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Many smart mobile devices, including smartphones, smart televisions, smart watches, and smart vacuums, have been powered by Android devices. Therefore, mobile devices have become the prime target for malware attacks due to their rapid development and utilization. Many security practitioners have adopted different approaches to detect malware. However, its attacks continuously evolve and spread, and the number of attacks is still increasing. Hence, it is important to detect Android malware since it could expose a great threat to the users. However, in machine learning intelligence detection, too many insignificant features will decrease the percentage of the detection’s accuracy. Therefore, there is a need to discover the significant features in a minimal amount to assist with machine learning detection. Consequently, this study proposes the Pearson correlation coefficient (PMCC), a coefficient that measures the linear relationship between all features. Afterwards, this study adopts the heatmap method to visualize the PMCC value in the color of the heat version. For machine learning classification algorithms, we used a type of fuzzy logic called lattice reasoning. This experiment used real 3799 Android samples with 217 features and achieved the best accuracy rate of detection of more than 98% by using Unordered Fuzzy Rule Induction (FURIA).

The RADG (Reaction Automata Direct Graph) cryptosystem is the automata direct graph and reaction states combination. The classical RADG does not require key exchange (keyless), or agreement between users just the design of RADG, which is static. The RADG algorithm with keys has two agreements between users, one is on the keys, and other is a design of RADG. The RADG design depends on states and transitions between them, since transitions between states are static transitions, or dynamic transitions have agreement between users to determine the type of state (Jump state, Reaction state) of RADG algorithm with keys, and the transition between states must cover each states scenario of RADG design .This article presents algorithm called (Auto- Transition Function (ATF)), which merge properties of RADG algorithm with chaotic system to obtain on transitions between states are automatic. The parameters of ATF are chaotic initial value, parameter of chaotic function, and characteristics of RADG, then ATF is an auto creation of transitions among all states in RADG, and it satisfies each scenario of RADG design.