A Plan For Robocalls: Robosmelter

Transcription

1 A Plan For Robocalls: Robosmelter Ben Klang, Sene Sorrow Mojo Lingo LLC January 17, 2013 Submitted to the Federal Trade Commission s Robocall Challenge Abstract The rise of Voice over IP (VoIP) technologies has lead to an explosion in the utility and ease of communication offered by the telephone network. However, every powerful tool has the potential to become a powerful weapon. Illegal robocall telemarketers have seized on this and are using the dramatically low cost of VoIP calling to barrage consumers with illegal robotic calls. This document outlines an approach, called Robosmelter, using software and machine learning techniques to dramatically reduce the number of such illegal calls that ring the consumer s phone, while not interfering with the consumer s normal use and enjoyment of his or her phone.

2 Terms Consumer: An individual (not a business or other organization) who has telephone service, whether landline or mobile User: A consumer who has activated our proposed software application to filter their calls Carrier: A telecommunications service provider, including landline telephone, mobile telephone, or Voice over IP (VoIP) telephone service. Illegal Robocalls: Illegal automated telephone calls directed at Consumers. This excludes legal automated telephone calls, such as those by political candidates, charities, surveys, and other legally permissible calls. Robosmelter: Our proposed software system for identifying, learning, and blocking illegal robocalls. Robosmelter Proposal Summary Illegal robocalling is a complex problem that will be impossible to solve completely without sweeping changes to the technology, public policy, and cost/revenue structures that underpin

3 the public telephone network. However, using modern technology, we can enable consumers to more quickly identify and block illegal robocalls before the call rings the consumer s device. The decision to block any given call is made using a combination of technologies that are familiar to those who have worked to fight unsolicited commercial honeypots, statistical and crowdsourced analyses, rolling blacklists, exempting whitelists, and tarpitting. Just as is true with , for the technology to be effective it must be accurate and it must be very easy to use for the consumer. This proposal focuses on the following four goals: 1. It should be easy for the consumer to adopt and use 2. It should not require unreasonable effort by telephone carriers to install and maintain 3. It should work on any telephone connected to the PSTN (landline, mobile, voip) 4. It should stop a large percentage of illegal / spam calls today The blocking mechanism is designed to be under the full control of the user;; he or she may enable or disable it at any time. Control of Robosmelter by the user may be via text message

4 interaction (if using a mobile phone) or via web browser from any internet connected computer. Further, several optional blocking measures may be selected or deselected to change how calls are selected to be blocked. But even if the user takes no further action than simply enabling the service (which will be no more difficult than adding a number to the FTC s Do Not Call Registry), the application will still be able to stop a significant portion of illegal robocalls. Because the information about illegal robocalls is shared anonymously throughout the system, the entire system benefits from the activities of the active users. The proposed software works by analyzing the behavior of all calls that reach the users. Based on their behavior, we assign a score indicating the probability that each individual call is an illegal robocall. The scores for all users of Robosmelter are collected and merged in real-time to calculate a score indicating the probability that calls from a given telephone number are likely to be a illegal robocall. Once the score rises above a predetermined (and adjustable) threshold, further calls from that number to users of the application are stopped by the application. The authors of this proposal realize that most countermeasures are possible to circumvent by organizations which have no regard for the law. In the section Closing the Loopholes we discuss longer term solutions to mitigate the weaknesses that are left by this proposal. We have not included them in the body of the main proposal because of the effort required to deploy them. The focus is on improving the current situation, while opening the door to deeper, more fundamental changes that may eliminate illegal robocalls entirely in the future. One last note on the validity of using caller ID as a blocking source: based on complaint data gathered by the FTC and shared as a part of this Robocall Challenge, we, the authors of this document, observed that most robocallers reuse caller ID extensively. Of approximately 701,821 complaints during the year 2012, approximately 57,000 reported no caller ID. This may be because the consumer failed to report the information, or because the robocaller made the call without supplying caller ID. For the top 100 individual caller IDs, there were more than 1,000 complaints made for each number. The top 10 individual caller IDs accounted for over 50,000 complaints, with the absolute top offender single-handledly being responsible for 16,234 complaints alone. Since we know that not all consumers report illegal robocalls, just blocking these top 100 caller IDs would result in more than a 60% reduction of the number of robocalls received by consumers. Based on this information (as seen in the graph) the majority of complaints came from a strong minority of distinct caller IDs.

