ESET SMART SECURITY Guía de usuario descargar pdf (Pagina 46)

small sum ﬁrst

• You are asked to enter, under various pretenses (data veriﬁcation,

Financial operations), some of your personal data – bank account

numbers, usernames and passwords, etc.

• It is written in a foreign language

• You are asked to buy a product you are not interested in. If you

decide to purchase anyway, please verify that the message sender

is a reliable vendor (consult the original product manufacturer).

• Some of the words are misspelled in an attempt to trick your spam

ﬁlter. For example “vaigra” instead of “viagra”, etc.

6.3.4.1 Rules

In the context of Antispam solutions and email clients, rules are tools

for manipulating email functions. They consist of two logical parts:

1. Condition (e.g., an incoming message from a certain address)

2. Action (e.g., deletion of the message, moving it to a speciﬁed

folder)

The number and combination of rules varies with the Antispam

solution. These rules serve as measures against spam (unsolicited

email). Typical examples:

• 1. Condition: An incoming email message contains some of the

words typically seen in spam messages

2. Action: Delete the message

• 1. Condition: An incoming email message contains an attachment

with an .exe extension

2. Action: Delete the attachment and deliver the message to the

mailbox

• 1. Condition: An incoming email message arrives from your

employer

2. Action: Move the message to the “Work” folder.

We recommend that you use a combination of rules in Antispam

programs in order to facilitate administration and to more eectively

ﬁlter spam.

6.3.4.1 Bayesian ﬁlter

Bayesian spam ﬁltering is an eective form of email ﬁltering used by

almost all Antispam products. It is able to identify unsolicited email

with high accuracy and can work on a per‑user basis.

The functionality is based on the following principle: The learning

process takes place in the ﬁrst phase. The user manually marks a

sucient number of messages as legitimate messages or as spam

(normally 200/200). The ﬁlter analyzes both categories and learns, for

example, that spam usually contains the words “rolex” or “viagra”, and

legitimate messages are sent by family members or from addresses in

the user’s contact list. Provided that a sucient number of messages

are processed, the Bayesian ﬁlter is able to assign a speciﬁc “spam

index” to each message in order to determine whether it is spam or

not.

The main advantage of a Baysesian ﬁlter is its ﬂexibility. For example,

if a user is a biologist, all incoming emails concerning biology or

relative ﬁelds of study will generally receive a lower probability

index. If a message includes words that would normally qualify it as

unsolicited, but it is sent by someone from the user’s contact list,

it will be marked as legitimate, because senders from a contact list

decrease overall spam probability.

6.3.4.2 Whitelist

In general, a whitelist is a list of items or persons who are accepted,

or have been granted permission. The term “email whitelist“ deﬁnes a

list of contacts from whom the user wishes to receive messages. Such

whitelists are based on keywords searched for in email addresses,

domain names, or IP addresses.

If a whitelist works in “exclusivity mode“, then messages from any

other address, domain, or IP address will not be received. If a whitelist

is not exclusive, such messages will not be deleted, but ﬁltered in

some other way.

A whitelist is based on the opposite principle to that of a blacklist.

Whitelists are relatively easy to maintain, more so than blacklists.

We recommend that you use both the Whitelist and Blacklist to ﬁlter

spam more eectively.

6.3.4.3 Blacklist

Generally, a blacklist is a list of unaccepted or forbidden items or

persons. In the virtual world, it is a technique enabling acceptance of

messages from all users not present on such a list.

There are two types of blacklist: Those created by users within their

Antispam application, and professional, regularly updated blacklists

which are created by specialized institutions and can be found on the

Internet.

It is essential to use blacklists to successfully block spam, but they are

dicult to maintain, since new items to be blocked appear every day.

We recommended you use both a whitelist and a blacklist to most

eectively ﬁlter spam.

6.3.4.5 Server‑side control

Server‑side control is a technique for identifying mass spam based

on the number of received messages and the reactions of users. Each

message leaves a unique digital “footprint” based on the content of the

message. The unique ID number tells nothing about the content of

the email. Two identical messages will have identical footprints, while

dierent messages will have dierent footprints.

If a message is marked as spam, its footprint is sent to the server.

If the server receives more identical footprints (corresponding to a

certain spam message), the footprint is stored in the spam footprints

database. When scanning incoming messages, the program sends

the footprints of the messages to the server. The server returns

information on which footprints correspond to messages already

marked by users as spam.

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