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Malware detection using machine and deep learning algorithms for computer devices
(Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2022) Altaiy, Mohammed Thair Abdulsattar; Uçan, Osman Nuri
Malicious software, sometimes known as malware, poses a serious threat to computer security. Due to the quantity and diversity of variations, traditional security measures are inadequate, and there are a lot of places on the internet has malware infection such as Computer Virus, internet worms, and trojan horses are examples of malware. Despite the fact that malware multiformity and obstruction create it difficult to discover files, during runtime, dynamic malware-binary analysis is performed. Provides way to identify & protect towards the attack posed with malware software in this paper, we offer an approach for automated malware behavior analysis using machine learning. Using such technology, for a short time ago there is a new undiscovered malware families for the classes (classification) that have been found, as well as new malware classes with a similar behavior, have been uncovered (clustering) may be dynamically recognized. Both clustering and classification imply incremental analysis, which may monitor the daily activities of thousands of malwares and analyses their behavior. The innovative research ensures that new malware types are properly identified and discriminated against while greatly reducing the overhead of existing test procedures.