SBIR-STTR Award

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.

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to preserve the integrity of communication channels for business enterprises, financial institutions, and consumers. Attackers operate like businesses by investing in campaigns and generate returns by using stolen credentials to gain network access, inject ransomware, or commit direct fraud. The innovation aims to prevent $4 B a year in financial scams through robust and enduring countermeasures. 90% of large enterprise customer breaches start from email or p2p/SMS messages that trick customers and employees into revealing sensitive information. An average phishing attack spans 21 hours between the first and last victim and the detection of each attack occurs an average 9 hours after the first victim. This gives attackers a window of opportunity during which most of the damage is done. This project seeks to identify and protect against such attacks through a real-time machine learning system. This SBIR Phase II project develops a method for storing data in a blockchain in a high-performing manner, to ensure quality training of ML/AI models, and production level inference. The innovation addresses the challenge of data drift and adversarial AI attacks where the provenance of the data is unknown. The Phase II project also leverages swarm intelligence as a new consensus mechanism specifically designed for more democratic online group-to-group collaboration in a distributed system. The project's innovations will expand the understanding of telecommunications, the economics of smishing and vishing, and 5G security.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.