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In recent years, extensive research has been conducted in the field of detecting Android botnet, but most of the approaches introduced can provide a good answer to a limited number of these datasets. Now the question is how to introduce an approach that offers a high detection rate on various Android botnets. To answer this question, we propose a Smart Self-Adaptive Learning Based Particle Swarm Optimization Support Vector Machine (SSLPSO-SVM) approach to identify Android botnet with high accuracy. The SSLPSO algorithm simultaneously uses five different strategies for scanning search space, which are based on the PSO algorithm. Instead of choosing strategies using the Roulette Wheel Selection method, SSLPSO uses a novel method called Smart Selection Strategies (SSS). This method determines the frequency of implementation and the priority of each strategy based on the number of changes …