# Secure Aviation Control through a Streamlined ADS-B Perception System
###### Abstract  
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. a +
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also lacking any security measures to ensure the safety of the data transmitted. Failure to formally distinguish between actual and spoofed ADS-B messages is one of the most frequent issues that lead to misunderstandings while communicating with an aircraft or a sensor. Additionally, attacks on ADS-B systems are of many types. Figure 1 demonstrates a variety of attacks that could affect ADS-B systems [3; 4]. Nowadays, as stated earlier, around half of commercial aircraft are equipped with ADS-B hardware to improve aircraft traffic control as a whole. With the evident orientation towards using ADS-B and the rapid increase in the sophistication and diversity of cyber-attacks and their techniques, this paper will focus on providing security solutions for detecting false information gathered by sensors to improve aircraft traffic control and security. Unfortunately, no previous studies have introduced a dataset or a detection model in a realistic environment. This has led us to take the initiative to find a dataset that contains both genuine messages and injected messages that were transmitted and received by a sensor. We also used different classification approaches to provide a higher accuracy rate and enhance the overall security of the detection systems. The primary contribution of this paper is the proposal of a machine learning-based detection model for classifying injected messages using a specialized dataset. The following is a list of the specific contributions. * We outlined earlier research related to ADS-B, its threats and vulnerabilities, and some of the proposed solutions so we could determine the gap and select the best method to apply our study to maximize the benefits for the aviation industry. * We rendered the qualified dataset for ease of use. We also analyzed and performed an in-depth study of the different types of injected messages directed against aircraft and potential defenses against such attacks to aid and optimize our proposed solution. * We developed an efficient classification model using multiple techniques, and we achieved solid results in detecting injection within the messages used in path modification, ghost aircraft injection, and velocity drift attacks, bearing in mind that it needed to be performed in a rapid manner and with the least amount of training required.