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New Tech for an Age-Old Threat

While mass shootings and vehicle-ramming attacks have become a primary concern for counterterrorism officials and security practitioners alike during the past decade, one of the most prevalent threats where constant vigilance is still required is in the mitigation of vehicle-borne improvised explosive devices (IEDs).

The first attack on the World Trade Center coupled with the Oklahoma City bombing in the 1990s served as a wake-up call to the security industry about the deadly consequences of failing to adequately screen vehicles for explosives.

Nearly 20 years later, however, the fact remains that car bombings are one of the most destructive and frequently used tools in the arsenals of terror organizations around the world. In October 2022, for example, at least 100 people were killed and nearly 300 others were wounded following a pair of car bombings carried out in the Somali capital of Mogadishu.       

As with other forms of terrorism, car bombings have evolved with regards to the techniques used and the lethality with which they can be conducted. Some of the most destructive methods remain packing vehicles with explosive materials near a target and having a suicide bomber detonate themselves and their cargo. Still others prefer to be more tactical in their approach, opting to hide a bomb inside an automobile or somewhere underneath the vehicle’s chassis where it is less likely to be found.

Regardless of the tactics used by terrorists, many of the same principles for mitigating against a vehicle-borne bomb still apply. Modern technologies, such as AI-powered video analytics, are also providing today’s security teams with safer—and more effective—ways to detect the presence of explosives.

Here are a few of the traditional best practices, as well as some more technologically advanced measures, you should consider leveraging within your organization to protect against car bombs.

Creating a Secure Perimeter

There is an adage in our industry that security begins at the door with access control because if you cannot determine who is entering and leaving a facility, then all other technologies, policies, and procedures become superfluous. The same could be said for establishing a secure perimeter with adequate standoff distances when it comes to mitigating human casualties or property damage inflicted by car bombs.

In the aftermath of the Oklahoma City bombing, a commission determined that the U.S. federal government should establish various standoff distances—areas where vehicles and people are allowed to travel without being screened—based on the security level of the facility in question. A nuclear power plant, for example, would want to have a much greater distance between its critical assets and a main highway than a clothing manufacturer given the potential for wide-reaching ramifications in the event of a bomb detonation at its location.

After the appropriate distance has been established, it is incumbent upon the end user to create a screening checkpoint where vehicles and people can be thoroughly screened for explosives and other prohibited items prior to being granted entry. Barriers tested to withstand a variety of impact speeds and vehicle sizes should also be employed to minimize the impact of a blast from reaching further into a premises.

Human Capacity and K9s

For all the advanced screening technologies in use at places like airports and government buildings today, many public and private sector organizations still depend on good, old-fashioned visual inspections from law enforcement or security personnel. Aside from using handheld tools—such as mirrors and metal detectors—to sweep vehicles, human screeners are necessary to be able to notice different behavior indicators that could point to a driver or passenger who may be trying to hide something.

For example, if someone is attempting to use a phony identification to pass through a checkpoint and seems unusually nervous when responding to simple questions, trained personnel can interpret these actions as suspicious and conduct additional screening measures—or deny the person entry altogether.

Bomb-sniffing canines are another popular tool for mitigating against vehicle-born explosives. As with drug detection, dogs can be trained to recognize the scents of various explosives and alert to their presence.

Aside from the fact that there just are not that many to go around, however, the drawback with canines is that they also require dedicated handlers who are responsible for their daily care and ongoing training to meet the rigors of the job. Bomb-sniffing canines are also prone to falsely alerting to the presence of explosives when there are none due to a multitude of factors, including elevated levels of stress in the environment or just simply mistaking a benign odor for that of bomb-making materials.

An investigation conducted by NBC News in 2016 found that bomb-sniffing canine teams at 10 major airports failed tests designed to gauge their accuracy in detecting explosives. Specifically, K9 teams at these airports reportedly failed their annual certification tests 52 times between 1 January 2013 and 15 June 2015.

AI Comes of Age

While the industry has made great strides in the field of explosives detection, many of these solutions, due to either their prohibitive costs or deployment logistics, remain out of reach for most of the market. But just as artificial intelligence (AI) has transformed the video analytics space, it also stands poised to do the same for vehicle explosives screening.

Security checkpoints today can be outfitted with a combination of video hardware and software tools that scan underneath automobiles to detect whether explosives, as well as a wide range of other contraband, are present. These AI algorithms, which have been trained on thousands of hours of footage and can differentiate between a wide range of vehicle makes and models, enable end users to check for explosive devices more quickly and efficiently than what can be achieved with traditional methodologies.           

Vehicles can also remain in motion during the process, and operators can be located a safe distance away from the checkpoint—or in another facility or region entirely. Integrations with license plate recognition or automatic number plate solutions provide security teams with the capability to perform comparison scans of cars should they appear at the checkpoint later. Many of these solutions can also be used in conjunction with a variety of other traffic and security systems, such as X-ray scanners, vehicle barriers, traffic lights, and information displays.

Identifying Trends

Attacks are typically pre-planned, sometimes months in advance, and harnessing AI to know if a vehicle is arriving at multiple locations, and the trends behind this, can be critical to stopping attacks or applying enhanced scrutiny.

Often, time and timing are critical to understanding and preventing attacks. For example, security departments want to know if a vehicle is showing up at multiple checkpoints. Being able to create a database of vehicle images and identifying information, such as a license plate, and drivers and their identifying information is critical for multi-site operations.

Of course, the degree to which public and private sector organizations can record and store this type of data varies from state-to-state and from business-to-business. At least 16 U.S. states have laws on the books that address the use of license plate readers and the retention of data they collect, according to the National Conference of State Legislatures. Some states have even banned private use of such technologies and there may be EU General Data Protection Regulation concerns, so it is paramount that security practitioners evaluate the legality of solutions they are thinking about deploying for their applications.

Understanding when a vehicle or a driver is showing up may be critical to identifying why the vehicle is appearing at the site. If the vehicle or driver is showing up at a certain time each week, or at different locations at certain times each week, it may lead to greater understanding of the timing of the arrival. Harnessing AI and databasing, investigative groups can overlay vehicle or driver arrival time against schedules of guard services, or arrivals of perhaps regularly scheduled explosive containers onsite. Additionally, databasing license plate information may lead to knowing that various vehicles are arriving at multiple locations with the same license plate, identifying a worrying trend of stolen vehicles being used to probe security forces.

Gathering information and making sense of it is a key component to stopping vehicle-borne attacks. Pattern recognition is enhanced by networking security checkpoints together, gathering as much information as possible, and then having analysts or AI identify patterns. Tying together information from disparate sites into an awareness picture can be the difference between being ahead of the attack or acting too late.

Though they may not be as top of mind for security practitioners as they once were, the threats presented by vehicle bombs are just as significant today as they were 30 or 40 years ago. Only by implementing a strategy that takes advantage of both best practices and advanced technologies can the industry hope to avoid the mistakes of the past.


Matt Powell is managing director for North America at ISS (Intelligent Security Systems), a pioneer and leader in the development of video intelligence and data awareness solutions. He has more than two decades of experience in security and transportation technologies having formerly served as principal-infrastructure markets at systems integrator Convergint Technologies and as a developer of ITS/DoT market strategies for Videolarm and Moog prior to that. He can be reached at [email protected].

© Matthew Powell