Biblio
The purpose of this paper is to improve the safety of chaotic image encryption algorithm. Firstly, to achieve this goal, it put forward two improved chaotic system logistic and henon, which covered an promoted henon chaotic system with better probability density, and an 2-dimension logistic chaotic system with high Lyapunov exponents. Secondly, the chaotic key stream was generated by the new 2D logistic chaotic system and optimized henon mapping, which mixed in dynamic proportions. The conducted sequence has better randomness and higher safety for image cryptosystem. Thirdly, we proposed algorithm takes advantage of the compounded chaotic system Simulation experiment results and security analysis showed that the proposed scheme was more effective and secure. It can resist various typical attacks, has high security, satisfies the requirements of image encryption theoretical.
Quantifying vulnerability and security levels for smart grid diversified link of networks have been a challenging task for a long period of time. Security experts and network administrators used to act based on their proficiencies and practices to mitigate network attacks rather than objective metrics and models. This paper uses the Markov Chain Model [1] to evaluate quantitatively the vulnerabilities associated to the 802.11 Wi-Fi network in a smart grid. Administrator can now assess the level of severity of potential attacks based on determining the probability density of the successive states and thus, providing the corresponding security measures. This model is based on the observed vulnerabilities provided by the Common Vulnerabilities and Exposures (CVE) database explored by MITRE [2] to calculate the Markov processes (states) transitions probabilities and thus, deducing the vulnerability level of the entire attack paths in an attack graph. Cumulative probabilities referring to high vulnerability level in a specific attack path will lead the system administrator to apply appropriate security measures a priori to potential attacks occurrence.
The paper considers an issues of protecting data from unauthorized access by users' authentication through keystroke dynamics. It proposes to use keyboard pressure parameters in combination with time characteristics of keystrokes to identify a user. The authors designed a keyboard with special sensors that allow recording complementary parameters. The paper presents an estimation of the information value for these new characteristics and error probabilities of users' identification based on the perceptron algorithms, Bayes' rule and quadratic form networks. The best result is the following: 20 users are identified and the error rate is 0.6%.