Support vector machine (SVM) is a renowned machine learning technique, which has been successfully applied to solve many practical pattern classification problems. One of the difficulties in successful implementation of SVM is its different parameters (i.e., kernel parameter(s), penalty parameter ( C ) and the features available in the dataset), which should be well adjusted during the training process. In this paper, a new approach called smart adaptive particle swarm optimization–support vector machine (SAPSO–SVM) is developed to adapt the parameters of optimization algorithm (i.e., inertia weight and acceleration coefficients) to the latest changes in the search space, so that each particle explicitly explores the search space based on the latest changes made to Personal best, Global best and other particle locations. In this algorithm, using the changes in Personal best and Global best at each stage of execution, the new evolution factor values are designated and the interference of the intervals of inertia weight is eradicated. Then, the states of each particle (i.e., convergence, exploitation, exploration, jumping-out) at each stage of administration, based on the interval weights, are specified accurately. By fine tuning the parameters of SAPSO, this algorithm can acquire the best optimal responses for SVM parameters. The results obtained from the SAPSO–SVM method demonstrate the superiority of this method in four different measures (i.e., sensitivity, specificity, precision, accuracy) in comparison with the other three similar ones. Finally, the top 20 features of Android botnets are somehow introduced by the proposed approach and three other approaches; firstly, these features are not encrypted by Android botnets, and secondly, are selected based on the best results.

中文翻译：

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一种智能自适应粒子群优化——支持向量机：安卓僵尸网络检测应用

支持向量机（SVM）是一种著名的机器学习技术，已成功应用于解决许多实际的模式分类问题。SVM 成功实现的难点之一是其不同的参数（即内核参数、惩罚参数 (C) 和数据集中可用的特征），在训练过程中应该很好地调整这些参数。在本文中，开发了一种称为智能自适应粒子群优化-支持向量机（SAPSO-SVM）的新方法，使优化算法的参数（即惯性权重和加速度系数）适应搜索空间的最新变化，因此每个粒子都基于对个人最佳、全局最佳和其他粒子位置的最新更改明确探索搜索空间。在这个算法中，利用每个执行阶段个人最好和全局最好的变化，指定新的进化因子值，消除惯性权重区间的干扰。然后，基于区间权重，精确指定每个粒子在每个给药阶段的状态（即收敛、开发、探索、跳出）。通过对 SAPSO 的参数进行微调，该算法可以获得 SVM 参数的最佳响应。与其他三个类似的方法相比，从 SAPSO-SVM 方法获得的结果证明了该方法在四种不同的测量（即灵敏度、特异性、精密度、准确度）方面的优越性。最后，所提出的方法和其他三种方法以某种方式介绍了 Android 僵尸网络的前 20 个特征；首先，