(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
With the rise in use of internet ,also have grown the security concerns associated with it. The most common threats that we encounter on the internet are Malicious URLs and Malware.Traditional solutions for combating these threats, are to build a databases of known sources of trouble or/and using filters to restrict access to resources. This is however not dynamic enough to detect attacks of smart Cyber-criminals. Techniques such as type-squatting, domain-squatting , code obfuscation,etc are hard to identify using conventional methods. Thus to stay one step ahead in the war against Cyber-crime we need to use Machine and Deep learning enabled methods in our defense equipment . The tool Malware and Malicious URL Classifier (MAMUC) is a unique tool which has 3 features. The first feature helps us to identify if a given URL is malicious or benign. This classifier runs on a novel Malben dateset. The second feature is used to covert a malware sample from byte-code to its corresponding malware image. This called as malware visualization,which is a strategy used in static malware inspection. The third feature of the tool is a malware classifier. Once malware is converted to an image ,its analysis is simplified to a case of image classification. This part identifies to which family an input malware is most closely associated with. Once the malware family is identified it is easier to deal with it. The novelty we have employed in this classifier is the use of Dual channel CNN(DCCNN) algorithm for malware classification