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SPAM over IP Telephony (SPIT) is an emerging challenge as VoIP solutions gain popularity. This comprehensive framework outlines five stages for SPIT prevention, aiming to minimize false positives, user interaction, and caller inconvenience across various environments. Techniques include blacklists, whitelists, anomaly detection, computational puzzles, and Turing tests to verify caller identity and intent. As the potential for productivity disruption grows with automated calls, organizations must implement robust measures to protect users from SPIT and maintain the integrity of their communication systems.
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Felipe Santos Manoj Deshpande ECE 4112 – Internetwork Security Georgia Institute of Technology SPAM over IP Telephony (SPIT) Identification and prevention Techniques
Background • SPAM considered one of biggest problems in Internet • SPIT is expected to become a major issue in the next few years with increasing deployment of VoIP solutions • Potential for productivity disturbance is much greater than SPAM
Background • Definition: The transmission of unsolicited calls over Internet telephony (VoIP) • “SPITTERS” will forge their identities • SPITTING agent capable of placing hundreds of simultaneous automated calls • SIP is not voice only, but applies to Instant Messaging and video as well
SPIT Prevention Framework • Goals: • Minimize false positives & negatives • Minimize callee interaction in identifying SPIT • Minimize inconvenience to caller • General enough to work in different environments (work, home, etc) and cultures
SPIT Prevention Framework • 5 Stage Approach: • Stage 1: no interaction w/ users • Blacklist, Whitelist, Graylisting, Circles of Trust, Pattern / AnomalyDetection • Stage 2: caller interaction • Computational Puzzles, SenderChecks, Audio CAPTCHAS (Turing Tests)
SPIT Prevention Framework • 5 Stage Approach (continued): • Stage 3: feedback before call • Manual authorization to receivecall and/or authenticate user • Stage 4: during the call • Content analysis (not currentlyviable) • Stage 5: feedback after call • Reputation System, Limited-Use Address, Payments at Risk, Litigation
SPIT Prevention Techniques • Blacklists & Whitelists • Pros: • Simple implementation • Effective (users in whitelist will always be allowed through and vice versa) • Cons: • Manual data gathering by user or global service required to build such lists • SPITTERS can easily spoof identity and bypass lists
SPIT Prevention Techniques • Circles of Trust • Inter-domain connections are checked before a call is forwarded. Each domain control its users • Pros: • Efficient • Even if a user misbehaves, easy to identify user • Cons: • Requires a priori inter-domain agreements/validation • Relatively complex implementation
SPIT Prevention Techniques • Pattern/Anomaly Detection • Statistical analysis of a user’s calling behavior based on studies that identify “normal” call behavior. • Pros: • Potentially most acurate • Mature methodology • Cons: • Requires monitoring agent to keep track of user behavior • Never before implemented to voice calls
SPIT Prevention Techniques • Graylisting • Consists of calculating a gray level for each and every caller • Gray level determines how likely a caller is to be a SPITTER
SPIT Prevention Techniques • Graylisting (continued) • Progressive Multi Gray-Leveling (PMG) • Considers two levels per caller: short-term level and long-term level • Short-term level • considers the number of calls a given user places within a short period of time (i.e. 10 min) • Level changes rapidly - Prevents DoS attacks • Long-term level • considers the number of calls a given user places within a long period of time (i.e. 10 hours) • Level changes slowly – prevents SPITTER from regaining calling rights
SPIT Prevention Techniques • Graylisting (continued) • Progressive Multi Gray-Leveling (PMG) (continued) • A threshold is established, such that if (short-term level + long-term level) > ThresholdA user’s outgoing call is blocked
SPIT Prevention Techniques • Graylisting (continued) • Pros: • Effective caller limiting approach • Relatively simple implementation • Makes a SPITTER’s task much harder • Cons: • Legitimate users can potentially have calls blocked just for placing too many calls within a given time frame.
SPIT Prevention Techniques • Computational Puzzle • Verify a caller’s “willingness” to place the call by imposing that the client solves a digital puzzle/calculation prior to call establishment • Caller must spend at least a given minimum period of time to ensure solution is not “guessed” • Pros: • Limit a SPITTER’s calling rate by adding required computational overhead to establish • Cons: • Increased overhead for call establishment • Could be relatively easily circumvented
SPIT Prevention Techniques • Sender Check • Verify/authenticate a caller by actively consulting its domain • Equivalent of Sender Policy Framework (SPF) and Sender ID in email • Pros: • Originating domain certifies its users • Prevents user ID spoofing • Cons: • Relies on remote domain information that may not be correctly implemented or updated
SPIT Prevention Techniques • Turing Test • Differentiate between automated computer placed calls (likely SPIT) and calls placed by human beings • Uses Audio Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAS) • Pros: • Quickly and easily identify automated vs. human calls • Cons: • Increased overhead for connection establishment • Could potentially block non-SPIT automated calls (banks, package delivery notifications, reverse 911, etc)
SPIT Prevention Techniques • Consent-Based Communication • User authentication / identity verification • Calleeauthoizes caller a priori with a previously exchanged key or passphrase • Pros: • SPIT is completely blocked, since only authorized callers can place call to user • Cons: • Any new caller who wishes to contact a user must request and receive the shared key a priori
SPIT Prevention Techniques • Content Filtering • Process call content to detect SPIT as done in SPAM filters • Pros: • If viable, would be the most accurate technique • Cons: • Not viable / implementable. Although there exist DSP algorithms to analyze audio data and convert audio waveforms to ASCII text, process is not real-time and call contents are not available for processing until after the call is actually placed.
SPIT Prevention Techniques • Reputation System • Centralized reputation score based on user behavior and other users’ feedback • Pros: • Centralized global resource to identify SPITTERS • Cons: • Requires protocol standardization for feedback framework
SPIT Prevention Techniques • Payments at Risk • Require a refundable payment for each call from an unknown party. The payment is only refunded if the caller was not a SPITTER. • Pros: • Increase cost / decrease profitability of SPIT • Cons: • Quite unrealistic scenario, since a standardized framework would be required for feedback and payment charging and many VoIP services are free and fully p2p
Lab Exercises • Students will: • Configure and setup the VoIP testbed • Establish an authenticated VoIP call and notice a SPITTER’s inability to contact a user that requires caller authentication • Create a SPIT message • Place an automated SPIT call by capturing and replaying the SPIT message created above • Place an automated SPIT call with a spoofed ID
Exercise Results • User Authentication (with shared keys)
Exercise Results • User Authentication (no shared keys)
Exercise Results • Creating SPIT Message & Generating Automated SPIT Call
Exercise Results • Spoofing Caller ID
References • J. Quittek, S. Niccolini, S. Tartarelli, and R. Schlegel, “Prevention of Spam over IP Telephony,” NEC Technical Journal, vol. 1, no. 2, Feb., pp. 114-119, 2006. • D. Shin and C. Shim, “Voice Spam Control with Gray Leveling,” Proceedings of 2nd VoIP Security Workshop, Washington DC, June 1-2 2005. • F. Hammer et al. “Elements of Interactivity in Telephone Conversations,” Proceedings of 8th International Conference on Spoken Language Processing (ICSLP/INTERSPEECH 2004), Vol3, pp.1741-1744, Jeju Island, Korea, Oct. 2004.