5 Assumptions 1. While Caller ID is trivially spoofed, it remains the only method by which calls may be identified at the terminating telephone carrier before they reach the consumer. Further, empirical evidence and data from the FTC indicates that Caller ID spoofing is used today primarily to hide the identity of the caller, and is often not changed from one individual call to the next. Thus, if we can determine the current Caller ID for a given illegal robocall campaign, we can stop a large number of calls from reaching consumers by blocking calls from that number for a period of time. 2. Originating carriers have little incentive to stop calls leaving their network, and in fact may open themselves to liability for failing to complete calls as required by contract or law. Thus, the most effective place to install a call block is within the consumer s telephone carrier (where the consumer/call recipient may directly authorize such blocking) or in the consumer s device. 3. Due to the requirements of scale, illegal robocall telemarketers are vulnerable to any increase in the per-call or per-minute costs associated with their operation. Any measures taken to drive up those costs negatively impact their ability to function. Technical Description The central goal of the service is to block a call before it rings the consumer s device. To make such a decision requires collecting a large amount of information and processing that information in real-time. There are several ways we can collect information about whether a given call is an illegal robocall or not: By asking the consumer (either during or after a call has taken place) By examining patterns in call behavior (call source, duration) By examining the call contents (identifying audio characteristics, repetitive phrases) To collect the required information and take the appropriate action, we propose installing software that runs inside the carrier s network. This software may be operated by the carrier itself, or by a third party service provider. In both cases, it is important to note that the software is operating at the direction of the consumer, not the carrier or the third party service provider. This distinction allows the software to act in the consumer s best interest and alleviates any potential liability concerns that may be raised by carriers refusing to connect any calls. The application works in three phases: 1. Identification 2. Learning 3. Blocking (with optional Tarpitting) In the first phase, Identification, the application is attached to the user s account and begins observing phone calls that the user receives. When a call comes in, the application looks for several behaviors during the course of that call which may indicate an illegal robocall. Several

6 key behaviors have already been identified, including audio characteristics of the call, short call duration resulting from the user hanging up, and others. The information and metrics applied to the call come from a shared database of all users of the application. In this way users will be able to share information about illegal robocalls quickly and effortlessly. Robosmelter then assigns a score for the call, based on these and other criteria, as to how confident it believes the current call is likely to be an illegal robocall. In the second phase, Learning, users may provide feedback to the application to correct and improve the scores assigned by the application to individual calls. For example, if a user receives a call from caller ID and the application assigns a score of 40% (below the blocking threshold of 65%), the user may notify the application that the call was, in fact, an illegal robocall. The application takes this information and updates its database to indicate that the observed call characteristics and the caller ID was associated with an illegal robocall. As other users do the same, the score for calls coming from that caller ID will go up. Over time this will cause the score to rise above the blocking threshold, and the application will no longer allow calls from this number to reach users. By using crowdsourcing techniques, we can determine which calls to block from reaching users of Robosmelter. In the third phase, Blocking, the combined score from all users within the system is consulted each time a call is attempted to a user. If the score is above a predetermined threshold (or, a threshold as specified directly by the user), the call is blocked. Optionally, Robosmelter may tarpit the call. This means that the line will be answered by the application (without passing the call along to the user), and the line will be left open as long as it is not disconnected by the caller. In this way, illegal robocallers are forced to use more time and more channels talking to dead air. This has the effect of raising their incremental cost and consuming channels, reducing the number of calls they are able to make. Advanced users may wish to enable additional, more aggressive blocking features. While we anticipate that the default choices will dramatically reduce the number of illegal robocalls that actually make it to a consumer s device, there are additional policies that consumers may elect to more aggressively block calls. These options include: Blocking toll-free numbers Blocking calls without caller ID information Blocking calls from foreign countries Honeypots and Fully Automatic Detection Since illegal robocallers often dial sequential telephone numbers looking for more targets, we may use this behavior to help identify certain illegal robocallers. Carriers may assign unallocated numbers to be handled by Robosmelter. Whenever the system receives a call to one of these parked numbers, it can emulate a human and listen to the call. By analyzing the call audio, a fully automatic decision may be made as to whether the call is robotic. By using speech recognition technologies, keywords within the call may be identified which can be used

