Hossain, HumairaKayum, Syed IrfanPaul, ArjaRohan, Alim AldinTasnim, NafisaHossain, Muhammad Iqbal2026-08-222026-08-222021-01-01H. Hossain, S. I. Kayum, A. Paul, A. A. Rohan, N. Tasnim and M. I. Hossain, "Malware Detection Using Neural Networks," 2021 5th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 2021, pp. 1-6, doi: 10.1109/EICT54103.2021.9733457.97816654090632-s2.0-85127624274https://hdl.handle.net/10361/29408The Internet has a large amount of data and files that need to be analyzed for possible malicious purposes since the number of malicious applications is growing at a rapid rate. Researchers have tried to detect malware using neural networks and deep learning methods which we have discussed in the related works section. However, in this paper we are analyzing and contrasting performance of three different neural network models which are: Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM) Network, and Gated Recurrent Unit (GRU) for malware detection. Besides, we used secondary dataset in our research. From the aforementioned models, CNN is performing better giving 83 percent accuracy in recognizing malware whereas LSTM and GRU gives 65 percent and 76 percent respectively.6 Pagesen-USDeep learningAnalytical modelsLogic gatesInformation and communication technologyConvolutional neural networkLong-short term memory networkGated recurrent unitSecondary dataMalware (Computer software).Computer security.Conference Proceeding10.1109/EICT54103.2021.9733457