: :In recent years, cyber-attacks against individuals, businesses, and organizations have increased. Cyber criminals are always looking
for effective vectors to deliver malware to victims in order to launch anattack. Images are used on a daily basis by millions of people around the
world, and most users consider images to be safe for use; however, some types of images can contain a malicious payload and perform harmful
actions. JPEG is the most popular image format, primarily due to its lossy compression. It is used by almost everyone, from individuals to large
organizations, and can be found on almost every device (on digital cameras and smartphones, websites, social media, etc.). Because of their
harmless reputation, massive use, and high potential for misuse, JPEG images are used by cyber criminals as an attack vector. While machine
learning methods have been shown to be effective at detecting known and unknown malware in various domains, to the best of our knowledge,
machine learning methods have not been used particularly for the detection of malicious JPEG images. In this paper, we present MalJPEG, the
first machine learning- based solution tailored specifically at the efficient detection of unknown malicious JPEG images. MalJPEG statically
extracts 10 simple yet discriminative features from the JPEG file structure and leverages them with a machine learning classifier, in order to
discriminate between benign and malicious JPEG images. We evaluated MalJPEG extensively on a real-world representative collection of
156,818 images which contains 155,013 (98.85%) benign and 1,805 (1.15%) malicious images. The results show that MalJPEG, when used with
the LightGBM classifier, demonstrates the highest detection capabilities, with an area under the receiver operating characteristic curve (AUC)
of 0.997, true positive rate (TPR) of 0.951, and a very low false positive rate (FPR) of 0.004.
INDEX TERMS JPEG, image, malware, detection, machine learning, features.