A Better Defensive Line

A Better Defensive Line

How U.S. agencies are working toward a more appropriate security line

If I asked you to name the greatest defensive line of all time, what immediately pops into your head? The Steel Curtain of the Pittsburgh Steelers? Maybe the Denver Broncos Orange Crush? While these defensive linemen were certainly legendary, we all recognize that they weren’t infallible. It ultimately required a team effort to win the day.

The same is true when it comes to border security. When the ancient Chinese constructed the Great Wall of China—an architectural feat that stretched across more than 13,000 miles of rugged country and steep mountains—the wall was by no means their only line of defense. The provinces also relied on the protection of a highly skilled military force. Fast forward 2,300 years and border security in countries like the United States not only rely on highly trained law enforcement agencies and physical barriers, but a sophisticated array of technologies designed to prevent unlawful entry.

In the United States, technology has become the essential force multiplier. With nearly 6,000 miles of border with the neighboring north and south, it would be impossible to hire enough border personnel to protect every mile. So in addition to fences, walls and vehicle blockades, the U.S. increasingly relies on a strategic mix of technologies, sensors, radar and thermal detection, biometrics, and video analytics to safeguard the integrity of our borders.

Deploying Networked Sensors and Detectors

Post 9/11, the Department of Homeland Security (DHS) has more than tripled the number of Border Patrol agents along U.S. northern and southern frontiers. They’ve also greatly increased the number of sensors embedded in roads and wooded areas near crossings to detect vehicles and people attempting to circumvent legitimate points of entry. Depending on how they’ve been programmed, they can trigger different actions—sirens, floodlights, loudspeaker announcements, video recording and alerts to border patrols. Some of the sensor and detector technology you can find currently being deployed at U.S. borders are:

Passive-infrared (PIR). PIR sensors sense the movement of things that radiate heat—people, animals, vehicles and other objects. The typical detection range for PIR sensors is relatively short—approximately 33 feet (10 meters). That’s about one-tenth of the length of a football field or about half the length of a bowling alley lane.

Thermal. Thermal detectors capture the heat signature of objects providing border security with sufficient detail to discern the difference between people, animals and things. Their detection range is considerably longer than PIR, though the field of view is somewhat narrower.

Radar. Designed primarily to protect against ground targets, radar detectors analyze the electromagnetic pulses bouncing off objects to determine their position, distance and velocity, as well as the direction of their movement (towards or away from the radar). Mid-range radar detectors can spot a moving target up to 164 feet (50 meters) away. That’s almost half the length of a football field. Long-range radar detectors can distinguish a walking target up to three-quarters of a mile away.

Another notable thing about radar technology is that it can detect multiple targets simultaneously. The more advanced models can also provide important GIS data about the target. Geographic Information System (GIS) data is three dimensional in nature, encompassing latitude and longitude, speed and distance. If required, it can also provide the target’s altitude or elevation.

Drone detection radar. With the increasing incidents of drug traffickers using drones to drop ship contraband across the border, DHS is beginning to explore radar technology specifically designed to recognize and detect and small drones up to twothirds of a mile in the sky. They continuously monitor the drone’s changing GPS coordinates and can transmit that data to video cameras to assist in the tracking of the object’s movement.

Generally these technologies can only detect an object of interest. But when they’re augmented with video they can be interrogated to provide recognition and identification affording operators true situational awareness of their environment.

Tying in Surveillance Cameras, Video Analytics and Biometrics

If you think of sensors and radar technology as the first line of defense, consider surveillance cameras and analytics as the secondary. Sensors provide the early warning or detection. Cameras, now enhanced with an array of advanced features, provide the critical verification, identification and recognition. Here, too, DHS avails itself of a wide portfolio of tools in order to remain adaptive to evolving threats.

