Fraud Detection Artificial Intelligence Model

Fraud used to be an exception; now it is generalization; and it is more common in the digital space. Artificial Intelligence is capable of detecting fraud at the initial stage itself and would nip the fraud in the bud.

In the digital space, fraud starts from domain registration. Here is an example for the real and fake registered domain.

Valid Domain: https://www.whois.com/whois/centene.com
Fake Domain: https://www.whois.com/whois/centenes.co

Google Notebook LM: You’re proposing using vector embeddings of the domain names centenes.co and centene.com and validating their relationship using the k-nearest neighbor (k-NN) algorithm. This is a conceptually sound approach to quantitatively assess the similarity or dissimilarity between these domain names, especially in the context of identifying potential typosquatting or fraudulent domains. Here’s how it could work, drawing on our understanding from the sources and our conversation history (note that the technical details of vector embeddings and k-NN are outside the scope of the provided sources and should be independently verified):

1. Vector Embeddings for Domain Names:

Vector embeddings are numerical representations of items (in this case, domain names) in a multi-dimensional space, where the position and orientation of a vector capture semantic or structural properties of the item.

For domain names, several techniques could be used to generate these embeddings (these techniques are not mentioned in your sources):

Character-based embeddings: Each character in the domain name could be represented by a vector, and the embedding for the entire domain could be derived by aggregating these individual character embeddings (e.g., through averaging, summing, or using recurrent neural networks). This would capture the sequence of characters and thus be sensitive to small differences like the ‘s’ in centenes.

Subword embeddings: The domain names could be broken down into meaningful subparts (e.g., “centene”, “s”, “co”, “com”), and embeddings could be learned for these subwords. The domain embedding would then be a combination of its subword embeddings.

N-gram embeddings: Sequences of n characters within the domain name could be treated as features, and embeddings could be learned for these n-grams.

The goal is to create embeddings such that semantically or structurally similar domain names have vectors that are close to each other in the embedding space, while dissimilar domains have vectors that are further apart. In our case, we would expect the embeddings for centenes.co and centene.com to be relatively close due to the high degree of character overlap, which is characteristic of typosquatting [our conversation history, where we identified the domain mismatch as a red flag].

2. Measuring Similarity:

Once the vector embeddings for centenes.co and centene.com (and potentially other domain names) are generated, their similarity can be quantified using distance or similarity metrics. Common metrics include:

Cosine Similarity: Measures the cosine of the angle between two vectors. A value close to 1 indicates high similarity, while a value close to -1 indicates high dissimilarity, and 0 indicates orthogonality (no correlation).

Euclidean Distance: Measures the straight-line distance between the endpoints of two vectors. Smaller distances indicate higher similarity.

By calculating the cosine similarity or Euclidean distance between the embeddings of centenes.co and centene.com, we could get a numerical measure of how similar they are based on their character sequences or subword components.

The validation process would involve the following steps:
Generate the vector embedding for the domain name we want to classify (centenes.co).
Calculate the distance (e.g., Euclidean or cosine distance) between the embedding of centenes.co and the embeddings of all the labeled domains in our dataset.
Identify the k nearest neighbors to centenes.co based on these distances (i.e., the k labeled domains with the most similar embeddings).
Predict the label for centenes.co based on the majority class among its k nearest neighbors. If most of the nearest neighbors are labeled as “fraudulent/suspicious”, then centenes.co would also be classified as such, providing validation to our suspicion based on the domain mismatch we observed [our conversation history].

In summary, using vector embeddings and k-NN could be a valuable approach to formally analyze the relationship between domain names like centenes.co and centene.com. By representing the domains numerically and comparing them to a labeled dataset of legitimate and fraudulent domains, we could obtain a data-driven validation of the suspicion that centenes.co is potentially malicious due to its similarity to the legitimate centene.com, a technique often employed in typosquatting attacks.

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