Assembly language analysis to find suspicious word for malware detection To generate a deep learning model to read assembly language and detect malicious code, we can use a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The overall architecture of the model can be broken down into the following steps: Preprocessing: The assembly code will need to be preprocessed to convert it into a format that can be input into a neural network. This may include steps such as tokenization, one-hot encoding, and padding.
Feature Extraction: The preprocessed code will be input into a CNN to extract features such as opcode sequences, function calls, and control flow patterns. The CNN can consist of several convolutional layers followed by pooling layers to downsample the input and capture important features.
Sequence Modeling: The features extracted from the CNN will be input into an RNN to model the sequential dependencies in the code. The RNN can consist of several recurrent layers such as Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRUs).
Output: The final output of the model will be a binary classification indicating whether the input assembly code is malicious or not. The assembly code is first preprocessed to convert it into a sequence of tokens, which are then embedded into a dense vector space using an embedding layer. The resulting vector sequence is then fed through a series of convolutional and pooling layers to extract features, which are then modeled by a LSTM layer to capture the sequential dependencies in the input code. Finally, the output of the LSTM layer is fed through a fully connected output layer with sigmoid activation for binary classification. The model is trained on a set of preprocessed assembly code and corresponding labels, and evaluated on a separate test set.