High-resolution network video cameras. Because our borders encompass diverse terrain—from wetlands, grasslands, rivers and mountain to forests, deserts and urban sprawl—no one type of surveillance camera is ideally suited to monitoring every kind of environment. So a strategic mix is essential. That’s why you’ll find a mix of fixed cameras with variable focal lengths, PTZ cameras to track suspicious movement, cameras with wide and 360-degree fields of view, thermal cameras that can detect people and objects in darkness, fog and rain and even camouflaged in the background, cameras with wide dynamic range that provide critical details in both shadow and bright sunlight, day/night cameras, even lowlight-sensitivity cameras that can provide full color in near darkness, and more.

Advanced video analytics. Video analytics have come a long way in terms of accuracy and reliability. While motion detection and cross-line detection have been staples of border security for years, facial recognition is starting to gain traction as another force multiplier.

Fueled by advances in artificial intelligence and machine learning, the software measures distinguishing characteristics of a face—such as the distance between the eyes, skin tone, hairline, etc.—and cross-references them against a database of photographs collected by federal, state and local law enforcement agencies. While countries like China are using the technology to monitor and influence the social behavior of their citizens, U.S. agencies are using facial recognition to augment screening at checkpoints. Considering the complexity of border crossing—the need to verify country clearances, check for contraband and simultaneously identify individuals on a watch list—introducing technology that can reliably match an image to a person of interest has the potential to greatly reduce the opportunity for human error while allowing those who are properly vetted to move through checkpoints more efficiently.

Introducing Biometrics into the Mix

U.S. Customs is already testing the efficacy of other security tools like fingerprint scanning and hand geometry imaging to verify individuals pre-approved for entry into the country. In order to expedite passage at ports of entry, programs like GOES (Global Online Enrollment System for U.S. Customs and Border Protection Trusted Traveler Program) along with its Canadian counterpart NEXUS are applying emerging technologies ranging from IDs embedded with RFID chips to iris scans and other biometrics. It’s a dual authentication procedure that links passport documents with other markers unique to the individual. Employing these new screening technologies allow border agents to concentrate the bulk of their time on potentially higher-risk travelers and goods.

Of course, agents still conduct random screening interviews, but using technology brings a whole new level of efficiency to checkpoint processing. Additionally, there are emerging technologies that can examine a person’s facial expression and detect “sentiment” or mood. These types of incredible advances have only recently been enabled by similar advances in computing.

Adding Artificial Intelligence and Machine Learning

The next step in the evolution of border security will likely be through artificial intelligence (AI). In the not too distant future, introducing AI into the security technologies used at the borders will enable agents and operators to accomplish specific tasks and make autonomous decisions as well as, or ideally better than humans. Bloggers are already noting the AI has permeated the tech world on so many fronts. It’s powering our virtual assistants, directing our recommended playlists on media streaming services, using image recognition to suggest tags when we upload snapshots to social media, etc. Eventually it will transform the ways we interact with technology on a fundamental level.

The ability to achieve human or superhuman performance with AI-enhanced technology rests with the underlying building blocks of machine learning. At a basic level, machine learning is the practice of using algorithms to parse data, learn from it, and then make determinations or predictions. In other words, mimic human intelligence, but at a scale not achievable by human physiology. What really fuels machine learning, and ultimately AI, is access to data—preferably huge amounts of data. Thanks to the emergence of the Internet, with its vast repository of information and similarly, the migration from analog to digital technology and the seamless integration of networked security technologies into a comprehensive physical security information system, there’s plenty of data to draw on and learn.

For DHS advanced machine learning algorithms will eventually help to expand the capabilities and extend the value and ROI of technologies deployed on our southern and northern frontiers. AI-enhanced technologies will be able to selectively harvest data and apply it against problem sets called TTPs (Tactics, Techniques and Procedures) which are constantly evolving. Having the ability to ingest vast amounts of data and develop predictive intelligence will make it possible for border agents to make the critical, split-second decisions fundamental to the protection of our national security. And that’s how U.S. border agencies will ultimately create its own legendary defensive line.

This article originally appeared in the October 2017 issue of Security Today.

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