately, the effectiveness of these deception resources has been greatly restricted by the static deployment policy, which also causes the deployed deception resources to be easily identified and bypassed by attackers. Moreover, the existing studies on dynamic deployment policy, which make many strict assumptions and constraints, are too idealistic to be practical. To overcome this limitation, an intelligent deployment policy used to dynamically adjust the locations of these deception resources according to the network security state is developed. Starting with formulating the problem of deception resources deployment, we then model the attacker-defender scenario and the attacker's strategy. Next, the preliminary screening method that can derive the effective deployment locations of deception resources based on threat penetration graph (TPG) is proposed. Afterward, we construct the model for finding the optimal policy to deploy the deception resources using reinforcement learning and design the Q-Learning training algorithm with model-free. Finally, we use the real-world network environment for our experiments and conduct in-depth comparisons with state-of-the-art methods. Our evaluations on a large number of attacks show that our method has a high defense success probability of nearly 80%, which is more efficient than existing schemes.

Private data in healthcare system require confidentiality protection while transmitting. Steganography is the art of concealing data into a cover media for conveying messages confidentially. In this paper, we propose a steganographic method which can provide private data in medical system with very secure protection. In our method, a cover image is first mapped into a 1D pixels sequence by Hilbert filling curve and then divided into non-overlapping embedding units with three consecutive pixels. We use adaptive pixel pair match (APPM) method to embed digits in the pixel value differences (PVD) of the three pixels and the base of embedded digits is dependent on the differences among the three pixels. By solving an optimization problem, minimal distortion of the pixel ternaries caused by data embedding can be obtained. The experimental results show our method is more suitable to privacy protection of healthcare system than prior steganographic works.

With the wide application of low bit-rate codecs in speech communication systems, low bit-rate speech streams have become new cover media of great potential for steganography. In this paper, through analyzing the pitch period prediction process in G.729 codec, the pitch parameter of the second speech subframe is found suitable for performing embedding. Then a novel triple-layer steganography method is proposed for low bit-rate speech streams. In this method, modification directions (adding or subtracting one) of the pitch parameter are selected adaptively in order to achieve a high embedding efficiency. Based on the "Hamming+ 1" scheme, we use the matrix encoding method twice to increase the hiding capacity. Experimental results show that while keeping a good perceived quality of the synthetic speech, the proposed method has a good real-time performance and a satisfactory steganography security.

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This paper proposes an optimized LSB matching steganography based on Fisher Information. The embedding algorithm is designed to solve the optimization problem, in which Fisher information is the objective function and embedding transferring probabilities are variables to be optimized. Fisher information is the quadratic function of the embedding transferring probabilities, and the coefficients of quadratic term are determined by the joint probability distribution of cover elements. By modeling the groups of elements in a cover image as Gaussian mixture model, the joint probability distribution of cover elements for each cover image is obtained by estimating the parameters of Gaussian mixture distribution. For each sub-Gaussian distribution in Gaussian mixture distribution, the quadratic term coefficients of Fisher information are calculated, and the optimized embedding transferring probabilities are solved by quadratic programming. By maximum posteriori probability principle, cover pixels are classified as the categories corresponding to sub-Gaussian distributions. At last, in order to embed message bits, pixels chose to add or subtract one according to the optimized transferring probabilities of the category. The experiments show that the security performance of this new algorithm is better than the existing LSB matching.