Enhancing Networking Cipher Algorithms with Natural Language

• URL: http://arxiv.org/abs/2206.10924v1
• Date: Wed, 22 Jun 2022 09:05:52 GMT
• Title: Enhancing Networking Cipher Algorithms with Natural Language
• Authors: John E. Ortega
• Abstract summary: Natural language processing is considered as the weakest link in a networking encryption model. This paper summarizes how languages can be integrated into symmetric encryption as a way to assist in the encryption of vulnerable streams.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work provides a survey of several networking cipher algorithms and proposes a method for integrating natural language processing (NLP) as a protective agent for them. Two main proposals are covered for the use of NLP in networking. First, NLP is considered as the weakest link in a networking encryption model; and, second, as a hefty deterrent when combined as an extra layer over what could be considered a strong type of encryption -- the stream cipher. This paper summarizes how languages can be integrated into symmetric encryption as a way to assist in the encryption of vulnerable streams that may be found under attack due to the natural frequency distribution of letters or words in a local language stream.

Related papers

• Transformers -- Messages in Disguise [3.74142789780782]
  NN based cryptography is being investigated due to its ability to learn and implement random cryptographic schemes. R is comprised of three new NN layers: the (i) projection layer, (ii) inverse projection layer, and (iii) dot-product layer. This results in an ANC network that (i) is computationally efficient, (ii) ensures the encrypted message is unique, and (iii) does not induce any communication overhead.
  arXiv  Detail & Related papers  (2024-11-15T15:42:29Z)
• Revocable Encryption, Programs, and More: The Case of Multi-Copy Security [48.53070281993869]
  We show the feasibility of revocable primitives, such as revocable encryption and revocable programs. This suggests that the stronger notion of multi-copy security is within reach in unclonable cryptography.
  arXiv  Detail & Related papers  (2024-10-17T02:37:40Z)
• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [56.019447113206006]
  Large Language Models (LLMs) have achieved remarkable progress in code generation. CodeIP is a novel multi-bit watermarking technique that embeds additional information to preserve provenance details. Experiments conducted on a real-world dataset across five programming languages demonstrate the effectiveness of CodeIP.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion [117.178835165855]
  This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs. Our studies reveal a new and universal safety vulnerability of these models against code input. We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization.
  arXiv  Detail & Related papers  (2024-03-12T17:55:38Z)
• Post-Quantum Cryptography Neural Network [0.0]
  It is necessary to construct a Post-Quantum Cryptography (PQC) method to resist quantum computing attacks. This study proposes a PQC-based neural network that maps a code-based PQC method to a neural network structure. In the future, the proposed PQC-based neural network could be applied to various applications.
  arXiv  Detail & Related papers  (2024-02-25T06:19:04Z)
• Cryptanalysis and improvement of multimodal data encryption by machine-learning-based system [0.0]
  encryption algorithms to accommodate varied requirements of this field. Best approach to analyzing an encryption algorithm is to identify a practical and efficient technique to break it.
  arXiv  Detail & Related papers  (2024-02-24T10:02:21Z)
• GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher [85.18213923151717]
  Experimental results show certain ciphers succeed almost 100% of the time to bypass the safety alignment of GPT-4 in several safety domains. We propose a novel SelfCipher that uses only role play and several demonstrations in natural language to evoke this capability.
  arXiv  Detail & Related papers  (2023-08-12T04:05:57Z)
• Stealing the Decoding Algorithms of Language Models [56.369946232765656]
  A key component of generating text from modern language models (LM) is the selection and tuning of decoding algorithms. In this work, we show, for the first time, that an adversary with typical API access to an LM can steal the type and hyper parameters of its decoding algorithms. Our attack is effective against popular LMs used in text generation APIs, including GPT-2, GPT-3 and GPT-Neo.
  arXiv  Detail & Related papers  (2023-03-08T17:15:58Z)
• Revocable Cryptography from Learning with Errors [61.470151825577034]
  We build on the no-cloning principle of quantum mechanics and design cryptographic schemes with key-revocation capabilities. We consider schemes where secret keys are represented as quantum states with the guarantee that, once the secret key is successfully revoked from a user, they no longer have the ability to perform the same functionality as before.
  arXiv  Detail & Related papers  (2023-02-28T18:58:11Z)
• TEDL: A Text Encryption Method Based on Deep Learning [10.428079716944463]
  This paper proposes a novel text encryption method based on deep learning called TEDL. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution.
  arXiv  Detail & Related papers  (2020-03-09T11:04:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.