Chat bots target popular chat networks to distribute spam and malware.
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Chat bots target popular chat networks to distribute spam and malware. In this paper, we first conduct a series of measurements on a large commercial chat network.
Our measurements capture a total of 14 different types of chat bots ranging from simple to advanced. Moreover, we observe that human behavior is more complex than bot behavior. Based on the measurement study, we propose a classification system to accurately distinguish chat bots from human users. The proposed classification system consists of two components: 1 an entropy-based classifier and 2 a machine-learning-based classifier.
The two classifiers complement each other in chat bot detection. The entropy-based classifier is more accurate to detect unknown chat with people around the world bots, whereas the machine-learning-based classifier is faster to detect known chat bots. Our experimental evaluation shows that the proposed classification system is highly effective in differentiating bots from humans.
Millions of people around the world use Internet chat to exchange messages and discuss a broad range of topics on-line. Internet chat is also a unique networked application, because of its human-to-human interaction and low bandwidth consumption [ 9 ]. However, the large user base and open nature of Internet chat make it an ideal target for malicious exploitation.
The abuse of chat services chat with someone from fort macleod, alberta automated programs, known as chat bots, poses a serious threat to on-line users. Chat bots have been found on a of chat systems, including commercial chat networks, such as AOL [ 2915 ], Yahoo!
There are also reports of bots in some non-chat systems with chat features, including online games, such as World of Warcraft [ 732 ] and Second Life roooms 27 ]. Chat bots exploit these on-line systems to send spam, spread malware, and mount phishing attacks. So far, the efforts to combat chat bots have focused on two different approaches: 1 keyword-based filtering and 2 human interactive proofs.
The keyword-based message filters, used by third party chat clients [ 4243 ], suffer from high false negative rates because bot makers frequently update chat bots to evade published keyword lists. In AugustYahoo! There are online petitions against both AOL and Yahoo! While on-line systems rreal besieged with chat bots, no systematic investigation on chat bots has been conducted. The effective detection system against chat bots is in great demand but still missing.
In the paper, we first perform a series of measurements on a large commercial chat network, Yahoo! Our measurements capture a total of 14 different types of chat bots.
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The reall types of chat bots use different triggering mechanisms and text obfuscation techniques. The former determines message timing, and the latter determines message content. Our measurements also reveal that human behavior is more complex than bot behavior, which motivates the use of entropy rate, a measure of complexity, for wiithout bot classification. Based on the measurement study, we propose a classification system to accurately distinguish chat bots from humans.
There are two main components in our classification system: 1 an entropy classifier and 2 a machine-learning classifier.
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Based on sexting finder characteristics of message time and size, the entropy classifier measures the complexity of chat flows and then classifies them as bots or humans. In contrast, the machine-learning classifier is mainly based on message content for detection. While the entropy classifier requires more messages for detection and, thus, is slower, it is more accurate to detect unknown chat bots. Moreover, the entropy classifier helps train the machine-learning classifier.
The machine learning classifier requires less messages for detection and, thus, is faster, but cannot detect most unknown bots. By combining the entropy classifier and the machine-learning classifier, the proposed classification system is highly effective to capture chat bots, in terms of accuracy and speed. We conduct experimental tests on the classification system, and the validate its efficacy on chat bot detection.
The remainder of this paper is structured as follows.
Section 2 covers background on chat bots and related work. Section 3 details our measurements of chat bots and humans. Section 4 describes our chat bot classification dhat. Section 5 evaluates the effectiveness of our approach for chat bot detection. Finally, Section 6 concludes the paper and discusses directions for our future work.
The users connect to a chat server via chat clients that support a certain chat protocol, and they may rrooms and many chat rooms featuring a variety of topics. The chat server relays chat messages to and from on-line users.
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A chat service with a large user base might employ multiple chat servers. In addition, there are several multi-protocol chat clients, such as Pidgin formerly GAIM and Trillian, that allow a user to different chat systems. Although IRC has existed for a long time, it has not gained mainstream popularity. This is mainly because its console-like adult chat world and command-line-based operation are not user-friendly.
The recent chat systems improve user experience by using graphic-based interfaces, as well as adding attractive features such as avatars, emoticons, and audio-video communication capabilities.
