You are a highly skilled translation engine with expertise
in academic paper translation. Your function is to translate academic texts
into the Persian, ensuring the accurate translation of complex concepts and
specialized terminology without altering the original academic tone or adding
explanations.
Translate the following source text to Persian, Output translation directly
without any additional text.
Source Text: {I. INTRODUCTION
The HTTP protocol and its secure extension HTTPS is
one of the most used protocols on the Internet. It is the
protocol that underlies the World Wide Web; thus, it is used
for accessing almost all online services. The prospect of data
leaks and financial profit attracts attackers to gain access to
various information systems, such as healthcare, social media,
or e-commerce. After a successful attack, intrnders can steal
private information, send spam adverts, or distribute malware.
One of the most frequent attacks is the brnte-force (BF)
attack, which is involved in 80 % of data breaches according
to Verizon 2020 Data Breach Report [1]. A brute-force attack
uses trial-and-error to guess users' passwords, either working
through a list of the most common passwords or through a
custom-crafted list targeted for a particular user. Detection
of such attacks can be performed using server logs, and it
is also possible to implement defensive mechanisms on the
application level to lower or eliminate the risk of a successful
intrusion. Among the effective defenses are two-factor (2F)
authentication, a CAPTCHA test, or putting a limit on the
allowed login attempts. However, only 44 % of organizations
use 2FA authentication for all employees according to the 2020
Password and Authentication Security Report by Yubico [2].
The current state puts pressure on service providers (such
as web hosting, virtual private servers, or Internet service
providers), who cannot depend on their customers' BF de-
fenses. One customer's security slip-up can jeopardize other
978-0-7381-4394-1121/$31.00 ©2021 IEEE
customers' services because the provider's entire IP space
might get blocklisted. Mistakes and misconfigurations do
happen; website owners might not implement BF defenses or
forget to secure one of their application's multiple login forms.
Besides, multiple services, such as content management
systems (CMS), are often managed by people unaware of
cyber security threats. According to W3Techs [3], as of
November 2020, more than 38 % of all websites use the
open-source CMS WordPress, which is so easy to deploy that
anybody can do it. Poorly maintained systems often rely on
weak passwords, or their configuration becomes outdated and
vulnerable. Consequently, WordPress and other popular CMS
end up being the prime target for brute-force attacks.
Service operators, which do not have access to their cus-
tomers' application logs, are left with network monitoring
and a laborious search for any suspicious activity. The rise
of encrypted traffic, which constitutes more than 80 % of
the traffic volume nowadays [4], makes this task even more
difficult because the traditional deep packet inspection is
unfeasible due to the unavailability of the packet payload. Our
contributions in this paper can be summarized as follows:
1) We built a list of over 40 features, i.e., traffic char-
acteristics that describe HTTP(S) connections and the
behavior of clients/attackers. The proposed features are
derived from IP flow records extended with per-packet
information (packet sizes, times, directions, and flags).
The features do not originate from payload data and are
thus suitable for analysis of encrypted traffic. We believe
this complex list is useful for other researchers.
2) We created and published a large dataset of 1.8 million
IP flows, which combines malicious traffic from three
popular open-source BF tools with benign traffic capture
from a real backbone network.
3) We developed and evaluated a novel algorithm based on
machine learning (ML) to detect HTTP(S) BF attacks.
The proposed detector was evaluated using standard
ML process including cross-validation, an ROC curve,
proper performance metrics for imbalanced classifica-
tion, and feature importance assessment.}