Image spam classificationusing neural network

Mohammad Mozammel Hoque Chowdhury, Junbin Gao, Morshed U. Chowdhury

Research output: Book chapter/Published conference paperConference paper

5 Citations (Scopus)


Spam, an unsolicited or unwanted email, has traditionally been and continues to be one of the most challenging problems for cyber security. Image-based spam or image spam is a recent trick developed by the spammers which embeds malicious image with the text message in a binary format. Spammers use image based spamming with the intention of escaping the text based spam filters. On the way to detect image spam, several techniques have been developed. However, these techniques are vulnerable to most recent image spam and exhibit lack of competence. With a view to diminish the limitations of the existing solutions, this paper proposes a robust and efficient approach for image spam detection using machine learning algorithm. Our proposed system analyzes the file features together with the visual features of the embedded image. These features are used to train a classifier based on back propagation neural networks to detect the email as spam or legitimate one. Experimental evaluation demonstrates the effectiveness of the proposed system comparable to the existing models for image spam classification.
Original languageEnglish
Title of host publicationSecurity and Privacy in Communication Networks
Subtitle of host publication11th EAI International Conference, SecureComm 2015
EditorsBhavani Thuraisingham, Xiao-Feng Wang, Vinod Yegneswaran
Place of PublicationSwitzerland
Number of pages11
ISBN (Electronic)9783319288642
ISBN (Print)9783319288642
Publication statusPublished - 2015
EventInternational Conference Security and Privacy in Communication Networks: SecureComm 2015 - Wyndham Garden Dallas North Hotel, Dallas, United States
Duration: 26 Oct 201529 Oct 2015


ConferenceInternational Conference Security and Privacy in Communication Networks
CountryUnited States
OtherSecureComm seeks high-quality research contributions in the form of well-developed papers. Topics of interest encompass research advances in ALL areas of secure communications and networking. Topics in other areas (e.g., formal methods, database security, secure software, theoretical cryptography) will be considered only if a clear connection to private or secure communication/networking is demonstrated.
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    Chowdhury, M. M. H., Gao, J., & Chowdhury, M. U. (2015). Image spam classificationusing neural network. In B. Thuraisingham, X-F. Wang, & V. Yegneswaran (Eds.), Security and Privacy in Communication Networks: 11th EAI International Conference, SecureComm 2015 (pp. 622-632). Springer.