Owais Crawford
Airport security has been a key focus of counter-terrorism efforts since the September 11 attacks on the United States in 2001. While airport reconfiguration may not be effective in deterring attacks, new technologies are constantly being developed to enhance security and improve the passenger experience. These include artificial intelligence systems for translation services and monitoring operations, biometric identity verification, Credential Authentication Technology (CAT), and CT scanning equipment for baggage screening. Airports are also implementing behavioural identification schemes and deploying special teams of security officers to monitor passenger behaviour and spot potential threats. International cooperation is also vital, with American airports turning to Israeli firms for security upgrades and NATO coordinating workshops, training courses, and research projects to develop new technologies for detecting explosives and illicit activities.
| Characteristics | Values |
|---|---|
| Technology | CT scanning equipment, backscatter X-rays, biometric data, AI systems, metal detectors, document verification technology, facial recognition technology, Credential Authentication Technology (CAT), automated screening lanes |
| Human factors | Behavioural identification schemes, behaviour-detection squads, behaviour pattern recognition (BPR), profiling, special teams of security officers, dog handlers, armed officers |
| Infrastructure | Roadblocks for random vehicle checks, strengthened terminal exteriors, shatterproof glass, closed-circuit televisions |
| Procedures | Secure Flight program, risk-based screening, baggage and passenger screening, limiting items carried on board, ID requirements |
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What You'll Learn
- Biometrics and facial recognition technology for identity verification
- Artificial intelligence systems for translation and monitoring
- Behavioural identification schemes to spot would-be terrorists
- Credential Authentication Technology for ID and reservation verification
- Sensors and data fusion technologies to detect explosives and weapons
Biometrics and facial recognition technology for identity verification
Biometrics and facial recognition technology are increasingly being used to enhance identity verification processes at airports. This technology is seen as a significant security enhancement that improves traveller convenience. Facial recognition technology is used to match a live image of a passenger with a gallery of pre-existing templates or photos provided to the government, such as passport photos. This ensures that the person at the checkpoint matches the person pictured on the identification document.
The Transportation Security Administration (TSA) in the United States employs facial comparison technology, such as CAT-2 units, at nearly 250 airports. Participation in this process is voluntary, and travellers can opt-out of having their photos taken. TSA is committed to protecting traveller privacy and civil liberties while improving security effectiveness.
U.S. Customs and Border Protection (CBP) has also deployed facial recognition technology at 32 airports for departing travellers and all airports for arriving international travellers. CBP has incorporated privacy principles into its program, prohibiting airlines from storing or using traveller photos for their purposes.
The use of biometrics and facial recognition technology is part of a broader effort to enhance airport security. Airports are investing in new technologies, such as artificial intelligence, to improve security screening, baggage screening, and aircraft safety. For example, backscatter X-rays can detect hidden weapons and explosives, and new metal detectors can identify metallic objects without requiring passengers to remove their shoes.
Additionally, organizations like NATO support the development of new technologies to tackle terrorist threats. Their SPS Programme includes initiatives like DEXTER, which aims to develop an integrated system of sensors and data fusion technologies to detect explosives and concealed weapons in real time at airports and other transport hubs.
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Artificial intelligence systems for translation and monitoring
Artificial intelligence (AI) is increasingly being used in airports to enhance security, improve efficiency, and optimise operations. AI systems can assist with translation services, monitoring, and various other tasks.
AI-powered language translation tools can aid airport staff in communicating with passengers who speak different languages. This can take the form of an AI-generated avatar on screens at information desks that "listens" to passenger queries and responds in a human-like voice. Augmented reality headsets could also be used to provide live translation.
AI-integrated cameras are being utilised to monitor various aspects of airport operations, such as the refueling of planes, baggage unloading, and safety concerns on the airfield. This data is then analysed to identify and mitigate delays and cancellations by predicting flight delays more accurately. For example, Japan Airlines, in collaboration with NEC Corporation, has introduced an AI-driven system at Tokyo International Airport (Haneda) to monitor carry-on luggage. The system captures images of passengers' carry-on items, analyses the quantity and type of baggage, and estimates the space required in overhead bins. This helps to streamline the boarding process and improve the efficiency of cabin baggage storage.
AI can also assist with security screening and the detection of dangerous goods. For instance, London Heathrow Airport is piloting an AI system called AIMEE to assist Air Traffic Controllers (ATC) in monitoring aircraft movements across the airfield, providing a comprehensive visualisation of their positions. This helps ensure the safe completion of each phase of the aircraft's runway activity.
Additionally, AI has the potential to enhance the customer experience by providing personalised flight information and trip planning assistance through airport apps or websites.
While AI offers numerous benefits to airports, it is important to consider potential challenges and risks, such as the complexity of machine learning systems, ethical implications, and cybersecurity concerns.
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Behavioural identification schemes to spot would-be terrorists
The use of new technologies is essential in enhancing airport security measures and countering terrorism. One critical aspect of this effort is the implementation of behavioural identification schemes aimed at spotting potential terrorists. These schemes involve the analysis of behavioural indicators and patterns that may suggest an individual's malicious intentions.
Transportation Security Administration (TSA) in the United States has developed a controversial program called Screening of Passengers by Observation Techniques (SPOT). This program utilises Behaviour Detection Officers (BDOs) who are trained to interact with passengers and identify behavioural indicators such as fidgeting, whistling, sweaty palms, arrogance, cold penetrating stare, and rigid posture. These indicators are assigned point values, and officers use a checklist to assess passengers during screening. However, the effectiveness of the SPOT program has been questioned, with critics arguing that it may lead to racial profiling and unnecessary harassment of travellers.
