Stegomalware: A Comprehensive Survey on Creation and Detection of Malware in Images

Cyberattacks are becoming more sophisticated as technology progresses. Since fraudsters have mastered the art of hiding dangerous code in digital media, photos are becoming a more and more common choice. Stegomalware is a type of cyberattack that combines steganography principles with malevolent intent, enabling attackers to conceal dangerous code within seemingly benign images. The primary purpose of these attacks is to bypass traditional defenses and exploit people’s trust in seemingly benign image files. Attackers can evade detection systems such as intrusion detection systems, network firewalls, and antivirus software by hiding harmful payloads inside images. We provide a thorough explanation of the development process and historical occurrences of stegomalware in this work. Machine learning models may be trained to recognize patterns and traits typical of stegomalware in images by utilizing artificial intelligence. These algorithms are capable of reliably identifying concealed harmful payloads by analyzing massive datasets of benign and malicious photos. In this work, we examined a few machine learning-based methods that aid in the identification of stegomalware. Cybersecurity professionals may create more resilient defenses and proactive plans to combat future assaults by knowing the traits of stegomalware.