compiled and packaged in a format suitable for machine learning model input. Examples of commonly used features include packets/second, connections/second, bytes/second, and packet header fields. This preprocessing of network traffic is a necessary time-consuming and resource-intensive step before applying an ML model to detect malicious flows. Selecting the appropriate features is critical to the model's performance and malicious flow detection accuracy. The set of essential features varies based on the network and traffic type that needs to be analyzed. Since it is hard to determine the essential features intuitively, it is common to start with a large number of features and select a smaller subset based on the analysis of each feature's importance using specialized machine learning models or other approaches. Once the desired features are determined, the data corresponding to those features are extracted from the network packet capture (pcap) and converted to an appropriate format for input into the desired machine learning model. Several research efforts in recent years have consisted of performing this feature extraction process to input the selected data into CNNs. However, an attractive trait of CNNs is that they can analyze raw data and identify noteworthy features quickly and efficiently. Therefore, CNNs are suited to object identification in images, with little or no image modification. The essential visual features are automatically determined and used by a CNN. Therefore, the computationally expensive task of identifying essential features and then extracting them during the data preprocessing stage of analysis may be unnecessary if a CNN is used to analyze the data. Several recent research efforts have focused on using One-Dimensional CNNs (1D-CNNs) to analyze network packets as a single one-dimensional array of bytes. While these works are shown to detect malicious traffic accurately, the requirements of these techniques are too great. Their machine learning models are often complex. These models feature several convolution layers, each capable of being used as a stand-alone CNN similarly to the first layer in our model, which we will discuss in Section 3. They also have several large densely-connected layers, where every node in the layer is connected to every node in the layers before and after it. Moreover, their preprocessing methods are extensive, generally involving several conversions, calculations, and filters. Many of them also require a network flow to be completed before its features are extracted and analyzed. Owing to the high computational requirements, the current methods of detecting malicious traffic are more suitable for offline or post mortem analysis. Consequently, all of the analyses presented, which are based on post mortem analysis, are unsuitable for on-the-fly malicious flow detection. The problem we address in this paper is to develop images from network traffic flows with very low overhead and apply deep learning (DL) models for image processing to detect anomalous flows. Specifically, we develop a simple method to convert raw IP (Internet Protocol) packet data directly into bitmap files and analyze with computer vision models such as the 2D-CNN without any significant preprocessing of the raw data. We evaluate our method's effectiveness on three different network traffic datasets, including the popular USTC-TFC2016 dataset [1] used by numerous other research teams in this field of study, and we compare our results with those presented in the literature. Our method has several advantages compared to 1D-CNN for raw packet analysis and 2D-CNN analysis based on extracted features or entire pcap file analysis. No extraneous analysis is required to identify what derived features are useful since all or a specified number of bytes of the packet are used for analysis. The 2D-CNN model then performs the work of identifying the useful features during model training; the trained model rapidly identifies anomalous traffic based on the chosen features of raw data.