While behavioural identification schemes aim to enhance security, it is important to acknowledge their limitations. In 2013, the Government Accountability Office (GAO) concluded that there was insufficient evidence to support the idea that behavioural indicators could reliably identify individuals posing a risk to aviation security. The human ability to accurately detect deceptive behaviour based on these indicators was found to be marginally better than chance. Additionally, traditional violence risk factors, such as a history of violence, psychiatric disorders, or drug abuse, are not always predictive of lone terrorist acts and may result in false positives.
To address these challenges, organisations like the United Nations Office of Counter-Terrorism (UNOCT) have established initiatives such as the Behavioural Insights (BI) Hub. This hub serves as a global platform for knowledge sharing, capacity building, and coordination among counter-terrorism actors. By leveraging behavioural insights, UNOCT aims to identify risk factors and develop validated strategies to prevent and counter violent extremism effectively. Additionally, the use of artificial intelligence (AI) systems can aid in monitoring and analysing passenger behaviour, enhancing the efficiency of security checks, and improving overall airport security.
In conclusion, while behavioural identification schemes contribute to airport security efforts, they should be continuously evaluated and complemented by other security measures. The application of new technologies, such as AI, plays a crucial role in strengthening these schemes and ensuring the effective detection and prevention of potential terrorist threats at airports.
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Credential Authentication Technology for ID and reservation verification
Airport security has been a key focus of governments and international organisations since the tragic events of September 11, 2001. Terrorist attacks involving aircraft and airports have highlighted the need for robust security measures. One of the critical tools in the fight against terrorism at airports is Credential Authentication Technology (CAT).
CAT is a game-changer for airport security, enhancing the ability of TSA officers to detect fraudulent IDs and verify travellers' identities and flight information. When a passenger presents their photo ID, the TSA officer scans and analyses it with a CAT unit. This unit is electronically linked to the Secure Flight database, which confirms flight details and pre-screening status. CAT improves the accuracy of ID authentication and helps address vulnerabilities related to ID and boarding pass fraud.
The latest generation of CAT, known as CAT-2, employs facial matching technology to further enhance identity verification. This technology compares a traveller's photo ID with their physical appearance, providing an additional layer of security. CAT-2 units are currently in use at Portland International Airport (PDX), building on the success of the first-generation CAT implementation.
The use of CAT technology improves the overall security of airports by streamlining the identity verification process. By quickly and accurately authenticating travellers' identities, CAT helps TSA officers focus on potential threats. This technology also reduces the risk of fraudulent document usage, enhancing the overall effectiveness of airport security measures.
Looking ahead, the integration of CAT with other emerging technologies, such as artificial intelligence and biometric solutions, could further bolster airport security. The combination of CAT with risk-based screening, intelligent video analytics, and biometric deployment may create a robust, multi-layered defence against terrorist activities at airports.
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Sensors and data fusion technologies to detect explosives and weapons
Sensors and data fusion technologies are vital tools in the detection of explosives and weapons, and thus play a critical role in counter-terrorism efforts. An example of such technology is the DEXTER (Detection of Explosives and Firearms to Counter Terrorism) initiative, which is overseen by NATO. DEXTER is an ambitious project that aims to develop an integrated system of sensors and data fusion technologies to detect explosives and concealed weapons in real-time. This technology is designed to secure mass transport infrastructures, including airports.
The DEXTER system encompasses two sensors, MIC and EXTRAS, which provide quantitative data to help identify individuals carrying explosives or weapons. This system does not require random checks on moving passengers or checkpoints in crowded venues and mass transit scenarios. Instead, it utilizes remote detection methods, with security guards wearing smart glasses that receive an image of the commuter, along with their position data, allowing for efficient and accurate identification of potential threats.
The development and advancement of sensor technologies are an ongoing process, with researchers continually striving to improve detection methods for explosives and weapons. For instance, the U.S. Army utilizes various sensor platforms, including air and ground vehicles equipped with high-definition electro-optical cameras, long-wave infrared, and GPS inertial navigation systems. These sensors, in conjunction with machine learning techniques, enable the standoff detection of explosive hazards.
Furthermore, the fusion of sensor data plays a crucial role in enhancing detection capabilities. By aggregating data from multiple sensors and applying algorithms, researchers can determine the best course of action and maximize the probability of detection while minimizing false alarms. This fusion of sensor data involves integrating multiple technologies, such as MIC and EXTRAS, with video and image data, to provide security operators with real-time, accurate information.
The application of sensors and data fusion technologies in airports can significantly enhance security measures. By implementing these tools, airports can detect explosives and weapons more effectively, thereby mitigating the threat of terrorist attacks. These technologies offer a robust solution to counter dynamic and evolving terrorist tactics, ensuring the safety of commuters and personnel within airport premises.
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Frequently asked questions
Technologies being used include:
- Biometrics for identity verification
- CT X-ray scanning equipment for carry-on baggage
- Automated Screening Lanes
- Credential Authentication Technology (CAT)
- Backscatter X-rays to detect hidden weapons and explosives
- Artificial intelligence systems for translation services and monitoring aircraft landing procedures
- Behavioural identification schemes
New technologies can improve security and enhance the passenger experience by reducing waiting times during the security screening process. They can also help secure mass transport infrastructures, such as airports, metro and railway stations, by detecting explosives and concealed weapons in real time.
Technology has its limitations and cannot guarantee the prevention of terrorist attacks. For example, machines may be unable to detect certain types of explosives. A multi-layered security approach that combines technology with human intelligence and behavioural identification schemes is therefore crucial.
