Neural network-based symmetric encryption algorithm with encrypted traffic protocol identification
Jiakai Hao, Ming Jin, Yuting Li, Yuxin Yang
TL;DR
This paper introduces a neural network-based encryption algorithm and a method to identify encrypted traffic protocols, aiming to improve power grid security.
Contribution
A novel encrypted traffic protocol identification scheme integrating temporal and spatial features is proposed.
Findings
A plaintext guessing model based on symmetric encryption algorithms is introduced and evaluated.
The proposed scheme demonstrates effectiveness in identifying encrypted traffic protocols.
The model's performance is analyzed within symmetric and asymmetric encryption frameworks.
Abstract
Cryptography is a cornerstone of power grid security, with the symmetry and asymmetry of cryptographic algorithms directly influencing the resilience of power systems against cyberattacks. Cryptographic algorithm identification, a critical component of cryptanalysis, is pivotal to assessing algorithm security and hinges on the core characteristics of symmetric and asymmetric encryption methods. A key challenge lies in discerning subtle spatial distribution patterns within ciphertext data to infer the underlying cryptographic algorithms, which is essential for ensuring the communication security of power systems. In this study, we first introduce a plaintext guessing model (SCGM model) based on symmetric encryption algorithms, leveraging the strengths of convolutional neural networks to evaluate the plaintext guessing capabilities of four symmetric encryption algorithms. This model is…
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Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Cryptographic Implementations and Security · Chaos-based Image/Signal Encryption