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

In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.

/pdf/adversarial-machine-learning-56wpx170q1.pdf

Adversarial machine learning

The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications. He gives a high-level overview and mentions several types of attacks, as well as several types of defenses, and theoretical limits derived from a study of near-optimal evasion.

/pdf/adversarial-machine-learning-2sref192q1.pdf

Adversarial Machine Learning

Each month, more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity for the purpose of stealing account information, logon credentials, and identity information in general. This attack method, commonly known as "phishing," is most commonly initiated by sending out emails with links to spoofed websites that harvest information. We present a method for detecting these attacks, which in its most general form is an application of machine learning on a feature set designed to highlight user-targeted deception in electronic communication. This method is applicable, with slight modification, to detection of phishing websites, or the emails used to direct victims to these sites. We evaluate this method on a set of approximately 860 such phishing emails, and 6950 non-phishing emails, and correctly identify over 96% of the phishing emails while only mis-classifying on the order of 0.1% of the legitimate emails. We conclude with thoughts on the future for such techniques to specifically identify deception, specifically with respect to the evolutionary nature of the attacks and information available.

/pdf/learning-to-detect-phishing-emails-19e61vbod6.pdf

Learning to detect phishing emails

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. Although academic research on recommender systems has increased significantly over the past 10years, there are deficiencies in the comprehensive literature review and classification of that research. For that reason, we reviewed 210 articles on recommender systems from 46 journals published between 2001 and 2010, and then classified those by the year of publication, the journals in which they appeared, their application fields, and their data mining techniques. The 210 articles are categorized into eight application fields (books, documents, images, movie, music, shopping, TV programs, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). Our research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this paper helps anyone who is interested in recommender systems research with insight for future research direction.

A literature review and classification of recommender systems research

This document describes the RELATIONAL extension to the Sieve mail
filtering language defined in RFC 3028. This extension extends
existing conditional tests in Sieve to allow relational operators. In
addition to testing their content, it also allows for testing of the
number of entities in header and envelope fields.

Sieve Email Filtering: Relational Extension

The Internet Message Access Protocol, Version 4rev2 (IMAP4rev2) allows
a client to access and manipulate electronic mail messages on a
server. IMAP4rev2 permits manipulation of mailboxes (remote message
folders) in a way that is functionally equivalent to local folders.
IMAP4rev2 also provides the capability for an offline client to
resynchronize with the server. IMAP4rev2 includes operations for
creating, deleting, and renaming mailboxes, checking for new messages,
permanently removing messages, setting and clearing flags, RFC 5322,
RFC 2045 and RFC 2231 parsing, searching, and selective fetching of
message attributes, texts, and portions thereof. Messages in IMAP4rev2
are accessed by the use of numbers. These numbers are either message
sequence numbers or unique identifiers. IMAP4rev2 does not specify a
means of posting mail; this function is handled by a mail submission
protocol such as the one specified in RFC 6409.

Internet Message Access Protocol (IMAP) - Version 4rev2

Electronic mail (e-mail) has become an important tool for companies to use to conduct their businesses. With the introduction of the World Wide Web, awareness of the existence of the Internet has exponentially increased over the last two years, and people are starting to realize that there is more to the Internet than just the Web. Companies are expanding their use of e-mail from internal to external. But the large set of proprietary, noninteroperable e-mail systems make this more of a trip through a jungle than a drive along the information highway. Most approaches to overcome the connectivity problems use gateways to convert between the proprietary format and the Internet standards. These conversions are lossy at best; hence, most proprietary system vendors are revamping their systems to base them on Internet standards. This paper summarizes the current state of the most important Internet standards related to e-mail and the general state of proprietary e-mail systems. It then introduces a set of technologies we developed to solve the complex problem of evolving from proprietary to Internet-standards-based e-mail systems. We have structured these technologies into Internet Messaging Frameworks.

Internet messaging frameworks

The Sieve scripting language can be used to implement whitelisting,
blacklisting, personal distribution lists, and other sorts of list
matching. Currently, this requires that all members of such lists be
hardcoded in the script itself. Whenever a member of a list is added
or deleted, the script needs to be updated and possibly uploaded to a
mail server. This document defines a Sieve extension for accessing
externally stored lists -- lists whose members are stored externally
to the script, such as using LDAP (RFC 4510), ACAP (RFC 2244), or
relational databases. ToDo o Should we add a mandatory-to-implement
tag? Ned suggests (and I agree) that it might be good to add a
registry of well-defined strings that can be used instead of URIs, and
define the initial string "ab" to represent the user's personal
address book. o Do we want to have the :list test set variables? See
Ned's comments in the text.

Sieve Extension: Externally Stored Lists

This paper talks about the cloud computing that had come full circle toward logically centralised administrative domains and shared computational resources. Distributed computing, cluster computing, grid computing, and cloud computing are all terms that have developed over the past few years. Each is distinct from the others in some ways, but there's a great deal of overlap. A turn-of-the-century distributed computing application that has the right profile could easily have morphed through the series, proudly calling itself a cloud computing application today.

Barry Leiba

Papers

Sieve Email Filtering: Relational Extension

Internet Message Access Protocol (IMAP) - Version 4rev2

Internet messaging frameworks

Sieve Extension: Externally Stored Lists

Having One's Head in the Cloud