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Malware distribution to the victim network is commonly performed through fileattachments in phishing email or from the internet, when the victim interactswith the source of infection. To detect and prevent the malware distribution inthe victim machine, the existing end device security applications may leveragetechniques such as signature or anomaly-based, machine learning techniques. Thewell-known file formats Portable Executable (PE) for Windows and Executable andLinkable Format (ELF) for Linux based operating system are used for malwareanalysis, and the malware detection capabilities of these files has been welladvanced for real-time detection. But the malware payload hiding in multimediausing steganography detection has been a challenge for enterprises, as theseare rarely seen and usually act as a stager in sophisticated attacks. In thisarticle, to our knowledge, we are the first to try to address the knowledge gapbetween the current progress in image steganography and steganalysis academicresearch focusing on data hiding and the review of the stegomalware (malwarepayload hiding in images) targeting enterprises with cyberattacks currentstatus. We present the stegomalware history, generation tools, file formatspecification description. Based on our findings, we perform the detail reviewof the image steganography techniques including the recent GenerativeAdversarial Networks (GAN) based models and the image steganalysis methodsincluding the Deep Learning(DL) models for hiding data detection. Additionally,the stegomalware detection framework for enterprise is proposed for anomalybased stegomalware detection emphasizing the architecture details for differentnetwork environments. Finally, the research opportunities and challenges instegomalware generation and detection are also presented.

Stegomalware: A Systematic Survey of MalwareHiding and Detection in Images, Machine LearningModels and Research Challenges