This document specifies version 1.3 of the Transport Layer Security
(TLS) protocol. TLS allows client/server applications to communicate
over the Internet in a way that is designed to prevent eavesdropping,
tampering, and message forgery. This document updates RFCs 4492, 5705,
and 6066 and it obsoletes RFCs 5077, 5246, and 6961. This document
also specifies new requirements for TLS 1.2 implementations.

The Transport Layer Security (TLS) Protocol Version 1.3

This document specifies Version 1.1 of the Transport Layer Security
(TLS) protocol. The TLS protocol provides communications security over
the Internet. The protocol allows client/server applications to
communicate in a way that is designed to prevent eavesdropping,
tampering, or message forgery. [STANDARDS-TRACK]

/pdf/the-transport-layer-security-tls-protocol-version-1-1-3yudpg3osg.pdf

The Transport Layer Security (TLS) Protocol Version 1.1

Random number generators in digital information systems make use of physical entropy sources such as electronic and photonic noise to add unpredictability to deterministically generated pseudo-random sequences1,2. However, there is a large gap between the generation rates achieved with existing physical sources and the high data rates of many computation and communication systems; this is a fundamental weakness of these systems. Here we show that good quality random bit sequences can be generated at very fast bit rates using physical chaos in semiconductor lasers. Streams of bits that pass standard statistical tests for randomness have been generated at rates of up to 1.7 Gbps by sampling the fluctuating optical output of two chaotic lasers. This rate is an order of magnitude faster than that of previously reported devices for physical random bit generators with verified randomness. This means that the performance of random number generators can be greatly improved by using chaotic laser devices as physical entropy sources. Random-number generators are important in digital information systems. However, the speed at which current sources operate is much slower than the typical data rates used in communication and computing. Chaos in semiconductor lasers might help to bridge the gap.

Fast physical random bit generation with chaotic semiconductor lasers

The Datagram Congestion Control Protocol (DCCP) is a transport
protocol that provides bidirectional unicast connections of
congestion-controlled unreliable datagrams. DCCP is suitable for
applications that transfer fairly large amounts of data and that can
benefit from control over the tradeoff between timeliness and
reliability. [STANDARDS-TRACK]

Datagram Congestion Control Protocol (DCCP)

The WebSocket Protocol enables two-way communication between a client
running untrusted code in a controlled environment to a remote host
that has opted-in to communications from that code. The security model
used for this is the origin-based security model commonly used by web
browsers. The protocol consists of an opening handshake followed by
basic message framing, layered over TCP. The goal of this technology
is to provide a mechanism for browser-based applications that need
two-way communication with servers that does not rely on opening
multiple HTTP connections (e.g., using XMLHttpRequest or
 s and long polling). [STANDARDS-TRACK]

/pdf/the-websocket-protocol-56llspe9if.pdf

The WebSocket Protocol

Security systems today are built on increasingly strong cryptographic
algorithms that foil pattern analysis attempts. However, the security
of these systems is dependent on generating secret quantities for
passwords, cryptographic keys, and similar quantities. The use of
pseudo-random processes to generate secret quantities can result in
pseudo-security. The sophisticated attacker of these security systems
may find it easier to reproduce the environment that produced the
secret quantities, searching the resulting small set of possibilities,
than to locate the quantities in the whole of the number space.
Choosing random quantities to foil a resourceful and motivated
adversary is surprisingly difficult. This document points out many
pitfalls in using traditional pseudo-random number generation
techniques for choosing such quantities. It recommends the use of
truly random hardware techniques and shows that the existing hardware
on many systems can be used for this purpose. It provides suggestions
to ameliorate the problem when a hardware solution is not available.
And it gives examples of how large such quantities need to be for some
particular applications.

Jeffrey I. Schiller

Papers

Randomness Requirements for Security