#Functionflip malware software

Malware is designed for a specific target, often attempting to camouflage itself in another way, with intentions such as file encryption, ransom, preventing a system from working, gaining unauthorized access to a network, data theft, or sabotage. Malicious software, commonly known as malware, is intentionally designed to damage computer systems and exploit security weaknesses.

#Functionflip malware software

This offers many opportunities for malicious software developers. Due to the fierce competition among the manufacturers and rapid product development, many products are released to the market with security weaknesses. Motivated by market demand, the manufacturers have started to produce devices with attractive features ignoring the security weakness caused by offering such features. The frequency of use for many devices has increased many folds, including mobile phones, laptops, webcams, etc. Recently our usage of technical gadgets has increased due to the aggressive invasion of technology in our daily life. The results obtained by the classifier demonstrate accuracy up to 98%, which is quite satisfactory. This study uses five different deep CNN model for malware family detection. The second component of the system create the augmented version of the images, and the last part builds a classification model. In the first component, the collected malware samples are converted into binary file to 3-channel images using the windowing technique. The main contributions of the article consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a CNN model. This article aims to provide an image augmentation enhanced deep convolutional neural network (CNN) models for detecting malware families in a metamorphic malware environment. In the solution to this problem, we created variants of the images by applying data augmentation methods. Attackers make different variants of the same software because they modify the contents of the malware. To overcome this method that attackers use to camouflage malware, we have created three-channel image files of malicious software. The most important feature of this type of malware is that they change their contents using their mutation engines to create another hash representation of the executable file as they propagate from one computer to another. It is quite impossible for anti-virus applications using traditional signature-based methods to detect this type of malware because they have different characteristics on each contaminated computer. Both WannaCry and Petya caused operational processes inoperable by targeting critical infrastructure. Some of the most prominent examples of ransomware attacks in history are WannaCry and Petya, which impacted companies’ finances throughout the globe.

Cyber-attacks can hit an organization hard by causing several damages such as data breach, financial loss, and reputation loss. Due to advancements in malware competencies, cyber-attacks have been broadly observed in the digital world.