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Abstract：
Neural network creates a neuron-based network similar to the human nervous system to solve classification problems efficiently. The smishing problem is a binary classification problem in which attackers target smartphone users through text messages. As smishing is a remarkable cybersecurity issue that is troubling researchers and smartphone users these days. Addressing this security issue using the most efficient algorithm is the need of the hour. This manuscript presented an algorithm for the model proposed by authors in \'Smishing Detector\' model and implemented it using Neural Network. The result obtained proves that the neural network is much efficient in detecting smishing problem. Neural Network outperformed other machine learning algorithms with a difference of 1.11%. Neural Network performed with the final accuracy of 97.40%. In this paper, system extracted the most efficient features of smishing SMS (Short Message Service) using the Neural Network. This manuscript also reported the accuracy shown by the system for each feature selected and implemented. It is evident from the implementation that each feature selected is most effective in smishing detection and URL (Uniform Resource Locator) feature is the most effective feature with an accuracy of 94%.
摘要：
神经网络创建类似于人类神经系统的基于神经元的网络，以有效地解决分类问题。smishing问题是一个二元分类问题，攻击者通过短信将智能手机用户作为目标。由于smishing是一个显着的网络安全问题，这些天困扰研究人员和智能手机用户。使用最有效的算法解决这个安全问题是小时的需要。该手稿提出了作者在“SmishingDetector”模型中提出的模型的算法，并使用神经网络实现了该算法。所获得的结果证明了神经网络在检测模糊问题方面的有效性。神经网络优于其他机器学习算法，差异为1.11%。神经网络的最终准确率为97.40%。在本文中,系统使用神经网络提取了smishingSMS（短消息服务）的最有效特征。该手稿还报告了系统所选择和实施的每个功能的准确性。从实现中可以明显看出，所选择的每个特征在smishing检测中都是最有效的，而URL（统一资源定位符）特征是最有效的特征，准确率为94％。
