Aviation Security Engineering: A Hollistic Approach

This book introduces a number of new concepts which illustrate how information and communiaction technologies can be utilised for enhancing aviation securit

Reference Trajectories: The Dataset Enabling Gate-to-Gate Flight Analysis

Without a doubt, a publicly verifiable data is required to ensure a strong, transparent and independent air traffic management performance review system. Community sourced data (such as ADS-B/Mode S provided by OpenSky Network and others alike) has been used to analyse different aspects of air traffic management. The main drawback of such ADS-B data is the lack of crucial pieces of information that need to be inferred. On the other hand, Eurocontrol has used correlated position reports (CPRs) gathered from European Air Navigation Service Providers (ANSP) to conduct some of its actual/flown trajectory oriented performance analysis. The availability and the granularity of the CPRs vary between Eurocontrol Member States, making it difficult to perform accurate wide-scale studies. Using the strengths of both data sources would obviously result in great benefits. This paper describes the first step in creating a pan-European Flight Table (FT) and its supporting reference trajectories (RT). It is expected that the resulting dataset will be made available for the general public and that the work will continue to improve in scope and accuracy

Towards analysing the impact of go-around occurrences at Large European airports

Go-arounds (GoA) or missed approaches are standard flight procedures initiated when an approach is aborted for safety reasons, requiring pilots to reposition the aircraft for a subsequent landing attempt. This study leverages ADS-B data sourced from the OpenSky Network, collected at 20 major European airports between January 2019 and July 2023. Out of 6.7 million retrieved landing trajectories, 20,196 GoA were identified and analyzed. We conducted statistical evaluations on these GoA instances to compare the rates of GoA at different airports, market segments, and aircraft types. We also looked at the distributions of distance, duration, and fuel consumption of GoA for the different airports. Of particular note, we quantified the impact of a GoA on the surrounding arrival traffic by analyzing how GoA events affect Arrival Sequencing and Metering Area (ASMA) timings. Our results show that the rate of GoA at the assessed airports ranged from 1.5 to 6 occurrences per 1,000 landings. The median duration and distance of a GoA varied depending on the airport, falling between 11.5 and 16.5 minutes, and 36.5 and 58.2 NM respectively. During a GoA, an Airbus A320 typically consumes between 350 and 600 kg of fuel. Importantly, our findings demonstrate that a GoA occurrence can significantly impact the efficiency of arriving traffic at an airport, causing disruptions lasting up to an hour. ASMA timings tend to increase directly after a GoA occurs and peak for landings occurring 10 to 25 minutes after a GoA, with flight time increases ranging from 30 to 100 seconds, depending on the airport. These timings gradually return to their pre-GoA levels within the following hour, though certain airports may experience longer recovery periods. This comprehensive study on GoA provides a deeper understanding of their impact, offering valuable insights that can support data-driven decision-making in the aviation industry

Addressing Security in the ATM Environment

This paper addresses the full lifecycle of security countermeasures identified in the Security Risk Analysis of the future Air Traffic Management System (ATM). The process establishes new security functions identified in the GAMMA project and their implementations in order to ensure acceptable levels of security for ATM. In this project, ATM Security is addressed by focusing on two dimensions defined by Single European Sky ATM Research: establishing a collaborative support capability by defining a framework embracing three-levels for Security Management (i.e. European, National, and Local) and developing security measures for the self-protection/resilience of the ATM Systems by exploiting automated security-related functions to handle potential threats. This paper concentrates on the second dimension and how the countermeasures are identified, implemented and developed in prototypes. The prototypes will then be validated in an operational scenario, through the new concept introduced by the project. The reader will be accompanied through a practical example of the whole process on how ATM Security needs have been identified. The objective is to protect the core ATM Security functionalities (Primary Assets) and corresponding Supporting Assets. We identified 44 of the most feared threat scenarios in terms of impact on the SESAR Key Performance Areas (KPA). The threat scenario described in this paper is “False ATCO”, affecting the Supporting Asset “Voice system”. The developed prototype is “SACom” (Secure ATC Communication) that considers the security countermeasures identified in the risk treatment analysis to reduce the risks. The paper concludes with the description of the activities planned for validating the SACom prototype as part of the proposed global solution

Real-time departure slotting in mixed-mode operations using deep reinforcement learning : a case study of Zurich airport

