Due to proliferation of online posts and rise in the active social media users, fraudulent activities related with spam messages have taken a spike drift. Spam is an activity by which hackers use electronic messaging system to unsolicited messages in mass content to unknown users. It can be also taken as one of the major attraction of attackers in the form of short message service (SMS) messages. Spam messages can be categorized in different categories such as business opportunity spam, trending topic spam, banking services spam etc. These problems can be tackled by confirming to the actions taken by users towards these messages. There is an urgent explicit need of practical medium in order to assist the users against these spam messages. This paper proposes a novel SMS spam filtering model based on Danger theory of Artificial Immune System (AIS). Several feature extraction and selection techniques have been applied for optimizing the algorithm and claiming an admissible accuracy. This paper uses a biologically inspired algorithm named Krill herd Optimization (KHO) for the task of feature selection and various optimization functions like Quing function, Sumsquare function, Levy function etc. are applied for enhancing its performance. The Dendritic Cell Algorithm (DCA) is also incorporated with KHA as an added advantage towards achieving efficiency. Comparative results between Dendritic Cell Algorithm (DCA) with KHA and other spam filtering models have been shown in comparison with several state-of-the-art machine learning classifiers. The algorithms have been experimented by using varied optimization functions illustrated using visualization tools and results have been validated in the paper. The obtained results demonstrate an admissible accuracy of 96% that is calculated using different information retrieval metrics using recall, F-measure and precision.

由于在线帖子的激增和活跃的社交媒体用户的增加，与垃圾邮件相关的欺诈活动出现了激增的趋势。垃圾邮件是黑客使用电子消息系统向未知用户发送大量内容中未经请求的消息的活动。它也可以作为短消息服务 (SMS) 消息形式的攻击者的主要吸引力之一。垃圾邮件可以分为不同的类别，例如商业机会垃圾邮件、趋势主题垃圾邮件、银行服务垃圾邮件等。这些问题可以通过确认用户对这些邮件采取的操作来解决。迫切需要实用的媒体来帮助用户抵御这些垃圾邮件。本文提出了一种基于人工免疫系统(AIS)危险理论的新型垃圾短信过滤模型。已经应用了几种特征提取和选择技术来优化算法并保证可接受的精度。本文使用名为 Krill herd Optimization (KHO) 的生物学启发算法进行特征选择任务，并应用了 Quing 函数、Sumsquare 函数、Levy 函数等各种优化函数来提高其性能。树突细胞算法 (DCA) 也与 KHA 相结合，作为提高效率的附加优势。树突细胞算法 (DCA) 与 KHA 和其他垃圾邮件过滤模型之间的比较结果已与几种最先进的机器学习分类器进行了比较。这些算法已经通过使用可视化工具说明的各种优化函数进行了实验，结果在论文中得到了验证。获得的结果证明了 96% 的可接受准确率，这是使用召回率使用不同的信息检索指标计算得出的，F - 测量和精度。