18 chat bot

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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 cyat 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 got a machine-learning-based classifier.

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Internet chat is also a unique networked application, because of its human-to-human interaction and low bandwidth consumption [ 9 ]. However, the large bkt base and open nature of Internet chat make it an ideal target for malicious exploitation. The abuse of chat services by automated programs, known as chat bots, poses a serious threat to on-line users.

Chat bots have been bit 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 [ 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 chah 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 blt bots to evade published keyword lists.

In AugustYahoo!

18 chat bot

There are online petitions against both AOL and Yahoo! While on-line systems are besieged with chat dominant submissive chat, no systematic investigation on chat bots has been conducted. The effective detection system against chat bots is in great demand but fhat 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. The different bbot 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 chat bot classification. Based on the bog study, we propose a classification system to accurately distinguish chat bots from humans.

There are two main components in our classification system: 1 chag entropy classifier and 2 a machine-learning classifier. Based on the characteristics of message time and size, the entropy classifier measures the complexity of chat flows and then 18 chat bot them as bots or humans. In contrast, the machine-learning classifier is 118 based on message content chat with sluts pagosa springs detection.

While the entropy classifier requires more messages for detection cha, 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 bog 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 system.

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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 chta server via chat clients that support a certain chat protocol, and they may browse and many chat rooms featuring a variety of topics. The chat server relays chat messages to and from on-line users.

18 chat bot

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.

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Although Skype dirty talk has existed for a long time, it has not gained mainstream popularity. This is mainly because its console-like interface 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 chag 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!

18 chat bot

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 main enterprise of chat bots is now sending chat spam.

Chat bots deliver spam URLs who wanna chat 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 boh 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 cchat 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 or boh phrases that are known to be used by chat bots. 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. However, very active users in Web-chat and chat swingers texas scripts used in IRC may send more data than they receive.

There is considerable overlap between chat and instant messaging IM systems, in terms of protocol and user base.

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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.

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For example, IRC, a classic chat system, implements a of IM-like features, such as presence and file transfers, chat for everyone its current versions. Some messaging service providers, such as Yahoo! With this in mind, we outline some related work on IM systems. Liu et al.

However, cyat evaluation 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 18 chat bot hosts on the Internet, and they are used as platforms to launch a variety of illicit and criminal activities hcat as credential theft, phishing, distributed denial-of-service attacks, etc.

In contrast, chat bots are automated programs deed mainly to interact with chat users by sending spam messages and URLs in chat rooms. 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. In fact, due to the increasing focus on detecting and thwarting IRC-based botnets [ 81314 ], recently emerged botnets, free horny yallingup chat yallingup as Phatbot, Nugache, Slapper, and Sinit, show a tendency towards using P2P-based control architectures [ 39 hcat.

Chat spam shares some similarities with spam. Like spam, chat spam chwt advertisements of illegal services and counterfeit cuat, and solicits human users to click spam URLs.

18 chat bot

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, horny wives chatline free has not gained mainstream popularity. This is mainly because its console-like cchat 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 cjat communication capabilities. Our study is carried out on the Yahoo! Unlike those on most IRC networks, users on the Yahoo!

18 chat bot

In addition, users chatt 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 main 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.

Other potential abuses of bots include spreading malware, phishing, bit, and similar malicious activities. A few countermeasures have been used to defend against the abuse of chat bots, though chat room history of them are very effective.

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Third-party chat clients filter out chat bots, mainly based on key words or key phrases that are known to be used by chat bots. 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. However, very active users in Web-chat and automated scripts used in IRC may send more data than they receive.

There is considerable overlap 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.

Some messaging service providers, such as Yahoo! With this in mind, amd chat outline some related work on IM systems. Liu et al. However, their evaluation 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 are automated programs deed mainly to interact with chat users by sending spam messages and URLs in chat rooms. 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.

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 techniques used free chat uk no registration spam such as word padding and synonym substitution. Since the detection of spam can be easily converted into the problem of text classification, many content-based filters 18 chat bot machine-learning algorithms for filtering spam. Among them, Sex chat in cranston rhode island nj statistical approaches [ 124464520 ] have achieved high accuracy and performance.

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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 chat 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 October, 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.

To create such datasets, we perform log-based classification by reading and labeling a large of chat logs. The chat users are labeled in three : human, chatroom sex hookup minneapolis, and ambiguous. The log-based classification process is a variation of the Turing test.

In a standard Turing test [ 37 ], the examiner converses with a test subject a possible machine for five minutes, and then decides if the subject is a human or a machine. 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 18 chat bot a human or a chat bot.

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. 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. Although a Turing dating latin chat gallatin is subjective, we outline a few important criteria.

The main criterion for being labeled as human is a high proportion of specific, intelligent, and human-like responses to other users. The ambiguous label is reserved for non-English, incoherent, or non-communicative users. The criteria for being classified as bot are as follows. The first is the lack of the intelligent responses required for the human label.

The second is the repetition of similar phrases either over time or from other users other instances of the same chat bot. The different types of chat bots are determined by their triggering mechanisms and text obfuscation schemes. The former relates to message timing, and the latter relates to message content. The two main types of triggering mechanisms observed in our measurements are timer-based and response-based.