A mixed-mode runway operation increases the runway capacity by allowing simultaneous arrival and departure operations on the same runway. However, this requires careful evaluation of safe separation by experienced Air Traffic Controllers (ATCOs). In daily operation, ATCOs need to make real-time decisions for departure slotting. However, an increase in runway capacity is not always guaranteed due to the stochastic nature of arrivals and departures and associated environmental parameters. To support ATCOs in making real-time departure slotting decisions, this paper proposes a Deep Reinforcement Learning approach to suggest departure slots within an incoming stream of arrivals while considering operational constraints and uncertainties. In this work, novel state representation and reward mechanism are designed to facilitate the learning process. Experimentation on A-SMGCS data from Zurich airport shows that the proposed approach achieves an efficiency ratio of more than 83.8% of the expected runway capacity while maintaining safe separation distances in mixed-mode operations. The results of this work have demonstrated the potentials of Deep Reinforcement Learning in solving decision-making problems in Air Traffic Management.Civil Aviation Authority of Singapore (CAAS)National Research Foundation (NRF)Published versionThis research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme

Cleared to land a multi-view vision-based deep learning approach for distance-to-touchdown prediction

With the broader adoption of digital air traffic control towers, real-time video data is expected to complement the current surveillance system (if available) and improve airport performance in terms of safety and efficiency. However, to fully utilize such data, a suite of computer vision algorithms needs to be developed for extracting useful information from real-time video feeds. Currently, most of the studies in the literature have focused only on the detection and tracking of aircraft on the airport surface, while approaching aircraft also play an essential role in airport and runway operations. The distance-to-touchdown of approaching aircraft is a critical parameter in final approach spacing and departure sequencing. Therefore, this research proposes a deep learning approach for estimating the distance of approaching aircraft to touchdown using multi-view video feeds. The proposed approach adopts a state-of-the-art computer vision model with an auto-calibration technique for detecting the approaching aircraft and extracting feature vectors from multiple camera views under various lighting and weather conditions. Then, an ensemble approach is introduced for combining the input vectors for distance estimation. The approach is evaluated with both Changi Airport simulated and real video data. Firstly, the proposed approach is designed to be easily updated and adapted for different camera system configurations. Secondly, the proposed approach has successfully combined the strength of both monoscopic and stereoscopic approaches to provide accurate distance-to-touchdown prediction in various scenarios. The experimental results demonstrate the advantages of the proposed approach with stable performance and low predicted errors (Mean Absolute Percentage Error = 0.18%) in estimating the distance-to-touchdown up to 10 NM. Such capability in a Digital Tower environment can augment the runway controller’s sequencing and final approach spacing capabilities.Civil Aviation Authority of Singapore (CAAS)National Research Foundation (NRF)Published versionThis research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme

Towards validating a security situation management capability

With SESAR and NextGen readying towards implementing novel operational concepts and technical enablers in ATM/CNS, the question of how to manage security in a dynamic environment across a highly distributed and networked system gains higher attention. The Global ATM Security Management project (GAMMA) addresses the development of such a security situation management capability. Following the September 11 attacks and major large-scale outages of critical infrastructures, the security of air navigation has emerged as a critical capability gap. On-going transformation programs like SESAR and NextGen are moving into the deployment phase with limited to none tangible security solutions. GAMMA addresses this gap by investigating a security situation management capability. The framework of this capability is devised as a distributed network of aviation stakeholders that jointly collaborate in identifying and localizing security incidents while considering the constraints given by the different participants, national responsibilities, and collaboration-related requirements. This paper addresses the preparatory work for the validation of an initial security situation management capability. For that purpose, project partners setup a joint configuration and trial network for the security functions and systems developed in the frame of a real-time human-in-the-loop simulation. The simulation results have been measured against the mapping of the operational concept and validation requirements, in particular in terms of situational awareness on the operator side and networked incident management response. These results will inform the further validation activities of the project

Provisioning for a Distributed ATM Security Management: the GAMMA Approach

Future Air Traffic Management (ATM) will become more complex by having a multitude of tightly interconnected systems requiring the exchange of a large amount of information for their secure and timely operation. Interconnecting various ATM systems, could potentially open up the systems to more attacks, thereby increasing vulnerabilities and the overall risk, unless adequate security measures are taken. This necessitates to provision for a security management solution in a multi-national scale, providing the basis for handling security, from identification of security threats to prevention, detection and efficient resolution of the attacks. GAMMA is aimed at providing a holistic vision for this ATM security management and maintaining alignment with ATM developments under SESAR. In this paper, we describe the reference model conceptualizing the principles for ATM security management from GAMMAs perspective. While using the SESAR risk assessment methodology, we extend the scope of threat assessment performed in SESAR to a more comprehensive system of systems level, including all foreseen ATM assets and possible threats. Following the assessment of risks and identifying the course of actions for treatments, the specification of system-level ATM security requirements are conducted that are used to define the NAF3.1 based architectural views from stakeholders perspectives. Overall, this paper highlights the GAMMA contribution in addressing the security challenges and the gaps identified within the ATM environment and demonstrate the feasibility of its security solution