7 to influence the determination whether the call is an illegal robotic sales call. For example: If a particular parked number has been out of service for more than 18 months, which corresponds to the legal robotic calling period for legal robocalls from business that have a relationship with the consumer, and the system can detect a sales attempt, it can assign a high probability that the call is illegal and assign a score to the caller ID appropriately. Alternatively, Robosmelter can look for the same recording to be played to many parked telephone numbers. Once identified, such recordings may be reviewed by a human (either a person responsible for the maintenance of the system, or perhaps even by enlisting support from users of the system directly) and use their determination to raise or lower the score of that call. Exempting Legal Robotic Calls A careful read of the above paragraphs may lead to the conclusion that users will begin to block legal robocalls, such as those by political candidates or charitable organizations. This is definitely possible, as the system as described lacks an ability to tell the difference. To prevent it from blocking legal calls, a whitelist mechanism would be instituted. In order to ensure that legal robocalls are passed through the application, legitimate robocallers would be required to register their caller ID and then to make all their calls from the registered number. The application s whitelist would take precedence over the score and over-all user-activated blocking options, allowing these legal robocalls to be completed. Deployment Considerations The easiest place to gather the information described above is within the consumer s direct telephone service provider. Collecting this information usually requires the consent of the consumer so as not to run afoul of wiretap laws. It is our proposal that software be installed into the carrier s network to enable consumer-approved monitoring of phone calls to collect that information. Such software is already commercially available;; in the United States, AT&T has already deployed what they term Call Management APIs which provide sufficient access to mobile phone calls to enable this kind of service. Small mobile phone networks, known in the industry as Mobile Virtual Network Operators (MVNOs) also have the ability to add applications that interact directly with the consumer s telephone calls. The advantage of MVNOs is one of size and agility: new services may be prototyped and deployed within months or even weeks. While not as seamless, other commercial services such as Google Voice and over-the-top services such as Tropo and Twilio allow applications to broker telephone calls, enabling similar analysis. Closing the Loopholes The proposed solution relies heavily on caller ID as the mechanism by which calls may be identified. Data from the Federal Trade Commission backs up the assertion that caller ID is a valid way to identify likely illegal robocalls. However, as caller ID is easy to spoof, further measures (beyond the technology-based focus of this proposal) should be considered in an effort to reduce ease of falsifying caller ID. These measures may include: 1. Fines for carriers who do not take reasonable measures to ensure that valid caller ID is

8 transmitted by telephone calls originating in their network. This shifts some liability to originating carriers, providing financial incentive to ensure that caller ID is valid at the point it enters the network 2. Stronger, possibly cryptographic, verification for Voice over IP calls. Several proposals exist here. See the slides from the FTC Robocall Summit Panel for an overview of some options: f 3. Adding additional secured metadata to telephone calls to reduce the dependence on caller ID as the only information about the source of a call. This mostly applies to Voice over IP calling (potentially in the form of SIP headers or other in-band information) Contact Information Ben Klang, Principal/Technology Architect Sene Sorrow, General Manager Mojo Lingo LLC