Formulas and Algorithms Used in Opcode Analysis and Threat Assessment

Feature Extraction: Utilize mathematical formulations for converting opcodes into numerical features. For example, one-hot encoding can be represented as a vector transformation where each opcode is transformed into a binary vector with a '1' in the position corresponding to the opcode and '0's elsewhere.
Sequence Analysis: For sequential data like opcodes, employ algorithms such as Hidden Markov Models (HMM) or Recurrent Neural Networks (RNNs). The HMM, for instance, uses transition probabilities (a_ij) between states (opcodes) and emission probabilities (b_j(k)) to model the sequence.
Threat Assessment: Implement classification algorithms, where the decision function for a simple linear classifier could be $y=wTx+b$ , where $w$ is the weight vector, $x$ is the feature vector, and $b$ is the bias. The sign of $y$ determines the classification (malicious or benign).

Last updated 1 year ago

Feature Extraction: Utilize mathematical formulations for converting opcodes into numerical features. For example, one-hot encoding can be represented as a vector transformation where each opcode is transformed into a binary vector with a '1' in the position corresponding to the opcode and '0's elsewhere.
Sequence Analysis: For sequential data like opcodes, employ algorithms such as Hidden Markov Models (HMM) or Recurrent Neural Networks (RNNs). The HMM, for instance, uses transition probabilities (a_ij) between states (opcodes) and emission probabilities (b_j(k)) to model the sequence.
Threat Assessment: Implement classification algorithms, where the decision function for a simple linear classifier could be $y=wTx+b$ , where $w$ is the weight vector, $x$ is the feature vector, and $b$ is the bias. The sign of $y$ determines the classification (malicious or benign).

Last updated 1 year ago