SBIR Phase I: St. Gabriel's ROBOCALL Blocker

这项建议的更广泛的影响/商业影响是减少电信欺诈。美国联邦通信委员会称，去年美国人接到了大约260亿个所谓的“机器人电话”，而在2019年3月，创下了50亿个机器人电话的月度新纪录。美国联邦贸易委员会（FTC）报告称，该机构将在2019年收到500多万起关于机器人电话的投诉，比2018年增加30%。自动电话诈骗者依靠的是相对廉价的技术，这种技术可以大规模使用，而且新的骗局越来越聪明，威胁也越来越大。值得注意的是，机器人来电者利用人工智能（ai）、合成语音（所谓的“深度伪造”）和来电显示欺骗，每年造成超过220亿美元的欺诈。这个提议的项目将通过在电信设备层面进行过滤来减少自动电话和相关的欺诈行为。拟议的创新利用了新的去中心化分类账技术，具有区块链加密、记录实时解析和实时机器算法，以阻止机器人呼叫并减少连接延迟。这项提议的目的是大幅减少欺诈电话的数量。该SBIR提案侧重于在设备级进行过滤；最初用于防止自动电话，但可能与其他安全应用程序相关。这项创新利用了区块链的共享存储和内存，在“无信任环境”下运行的能力（由于缺乏跨电信网络在集中自动呼叫列表上的协作），以及区块链加密、人工智能和机器学习、记录实时解析和机器算法方面的进步，以阻止自动呼叫并减少连接延迟。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial impact of this proposal is to reduce fraud in telecommunications. Americans received approximately 26 billion so-called "robocalls" last year, and in March 2019, a new monthly record of 5 billion robocalls was reached, according to the FCC. The Federal Trade Commission (FTC) reports the agency is on pace to receive 5+ million complaints about robocalls in 2019, a 30% increase over 2018. Robocalling scammers rely on relatively cheap technology that works on a large scale, and new schemes are getting smarter and pose a growing threat. Significantly, robocallers leverage artificial intelligence (A.I.), synthesized voice (so-called "deep fakes"), and caller ID spoofing, creating fraud of over $22 billion annually. This proposed project will work to reduce robocalls and associated fraud by filtering at the telecommunications device level.The proposed innovation leverages new decentralized ledger technology with blockchain encryption, real-time parsing of records, and real-time machine algorithms to block robocalls and reduce connection delays. The goal of the proposed innovation is to dramatically reduce the volume of fraudulent phone calls. This SBIR proposal focuses on filtering at the device level; initially applied to prevent robocalls, but potentially relevant for other secure applications. The innovation leverages blockchain's shared storage and memory, ability to operate in a "trustless environment" (due to lack of cross-telecom network collaboration on centralized robocall lists), as well as advances in blockchain encryption, artificial intelligence and machine learning, and real-time parsing of records and machine algorithms to block robocalls and reduce connection delays.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.