Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm

• Proposed an intelligent framework for multimodal textual spam filtering for mobile devices.
• A novel hybrid machine learning approach and fusion with dendritic cell algorithm.
• Analyzed the discrimination of a rich set of content and style related features that can be easily extracted from received messages.
• Rigorously evaluated and benchmarked models on five email and SMS datasets using a variety of performance measures.
• Reduce complexity for feature extraction while preserving good performance.

With the continual growth of mobile devices, they become a universal portable platform for effective business and personal communication. They enable a plethora of textual communication modes including electronic mails, instant messaging, and short messaging services. A downside of such great technology is the alarming rate of spam messages that are not only annoying to end-users but raises security concerns as well. This paper presents an intelligent framework for filtering multimodal textual communication including emails and short messages. We explore a novel methodology for information fusion inspired by the human immune system and hybrid approaches of machines learning. We study a number of methods to extract and select more relevant features to reduce the complexity of the proposed model to suite mobile applications while preserving good performance. The proposed framework is intensively evaluated on a number of benchmark datasets with remarkable results achieved.