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rfc:rfc9564



Independent Submission M. Blanchet Request for Comments: 9564 Viagenie Category: Informational 1 April 2024 ISSN: 2070-1721

              Faster Than Light Speed Protocol (FLIP)

Abstract

 The recent advances in artificial intelligence (AI) such as large
 language models enable the design of the Faster than LIght speed
 Protocol (FLIP) for Internet.  FLIP provides a way to avoid
 congestion, enhance security, and deliver faster packets on the
 Internet by using AI to predict future packets at the receiving peer
 before they arrive.  This document describes the protocol, its
 various encapsulations, and some operational considerations.

Status of This Memo

 This document is not an Internet Standards Track specification; it is
 published for informational purposes.
 This is a contribution to the RFC Series, independently of any other
 RFC stream.  The RFC Editor has chosen to publish this document at
 its discretion and makes no statement about its value for
 implementation or deployment.  Documents approved for publication by
 the RFC Editor are not candidates for any level of Internet Standard;
 see Section 2 of RFC 7841.
 Information about the current status of this document, any errata,
 and how to provide feedback on it may be obtained at
 https://www.rfc-editor.org/info/rfc9564.

Copyright Notice

 Copyright (c) 2024 IETF Trust and the persons identified as the
 document authors.  All rights reserved.
 This document is subject to BCP 78 and the IETF Trust's Legal
 Provisions Relating to IETF Documents
 (https://trustee.ietf.org/license-info) in effect on the date of
 publication of this document.  Please review these documents
 carefully, as they describe your rights and restrictions with respect
 to this document.

Table of Contents

 1.  Introduction
 2.  Protocol Peer Preparation
 3.  FLIP Header
 4.  Protocol Operation
 5.  Versioning
 6.  Future Work
 7.  IANA Considerations
 8.  Security Considerations
 9.  Informative References
 Acknowledgements
 Author's Address

1. Introduction

 ChatGPT was introduced to the public on 30 November 2022 [CHATGPT].
 Since then, large language models (LLMs) have been used for a large
 variety of applications.  It demonstrates the powerful ability to
 generate precise output based on the input and based on the
 appropriate training of the LLM.  This protocol specification uses
 this ability to predict future packets before they arrive at the
 receiving peer, therefore achieving faster-than-light-speed delivery,
 hence the protocol name: Faster than LIght speed Protocol (FLIP).
 Since FLIP can predict packets, frames, strings, or byte streams, it
 could be used at any layer of the IP protocol stack.  Moreover, with
 proper training, FLIP can also predict future encrypted packets, as
 encryption is just strings of bytes.  This specification shows FLIP
 as a Layer 2 shim as well as a transport shim layer.  Since FLIP can
 be used at any layer, it is expected that additional specifications
 will be created, such as predicting HTTP requests and answers, email
 content, and more.
 Since communications in deep space are unfortunately limited to light
 speed, and given the very large distances between spacecrafts and
 Earth, the consequence is very long delays.  By offering faster-than-
 light-speed delivery, FLIP is a key enabler and addition to deep-
 space IP networking [IP-DEEP-SPACE].

2. Protocol Peer Preparation

 In order to successfully achieve faster than light speed, the peers
 of any protocol layer used by FLIP must prepare their side of the
 connection with the right model trained for the specific case.  This
 document does not dictate any specific LLM, as the implementations
 may choose the one that best works for their use case and train them
 accordingly.  As with any LLM, it is paramount to use a lot of
 training data, such as packet captures, in a variety of conditions to
 produce the best trained model.  To avoid security, privacy, and
 legal issues, the specifics of which LLM is used, how it was trained,
 and what is the data set used, shall not be published nor disclosed
 in the protocol.
 As an example, an implementation may elect to collect a significant
 number of Packet Capture (PCAP) files from tcpdump wiretapping at
 various vantage points on the Internet.  The fact that traffic may be
 encrypted is not an issue, since a well-trained LLM will be able to
 predict encrypted traffic as accurately as unencrypted traffic.

3. FLIP Header

 Wherever FLIP is used (below IP, above IP or other transport, or at
 the application layer), a FLIP shim header is inserted.
    +----------+---------+----------------+----------------+
    |  Version | Command | Inner Protocol | Optional Data  |
    +----------+---------+----------------+----------------+
 The header contains the following fields:
 Version:  A field of variable and unspecified length that contains
    the SHA-256 hash of the model, used as the version, as described
    in Section 5.
 Command:  The codepoint identifying the operation of this FLIP frame.
    Commands are described in Section 4.  The initial list of valid
    FLIP commands is below.
    The maximum number size is infinite, given that artificial
    intelligence peers can support an infinite number of commands, by
    just updating their models without the need to update their
    protocol implementation.
                   +=========+===========+===========+
                   | Command | Codepoint | Reference |
                   +=========+===========+===========+
                   | model   | 0x01      | RFC 9564  |
                   +---------+-----------+-----------+
                   | data    | 0x02      | RFC 9564  |
                   +---------+-----------+-----------+
                                 Table 1
 Inner Protocol:  As the FLIP header is a shim header, the inner
    protocol is specified in this field.  For example, for a FLIP shim
    header inserted between IP and TCP, the IP packet will contain the
    FLIP codepoint as the transport protocol.  The FLIP inner protocol
    field will then contain the TCP codepoint that would otherwise be
    in the IP packet.
 Optional Data:  Some commands have additional data that are following
    the Command field.
 The header length is variable and depends on which command is used.
 Given the use of artificial intelligence by implementations of this
 protocol, the actual length of the header, and the length of each of
 its fields, is not specified in the header.  Instead, it is expected
 that the proper neural network on the receiver side will be able to
 find the actual header termination, thus saving many header bits.
 To properly signal the upper layer about the presence of the FLIP
 header, a specific codepoint is reserved at the layer below FLIP.
 Section 7 lists the registrations for IP and transport codepoints for
 this use.

