CipherSniffer: Classifying Cipher Types

06/13/2023
by   Brendan Artley, et al.
0

Ciphers are a powerful tool for encrypting communication. There are many different cipher types, which makes it computationally expensive to solve a cipher using brute force. In this paper, we frame the decryption task as a classification problem. We first create a dataset of transpositions, substitutions, text reversals, word reversals, sentence shifts, and unencrypted text. Then, we evaluate the performance of various tokenizer-model combinations on this task.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/03/2019

Predicting Algorithm Classes for Programming Word Problems

We introduce the task of algorithm class prediction for programming word...
research
11/21/2014

A Joint Probabilistic Classification Model of Relevant and Irrelevant Sentences in Mathematical Word Problems

Estimating the difficulty level of math word problems is an important ta...
research
06/05/2022

Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classification

The task of text classification using Bidirectional based LSTM architect...
research
09/17/2021

Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications

Sentence-level Quality estimation (QE) of machine translation is traditi...
research
03/07/2019

Creation and Evaluation of Datasets for Distributional Semantics Tasks in the Digital Humanities Domain

Word embeddings are already well studied in the general domain, usually ...
research
01/06/2018

Visual Text Correction

This paper tackles the Text Correction (TC) problem, i.e., finding and r...
research
10/24/2017

Scaling Text with the Class Affinity Model

Probabilistic methods for classifying text form a rich tradition in mach...

Please sign up or login with your details

Forgot password? Click here to reset