Securing the wireless emergency alerts system

Modern cell phones are required to receive and display alerts via the Wireless Emergency Alert (WEA) program, under the mandate of the Warning, Alert, and Response Act of 2006. These alerts include AMBER alerts, severe weather alerts, and (unblockable) Presidential Alerts, intended to inform the public of imminent threats. Recently, a test Presidential Alert was sent to all capable phones in the U.S., prompting concerns about how the underlying WEA protocol could be misused or attacked. In this paper, we investigate the details of this system and develop and demonstrate the first practical spoofing attack on Presidential Alerts, using commercially available hardware and modified open source software. Our attack can be performed using a commercially available software-defined radio, and our modifications to the open source software libraries. We find that with only four malicious portable base stations of a single Watt of transmit power each, almost all of a 50,000-seat stadium can be attacked with a 90% success rate. The real impact of such an attack would, of course, depend on the density of cellphones in range; fake alerts in crowded cities or stadiums could potentially result in cascades of panic. Fixing this problem will require a large collaborative effort between carriers, government stakeholders, and cellphone manufacturers. To seed this effort, we also propose three mitigation solutions to address this threat.

Applying Machine Learning and Data Fusion to the "Missing Person" Problem

We present a system for integrating multiple sources of data for finding missing persons. This system can assist authorities in finding children during amber alerts, mentally challenged persons who have wandered off, or person-of-interests in an investigation. Authorities search for the person in question by reaching out to acquaintances, checking video feeds, or by looking into the previous histories relevant to the investigation. In the absence of any leads, authorities lean on public help from sources such as tweets or tip lines. A missing person investigation requires information from multiple modalities and heterogeneous data sources to be combined.<div>Existing cross-modal fusion models use separate information models for each data modality and lack the compatibility to utilize pre-existing object properties in an application domain. A framework for multimodal information retrieval, called Find-Them is developed. It includes extracting features from different modalities and mapping them into a standard schema for context-based data fusion. Find-Them can integrate application domains with previously derived object properties and can deliver data relevant for the mission objective based on the context and needs of the user. Measurements on a novel open-world cross-media dataset show the efficacy of our model. The objective of this work is to assist authorities in finding uses of Find-Them in missing person investigation.</div>

Applying Machine Learning and Data Fusion to the "Missing Person" Problem

We present a system for integrating multiple sources of data for finding missing persons. This system can assist authorities in finding children during amber alerts, mentally challenged persons who have wandered off, or person-of-interests in an investigation. Authorities search for the person in question by reaching out to acquaintances, checking video feeds, or by looking into the previous histories relevant to the investigation. In the absence of any leads, authorities lean on public help from sources such as tweets or tip lines. A missing person investigation requires information from multiple modalities and heterogeneous data sources to be combined.<div>Existing cross-modal fusion models use separate information models for each data modality and lack the compatibility to utilize pre-existing object properties in an application domain. A framework for multimodal information retrieval, called Find-Them is developed. It includes extracting features from different modalities and mapping them into a standard schema for context-based data fusion. Find-Them can integrate application domains with previously derived object properties and can deliver data relevant for the mission objective based on the context and needs of the user. Measurements on a novel open-world cross-media dataset show the efficacy of our model. The objective of this work is to assist authorities in finding uses of Find-Them in missing person investigation.</div>

AMBER Alert Effectiveness Reexamined

Prior research based on limited datasets has suggested AMBER Alerts do little to prevent harm to child abduction victims. However, to investigate the possibility of recent improvements in AMBER Alert performance, the authors examine a sample of 472 AMBER Alerts issued over a 3-year period from 2012 to 2015, using available media accounts to code for relevant case information. The findings are consistent with prior research questioning AMBER Alert effectiveness: The crucial variable predicting Alert outcomes is abductor relationship to the victim, not AMBER Alert “performance.” Furthermore, cases involving “successful” AMBER Alerts are comparable on measurable factors to AMBER Alert cases where the child was recovered safely but the Alert played no role, suggesting both categories of cases involved little real risk. Implications for interpreting the viability of the AMBER Alert concept, public discourse regarding its contribution to child safety, and larger implications for crime control policy are discussed.

Stranger Danger

Starting in the late 1970s, a moral panic concerning child kidnapping and exploitation gripped the United States. For many Americans, a series of high-profile cases of missing and murdered children, publicized through an emergent twenty-four-hour news cycle, signaled a “national epidemic” of child abductions perpetrated by strangers. Some observers insisted that fifty thousand or more children fell victim to stranger kidnappings in any given year. (The actual figure was and remains about one hundred.) Stranger Danger demonstrates how racialized and sexualized fears of stranger abduction—stoked by the news media, politicians from across the partisan divide, bereaved parents, and the business sector—helped to underwrite broader transformations in US political culture and political economy. Specifically, the child kidnapping scare further legitimated a bipartisan investment in “family values” and “law and order,” thereby enabling the development and expansion of sex offender registries, AMBER Alerts, and other mechanisms designed to safeguard young Americans and their families from “stranger danger”—and to punish the strangers who supposedly threatened them.

Simulating “Peripheral Harm” as an AMBER Alert Issuance Criterion: Implications for Anticipating Threats to Child Safety

Although there is some limited research on the effectiveness of the America’s Missing: Broadcast Emergency Response (AMBER) Alert system, to date, there has been no research specifically examining the viability of prospective AMBER Alert issuance criteria. Using data acquired from various media accounts of 446 AMBER Alerts issued in the United States and Canada, we examine how well “peripheral harm” (harm to someone other than the abducted child during the course of the abduction) predicts subsequent harm to the abducted child. Counterintuitively (from the perspective of AMBER Alert issuance decision making), peripheral harm or threat is negatively associated with harm to the victim in cases involving an AMBER Alert. Furthermore, this negative finding is spurious, and is primarily driven by the fact that, disproportionately, the abductors who commit “peripheral harm” in AMBER alert cases are parents and other family members of the child who are presumably unlikely to harm child relatives despite whatever violence they might commit (or threaten) against others. We discuss the implications for the use of peripheral harm as an AMBER Alert issuance criterion, the empirical evaluation of the system, and the public discourse surrounding the AMBER Alert system and its relationship to child protection in general.