4. Protocol Operation

 Prior to sending a first packet using FLIP, the sender and the
 receiver should be configured with the appropriate model trained as
 discussed before.  It is left to the implementation to choose the
 right LLM and the right training data set.
 The following commands are defined:
 Model:  (codepoint 0x01).  This command provides a way for peers to
    send their model in-band of the FLIP protocol.  The model itself
    is carried in the Optional Data field of the FLIP header.  Prior
    to the actual model data, a MIME header is inserted with the
    proper media type.  If the media type for the model does not
    exist, it should be registered in the IANA Media Type registry.
 Data:  (codepoint 0x02).  This command tells the receiving peer that
    the data that follows can be predicted and therefore achieves
    faster-than-light-speed performance.
 Sending the model in-band to the other peer is an operation that
 should be done rarely, as models may be large in size.  Moreover, it
 actually discloses the model for any wiretapping adversary.
 Implementors may consider using a post-quantum cryptographic
 algorithm that is also immune to AI prediction, therefore a post-
 Quantum-AI cryptographic algorithm.

5. Versioning

 As described in [RFC6709], most protocols should be designed to
 enable future enhancements, such as providing a way to signal a new
 version of the protocol.  In the case of FLIP, trained models will
 always be enhanced by new training.  A SHA-256 [RFC6234] hash of the
 trained model is used as a version number so each peer knows which
 FLIP version is being used.  The SHA-256 hash is put in version field
 in the FLIP header as described previously.  Given that new SHA-256
 hashes are not sequential but fully random, replay attacks of future
 predictions are prevented.

6. Future Work

 This new protocol may revolutionize how we design Internet protocols
 and how we use the Internet.  For example, it is envisioned that this
 protocol may be used for video streaming, augmented reality, virtual
 reality, and post-quantum cryptography to name a few.  By predicting
 the future packets, all these protocols and applications can benefit
 the use of FLIP.

7. IANA Considerations

 For FLIP, codepoints could be registered in the following IANA
 registries.
  • Protocol Numbers [IANA-PN]: 345, FLIP, Faster than LIght speed

Protocol, RFC 9564

  • Service Name and Transport Protocol Port Number Registry

[IANA-SN]: FLIP, 68534, udp and tcp, RFC 9564

8. Security Considerations

 The ability to predict future packets based on LLMs can be used by
 adversaries that are listening to the traffic via wiretapping.  If
 they have access to the same model used by the destination peer, they
 could use it to predict the next packets and then initiate various
 attacks, including novel ones such as the "futureplay attack."
 Compared to the typical replay attack, this attack is where the
 adversary will predict future packets and then send them in advance
 to the destination.  While it may not be obvious at this time, these
 novel attacks should be investigated before they become a problem.
 Therefore, further research in this field is suggested.
 The ability for a peer to predict future packets enhances the overall
 security of the Internet because adversaries will not be able to
 inject bad packets in a connection, as the destination will be able
 to compare the received bad packet with the calculated prediction and
 therefore will easily identify and deny any bad packets.

9. Informative References

 [CHATGPT]  Wikipedia, "ChatGPT", 20 March 2024,
            <https://en.wikipedia.org/w/
            index.php?title=ChatGPT&oldid=1214732037>.
 [IANA-PN]  IANA, "Protocol Numbers",
            <https://www.iana.org/assignments/protocol-numbers/>.
 [IANA-SN]  IANA, "Service Name and Transport Protocol Port Number
            Registry", <https://www.iana.org/assignments/service-
            names-port-numbers/>.
 [IP-DEEP-SPACE]
            Blanchet, M., Huitema, C., and D. Bogdanović, "Revisiting
            the Use of the IP Protocol Stack in Deep Space: Assessment
            and Possible Solutions", Work in Progress, Internet-Draft,
            draft-many-deepspace-ip-assessment-01, 4 March 2024,
            <https://datatracker.ietf.org/doc/html/draft-many-
            deepspace-ip-assessment-01>.
 [RFC6234]  Eastlake 3rd, D. and T. Hansen, "US Secure Hash Algorithms
            (SHA and SHA-based HMAC and HKDF)", RFC 6234,
            DOI 10.17487/RFC6234, May 2011,
            <https://www.rfc-editor.org/info/rfc6234>.
 [RFC6709]  Carpenter, B., Aboba, B., Ed., and S. Cheshire, "Design
            Considerations for Protocol Extensions", RFC 6709,
            DOI 10.17487/RFC6709, September 2012,
            <https://www.rfc-editor.org/info/rfc6709>.

Acknowledgements

 Since this protocol specification is using artificial intelligence
 and large language models, it was deemed that dumb humans must not
 review this specification.  Instead, the specification has been
 submitted to multiple LLM chat services and was enhanced by their
 comments and suggestions, hence acknowledged here.  In fact, this
 specification may have been produced entirely by LLM chat services.
 Moreover, given the specifications being produced by the IETF relying
 upon human intelligence, using LLMs to produce specifications should
 be envisioned.  Finally, given the difficulty to find experts for
 management positions such as in the IESG or IAB, the use of LLMs
 should be considered to replace those roles.  Unfortunately, given
 privacy, security, and legal considerations, the LLM chat services
 used for this specification cannot be named here.

Author's Address

 Marc Blanchet
 Viagenie
 Email: marc.blanchet@viagenie.ca
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