Our study is carried out on the Yahoo! Unlike those on most IRC networks, users on the Yahoo! In addition, users on Yahoo! This recently-added feature is to guard against a major source of abuse—bots.
A chat bot is a program that interacts with a chat service to automate tasks for a human, e. The first-generation chat bots were deed to help operate chat rooms, or to entertain chat users, e. However, with the commercialization of the Internet, the wlthout enterprise of chat bots is now sending chat spam.
Chat bots deliver spam URLs via either links in chat messages or user profile links. A single bot operator, controlling a few hundred chat bots, can distribute spam links to thousands of users in different chat rooms, making chat bots very profitable to the bot operator who is paid per-click through affiliate programs.
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Other potential abuses of bots include spreading malware, phishing, booting, and similar malicious activities. A few countermeasures have been used to defend against the abuse of chat bots, though none of them are very effective. Third-party chat clients filter out chat bots, mainly based on key words roos key phrases that are known to be used by chat bots. The drawback with this approach is that it cannot sex chat in souris those unknown or evasive chat bots that do not use the known key words or phrases.
However, very active users in Web-chat and automated scripts used in IRC may send more data than they receive. There is considerable safford sex phone chat between chat and instant messaging IM systems, in terms of protocol and user base. Many widely used chat systems such as IRC predate the rise of IM systems, and have great impact upon the IM system and protocol de.
In return, some new features that make the IM systems more user-friendly have been back-ported to the chat systems. For example, IRC, a classic chat system, implements a of IM-like features, such as presence and file transfers, in its current versions. chaf
Some messaging service providers, such as Yahoo! With this in mind, we outline some related work on IM systems. Liu et al. However, their wthout is based on a corpus of short e-mail spam messages, due to the lack of data on spim. In [ 23 ], Mannan et al.
Leveraging the spreading characteristics of IM malware, Xie et al. However, the usage and behavior of bots in botnets are quite different from those of chat bots. The bots in botnets are malicious programs deed specifically to run on compromised hosts on the Internet, and they are used as platforms to launch a variety of illicit and criminal activities such as credential theft, phishing, distributed denial-of-service attacks, etc.
In contrast, chat bots wwithout automated programs deed mainly to interact with chat users by sending spam messages and URLs in chat rooms. Although having been used chxt botnets as command and control mechanisms [ 112 ], IRC and other chat systems do not play an irreplaceable role in botnets. In fact, due to the increasing focus on detecting and thwarting IRC-based botnets [ 81314 ], recently emerged botnets, such as Phatbot, Nugache, Slapper, and Sinit, show a tendency towards using P2P-based control architectures [ 39 ].
Chat spam shares some similarities with spam. Like spam, chat spam contains advertisements of illegal services and counterfeit goods, and solicits human users to click spam URLs.
Chat bots employ many text obfuscation withouy used by spam such live sex text word padding and synonym substitution. Since the detection of spam can be easily converted into the problem of text classification, many content-based filters utilize withoit algorithms for filtering spam. Among them, Bayesian-based statistical approaches [ 124464520 ] have achieved high accuracy and performance.
Although very successful, Bayesian-based spam detection techniques still can be evaded by carefully crafted messages [ 402218 ]. The focus of our measurements is on public messages posted to Yahoo! The logging of chat messages is available on the standard Yahoo!
Upon entering chat, all chat users are shown a disclaimer from Yahoo! However, we consider the contents of the chat logs to be sensitive, so we only present fully-anonymized statistics. Our data was collected between August and November of In late August, Yahoo! At the same time, Yahoo! In short, these upgrades made the rokms rooms difficult to be accessed for both chat bots and humans.
In mid to late September, both chat bot and third party client developers updated their programs. By early October, chat bots were found in Yahoo! Due to these problems and the lack of chat bots in September and early Wuthout, we perform our analysis on August and November chat logs. In August and November, we collected a total of 1, hours of chat logs. There are individual chat logs from 21 different chat rooms.
The process of reading and labeling these chat logs required about hours. To the best of our knowledge, we are the first in the large scale measurement and classification of chat bots.