With society's increasing dependence on the Internet, more private data is transmitted through networks every day. Unfortunately, this traffic is susceptible to a wide range of threats and vulnerabilities, including phishing attacks that trick users into compromising their systems or revealing sensitive personal information. In this research, we proposed a deep learning approach to detect malware using data collected from a web crawler that systematically sent requests to benign and malicious websites on the Internet. After applying procedures to segment the network flows and extract features, we used these extracted high-level network traffic features to train a deep neural network to recognize benign and malicious flows. Finally, we evaluated our malware detection approach against various metrics, including precision, recall, and f1 score. The achieved f1 score was 0.924, validating the overall performance of the detection scheme.