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This study focuses on analyzing the altitude of the zero degrees isotherm and variations in the environmental lapse rate (ELR) using the publicly available data collected from Automatic Dependent Surveillance–Broadcast (ADS-B) and Mode S Enhanced Surveillance (EHS) messages emitted by airplanes over a five-month period in 2021. The data was gathered using a professional receiver stationed in Bucharest (Romania). The aviation messages were decoded and the air temperature and pressure were determined, at the location of the airplane. The method has the advantage of the continuous messages that are emitted by the aircrafts during flight that allow instantaneous determination of the meteorological parameters at no additional costs. It can also be extended to permit almost real time maps of the ELR. When data was analyzed and a standard ELR value of 6.5 K/km is employed it was observed that the mean altitude of the 0 degrees isotherm exhibits a seasonal increase during the summer months, with an average altitude of 2874.2 m. The highest recorded altitude of the 0 degrees isotherm was found to be 5346.8 m, near Alexandria city (Romania), on 22.07.2021. Using a standard Least Mean Square algorithm alongside the International Standard Atmosphere pressure formula, the ELR values were calculated from pressure measurements data. The resulting mean ELR for the five-month period was determined to be 5.1331 K/km, slightly lower than the standard value.

Automatic dependent surveillance-broadcast (ADS-B) is the future of aviation surveillance and traffic control, allowing different aircraft types to exchange information periodically. Despite this protocol’s advantages, it is vulnerable to flooding, denial of service, and injection attacks. In this paper, we decided to join the initiative of securing this protocol and propose an efficient detection method to help detect any exploitation attempts by injecting these messages with the wrong information. This paper focused mainly on three attacks: path modification, ghost aircraft injection, and velocity drift attacks. This paper aims to provide a revolutionary methodology that, even in the face of new attacks (zero-day attacks), can successfully detect injected messages. The main advantage was utilizing a recent dataset to create more reliable and adaptive training and testing materials, which were then preprocessed before using different machine learning algorithms to feasibly create the most accurate and time-efficient model. The best outcomes of the binary classification were obtained with 99.14% accuracy, an F1-score of 99.14%, and a Matthews correlation coefficient (MCC) of 0.982. At the same time, the best outcomes of the multiclass classification were obtained with 99.41% accuracy, an F1-score of 99.37%, and a Matthews correlation coefficient (MCC) of 0.988. Eventually, our best outcomes outdo existing models, but we believe the model would benefit from more testing of other types of attacks and a bigger dataset.

Modern-day aircraft are flying computer networks, vulnerable to ground station flooding, ghost aircraft injection or flooding, aircraft disappearance, virtual trajectory modifications or false alarm attacks, and aircraft spoofing. This work lays out a data mining process, in the context of big data, to determine flight patterns, including patterns for possible attacks, in the U.S. National Air Space (NAS). Flights outside the flight patterns are possible attacks. For this study, OpenSky was used as the data source of Automatic Dependent Surveillance-Broadcast (ADS-B) messages, NiFi was used for data management, Elasticsearch was used as the log analyzer, Kibana was used to visualize the data for feature selection, and Support Vector Machine (SVM) was used for classification. This research provides a solution for attack mitigation by packaging a machine learning algorithm, SVM, into an intrusion detection system and calculating the feasibility of processing US ADS-B messages in near real time. Results of this work show that ADS-B network attacks can be detected using network attack signatures, and volume and velocity calculations show that ADS-B messages are processable at the scale of the U.S. Next Generation (NextGen) Air Traffic Systems using commodity hardware, facilitating real time attack detection. Precision and recall close to 80% were obtained using SVM.