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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.

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Chat bots exploit these on-line systems to send spam, spread malware, and mount phishing attacks. Since the detection of spam can be easily converted into the problem of text classification, many content-based filters utilize machine-learning algorithms for filtering spam. Fourth, and most interestingly, chat bots replay human phrases entered by other chat users. The focus of our measurements is on public messages posted to Yahoo!

What relationship exists between the written and spoken word? However, the usage and behavior of bots in botnets are quite different from those of chat bots.

However, with the commercialization of the Internet, the main enterprise of chat bots is now sending chat spam. First, chat bots introduce random characters or space into their messages, similar to some spam e-mails. The main criterion for being labeled as human is a high proportion of specific, intelligent, and human-like responses to other users.

Roo,s contrast, chat bots are automated programs deed mainly to interact with chat users by sending spam messages and URLs in chat rooms.

Moreover, given that the best practice of current artificial intelligences [ 36 ] can rarely pass a non-restricted Turing test, our classification of chat bots should be very accurate. The drawback with this approach is that it cannot capture those unknown or evasive chat bots that do not use the known key words or phrases. This is mainly because its console-like interface and command-line-based operation are not user-friendly.

Among chat bots, we further divide them into four different groups: periodic bots, random bots, responder bots, and replay bots. Based on the measurement study, we propose a classification system to accurately distinguish chat bots from humans.

With this in mind, we outline some related work on IM systems. The behavior of malware-spreading chat bots is very similar to that of spam-sending chat bots, as both attempt to lure human users to click links. There are many different kinds of text obfuscation schemes.

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While the entropy classifier requires more messages for detection and, thus, is slower, it warcdaft more accurate to detect unknown chat bots. Although a Turing test is subjective, we outline a few important criteria.

Thanks to a wireless keyboard set up in front of his body, the speaker can move about freely in interior and exterior spaces and concentrate on the written conversation. A chat service with a large user base might employ multiple chat servers.

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Section 2 covers background on chat bots and related work. The two main types of triggering mechanisms observed in our measurements are timer-based and response-based. Although IRC has existed for a long time, it has not gained mainstream popularity.

Like spam, chat spam contains advertisements of illegal services and counterfeit goods, and solicits human users to click spam URLs. The logging of chat messages is available on the standard Yahoo! In our classification process, the examiner observes a long conversation between a test subject a possible chat bot and one or more third parties, and then decides if the subject is a human or a chat bot. In return, some new features that make the IM systems more user-friendly have been back-ported to the chat systems.

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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. The written conversation becomes legible by the people in ones proximity. In addition, our examiner checks the content of URLs and typically observes multiple instances of the same chat bot, which further improve our classification accuracy.

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 worls architectures [ 39 ].

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. The log-based classification process is a variation of the Turing test.

To the best of our knowledge, we are the first in the large scale measurement and classification of chat bots. The first is to post a message with a spam link directly in the chat room.

Section 3 details our measurements of chat bots and humans. We conduct experimental tests on the classification system, and the validate its efficacy on chat bot detection. AS worms appeared in the November chat logs. Our logs also include some examples of malware spreading via chat rooms.