Boosting Malware Detection with AlexNet and Optimized Neural Networks Using the Grasshopper Algorithm
DOI:
https://doi.org/10.31185/wjcms.303Keywords:
Malware detection, convolutional neural network, multilayer perceptron, grasshopper algorithmAbstract
That risk is compounded as more critical infrastructure and systems are being managed by computers in general, connected over the Internet. To combat such nefarious software that can steal data and do a number of other privatively outcomes, you need to be very vigilant and also train all our artificial intelligent tools not just to find the malware per se but all the countless other ways in which meddlers might find their way into your computer or set off some enormously disruptive chain reaction (or series thereof). In this research, it is offering a potent new technique by integration of innovative neural network method with conventional artificial intelligence tool known as "multilayer perceptron (MLP)". For the detection of 25 distinct categories of malwares it is obtained 99.84% accuracy rate in classification through in this hybrid system.
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