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There are two main components in our classification system: 1 an entropy classifier and 2 a machine-learning classifier. 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 ].
By this method, a template with several synonyms for chat words can lead to thousands of random messages. Section 4 describes our chat bot classification system. A chat bot is a bot that interacts with rabdom chat service to automate tasks for a human, e. In addition, our examiner checks the content of URLs and typically observes multiple instances ramdom the same chat bot, which further improve our classification accuracy. The focus of our measurements is mainly on short term statistics, as these statistics are most likely to be useful in chat bot classification.
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randim However, with the commercialization of the Internet, the main enterprise of chat bots is now sending chat spam. In this paper, we first conduct a series of measurements on a large commercial chat network. For example, IRC, a classic chat system, implements a of IM-like features, such as presence and file transfers, in its current versions.
So far, the efforts to combat chat bots have focused on two different approaches: 1 keyword-based filtering and 2 human interactive proofs. However, very active users in Web-chat and automated scripts used in IRC may send more data than they receive. Although having been used by botnets as command and control mechanisms [ 112 ], IRC and other chat systems do not play an irreplaceable role in botnets.
The criteria for being classified as bot are as follows. The log-based classification process is a variation of the Turing test. The first is the lack of the intelligent responses required for the human label.
Chat spam shares some similarities with spam. Millions of people around radnom world use Internet chat to exchange messages and discuss a broad range of topics on-line.
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. Although a Turing test is subjective, we outline a few important criteria. In [ 23 ], Mannan et al.
AS [ 35 ] worms appeared in the August chat logs, and 23 W 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. There are 12 W The proposed classification system consists of two components: 1 an entropy-based classifier and 2 a machine-learning-based classifier.
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The focus of our measurements is on public messages posted to Yahoo! In AugustYahoo! The users connect to a chat server via chat clients that support a random chat protocol, and they may browse and many chat rooms featuring a variety of topics. In our classification process, the examiner observes a long conversation between a test subject a randmo chat bot and one or more third parties, and then decides if the subject is a human or a bot chat.
The two main types of triggering mechanisms observed in our measurements are timer-based and response-based. Rzndom are two approaches that chat bots use to distribute spam links in chat rooms.
There are online petitions against both AOL and Yahoo! Other potential abuses of bots include spreading malware, phishing, booting, and similar malicious activities. Internet chat is also a unique networked application, because of its human-to-human interaction and low bandwidth consumption [ 9 ]. Due to these problems and the lack of chat bots in September and early October, we perform our analysis on August and November chat logs. AS worms appeared in the November chat logs.
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. Second, chat bots use various synonym phrases to avoid obvious keywords. In return, some new features that make the IM systems more user-friendly have been back-ported to the chat systems.
The former determines message timing, and the latter determines message content. 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.
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According to our observation, the main activity of chat bots is to send spam links to chat users. There are individual chat logs from 21 different chat rooms.
The first is to post a message with a spam link directly in the chat room. Our measurements also cbat that human behavior is more complex than bot behavior, which motivates the use of entropy rate, a measure of complexity, for chat bot classification.
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The remainder of this paper is structured as follows. To create such datasets, we perform log-based classification by reading and labeling a large of chat logs. A response-based bot sends messages based on programmed responses to specific content in messages posted by other users. Chat spam shares some similarities with spam.
A timer-based bot sends messages based on a timer, which can be periodic i. Like spam, chat bot contains advertisements of random services and counterfeit goods, and solicits human users to click spam URLs. There is considerable overlap between chat and chat messaging IM systems, in terms of protocol and user base.
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Moreover, the entropy classifier helps train the machine-learning classifier. The former determines message timing, and the latter determines message content. Although very successful, Bayesian-based spam detection techniques still can be evaded by carefully crafted messages [ 402218 ]. Third-party chat clients filter out chat bots, mainly based radom key words or key phrases that are known to be used by chat bots.