用机器学习对二战时期密码进行分类
Classifying World War II Era Ciphers with Machine Learning
AI总结：
本研究用机器学习分类二战密码（Enigma等），在仅密文且1000字符下准确率超97%，经典模型不逊于深度学习，且相似密码区分难度略高但可控。
摘要：
我们确定了在仅提供密文的情况下，机器学习和深度学习技术对选定的二战时期密码进行分类的准确率。所考虑的具体密码包括 Enigma、M-209、Sigaba、Purple 和 Typex。我们实验了三种经典机器学习模型，即支持向量机（SVM）、k 近邻（k-NN）和随机森林（RF）。我们还实验了四种基于深度学习的神经网络模型：多层感知器（MLP）、长短期记忆网络（LSTM）、极限学习机（ELM）和卷积神经网络（CNN）。每个模型都基于由直方图、双字母组和原始密文字母序列组成的特征进行训练。此外，分类问题在四种不同场景下进行考虑：固定明文与固定密钥、随机明文与固定密钥、固定明文与随机密钥，以及随机明文与随机密钥。在最现实的场景下，给定每段密文 1000 个字符，我们能够以超过 97% 的准确率区分这些密码。此外，我们还考察了部分学习技术的准确率随密文消息长度的变化情况。有点出乎意料的是，我们的经典机器学习模型的表现至少与深度学习模型相当。我们还发现，设计上更为相似的密码区分起来更具挑战性，但难度并未达到预期那么高。
英文摘要：
We determine the accuracy with which machine learning and deep learning techniques can classify selected World War II era ciphers when only ciphertext is available. The specific ciphers considered are Enigma, M-209, Sigaba, Purple, and Typex. We experiment with three classic machine learning models, namely, Support Vector Machines (SVM), $k$-Nearest Neighbors ($k$-NN), and Random Forest (RF). We also experiment with four deep learning neural network-based models: Multi-Layer Perceptrons (MLP), Long Short-Term Memory (LSTM), Extreme Learning Machines (ELM), and Convolutional Neural Networks (CNN). Each model is trained on features consisting of histograms, digrams, and raw ciphertext letter sequences. Furthermore, the classification problem is considered under four distinct scenarios: Fixed plaintext with fixed keys, random plaintext with fixed keys, fixed plaintext with random keys, and random plaintext with random keys. Under the most realistic scenario, given 1000 characters per ciphertext, we are able to distinguish the ciphers with greater than 97% accuracy. In addition, we consider the accuracy of a subset of the learning techniques as a function of the length of the ciphertext messages. Somewhat surprisingly, our classic machine learning models perform at least as well as our deep learning models. We also find that ciphers that are more similar in design are somewhat more challenging to distinguish, but not as difficult as might be expected.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
