Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

IF 6.2 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Md. Atiqur Rahman , Touhid Bhuiyan , M. Ameer Ali
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引用次数: 0

Abstract

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system’s performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen’s kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems.
增强航空安全:通过先进的数据分析进行实时ADS-B注射检测的机器学习
飞机在全球运输中发挥着至关重要的作用,确保了人员和货物的高效流动。虽然总体上是安全的,但航空系统偶尔会遇到事件和事故,这强调了主动风险管理的必要性。本研究采用机器学习技术,利用广播自动相关监视(ADS-B)数据检测商用飞机运行中的异常情况。鉴于对ADS-B技术的日益依赖,对其易受注入攻击等安全漏洞影响的担忧日益加剧。为了解决这些漏洞,我们提出了一个强大的ADS-B注入检测系统。采用GridSearchCV进行模型优化,有效识别和分类注入风险。使用ADS-B消息注入攻击数据集对系统的性能进行了评估,取得了出色的结果,包括准确性、精密度、召回率和F1分数的值为0.9970。与现有方法相比,该分类器的曲线下面积(0.9999)、特异性(0.9956)和科恩kappa(0.9954)也更高,同时实现了更低的对数损失(0.0107)。本研究通过引入高精度、计算效率高、可靠的ADS-B注入攻击实时检测模型,显著增强了航空安全,确保了现代飞控系统的完整性和弹性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
alexandria engineering journal
alexandria engineering journal Engineering-General Engineering
CiteScore
11.20
自引率
4.40%
发文量
1015
审稿时长
43 days
期刊介绍: Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification: • Mechanical, Production, Marine and Textile Engineering • Electrical Engineering, Computer Science and Nuclear Engineering • Civil and Architecture Engineering • Chemical Engineering and Applied Sciences • Environmental Engineering
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