VoIP spam or SPIT (spam over Internet telephony) is unsolicited, automatically dialed telephone calls, typically using voice over Internet Protocol (VoIP) technology. VoIP systems, like e-mail and other Internet applications, are susceptible to abuse by malicious parties who initiate unsolicited and unwanted communications, such as telemarketers and prank callers. VoIP calling rates are cheap, and the technology provides convenient, often free tools, such as Asterisk and other applications. The primary underlying technology driving this threat is the Session Initiation Protocol (SIP), which is a standard for VoIP telecommunications. Various techniques have been devised to detect spam calls; some take effect even before the recipient has answered a call to disconnect it. These techniques rely on statistical analysis of the features of the call, such as the originating IP address, or features of the signalling and media messages. Character VoIP spam is characterized as unsolicited calls initiated by voice over Internet Protocol systems. The spammer attempts to initiate a voice session and plays a recorded message if the receiver answers. Robocalls can be delivered automatically using telephony software, such as Asterisk. Mitigation RFC 5039 contains some basic methods for the mitigation of telephony spam over SIP: Whitelists and blacklists Consent-based communications Reputation systems Address obfuscation and limited-use addresses Turing tests, CAPTCHAs, and computational puzzles Payment Legal actions A strong identification of the caller, for example as described in RFC 4474, helps to mitigate SPIT. In a public switched telephone network (PSTN), the Caller ID permits caller identification, but at least the displayed caller ID can be spoofed. Various SPIT mitigation methods and frameworks have been proposed. The vast amount of work on spam detection in emails does not directly apply here because of the real-time nature of the voice calls. A comprehensive survey of Voice over IP Security Research (Chapter IV b) provides an overview. Many proposals focus on the reputation and the behavior of callers, while some focus on machine learning classifiers using features extracted from the control signals or the data of the call. A statistical analysis of the signaling traffic and in particular the call frequency can be used to detect anomalies, to observe and finally to black-list suspicious callers. A semi-supervised machine learning tool creates clusters of similar calls and a human operator can flag any given cluster as being spam. A Voice Spam Detector (VSD) is a multi-stage spam filter based on trust and reputation. The