Image Based Malware Detection and Classification Approach Using Fine Tuned Neural Network

Abstract

Malware is a malicious software that can be outlined to affect the hardware or newlinesoftware that sticks on to the Automated process. It is a stealthy piece of code that completely newlineor partially infects the system without the human knowledge. In general, it causes damage newlineor disrupt to the devices, programs and files which can or cannot be replaceable at times. It newlinemay sometime cause a serious issue to the end user. When a malware affects the system, newlineuser may experience unauthorized access or data compromising or data loss or all. By the newlinetime, the user gets to know about the malicious attack, the software or hardware of the system newlinegets corrupted and most of the information gets lost. Normally the hacker may revert the newlinemalware once their expectations is achieved. Malware tricks the normal usage of the system newlineby acquiring the system through phishing sites, infected file, infected USB drives, malicious newlinewebsite, malicious emails and vulnerable ads. Malicious codes can manifest in an extensive newlinerange of various families such as Loader, BOT, RAT, STEALER, Keylogger, Rootkit, newlinecommand and control family and Wiper. The researchers have encountered significant newlinedifficulty in the process of identifying and classifying the malware. Indeed, the most newlinedemanding aspect of malware analysis has been the identification and classification of newlineimage-based malware newline