Controller-Pilot Data Link Communications (CPDLC) are rapidly replacing voice-based Air Traffic Control (ATC) communications worldwide. Being digital, CPDLC is highly resilient and bandwidth efficient, which makes it the best choice for traffic-congested airports. Although CPDLC initially seems to be a perfect solution for modern-day ATC operations, it suffers from serious security issues. For instance, eavesdropping, spoofing, man-in-the-middle, message replay, impersonation attacks, etc. Cyber attacks on the aviation communication network could be hazardous, leading to fatal aircraft incidents and causing damage to individuals, service providers, and the aviation industry. Therefore, we propose a new security model called AKAASH, enabling several paramount security services, such as efficient and robust mutual authentication, key establishment, and a secure handover approach for the CPDLC-enabled aviation communication network. We implement the approach on hardware to examine the practicality of the proposed approach and verify its computational and communication efficiency and efficacy. We investigate the robustness of AKAASH through formal (proverif) and informal security analysis. The analysis reveals that the AKAASH adheres to the CPDLC standards and can easily integrate into the CPDLC framework.

Cybersecurity attacks that target software have become profitable and popular targets for cybercriminals who consciously take advantage of web-based vulnerabilities and execute attacks that might jeopardize essential industry 5.0 features. Several machine learning-based techniques have been developed in the literature to identify these types of assaults. In contrast to single classifiers, ensemble methods have not been evaluated empirically. To the best of our knowledge, this work is the first empirical evaluation of both homogeneous and heterogeneous ensemble approaches compared to single classifiers for web -based attack detection in industry 5.0, utilizing two of the most realistic public web-based attack data -sets. The authors divided the experiment into three main phases: In the first phase, they evaluated the performance of five well-established supervised machine learning (ML) classifiers. In the second phase, they constructed a heterogeneous ensemble of the three best-performing ML algorithms using max vot-ing and stacking methods. In the third phase, they used four well-known homogeneous ensembles to evaluate the performance of the bagging and boosting method. The results based on the ECML/PKDD 2007 and CSIC HTTP 2010 datasets revealed that bagging, particularly Random Forest, outperformed sin-gle classifiers in terms of accuracy, precision, F-value, FPR, and area of the ROC curve with values of 99.597%, 98.274%, 99.129%, 0.523%, 100 and 99.867%, 99.867%, 99.867%, 0.267%, 100, respectively. In con-trast, single classifiers performed better than boosting and stacking. However, in terms of FPR, the boost-ing exceeded single classifiers. Max voting is appropriate when accuracy, precision, and FPR are the primary concerns, whereas single classifiers can be employed when recall, FNR, training, and prediction times are critical elements. In terms of training time, ensemble approaches are more likely to be affected by data volume than single classifiers. The papers findings will help security researchers and practition-ers identify the most efficient learning techniques for securing web applications. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The coronavirus pandemic has overburdened medical institutions, forcing physicians to diagnose and treat their patients remotely. Moreover, COVID-19 has made humans more conscious about their health, resulting in the extensive purchase of IoT-enabled medical devices. The rapid boom in the market worth of the internet of medical things (IoMT) captured cyber attackers attention. Like health, medical data is also sensitive and worth a lot on the dark web. Despite the fact that the patients health details have not been protected appropriately, letting the trespassers exploit them. The system administrator is unable to fortify security measures due to the limited storage capacity and computation power of the resource-constrained network devices. Although various supervised and unsupervised machine learning algorithms have been developed to identify anomalies, the primary undertaking is to explore the swift progressing malicious attacks before they deteriorate the wellness systems integrity. In this paper, a Dew-Cloud based model is designed to enable hierarchical federated learning (HFL). The proposed Dew-Cloud model provides a higher level of data privacy with greater availability of IoMT critical application(s). The hierarchical long-term memory (HLSTM) model is deployed at distributed Dew servers with a backend supported by cloud computing. Data pre-processing feature helps the proposed model achieve high training accuracy (99.31%) with minimum training loss (0.034). The experiment results demonstrate that the proposed HFL-HLSTM model is superior to existing schemes in terms of performance metrics such as accuracy, precision, recall, and f-score.

The safety of the passengers and goods in airplanes depends upon a number of combined factors. An airplane's condition and the pilot's experience are pivotal, but another very crucial element is the synchronization among the pilots and the air traffic controller (ATC). The communication link between the two carries many uncertain aspects.  The aviation sector often tends to give more priority to safety rather than cybersecurity.  Although the controller-pilot data communication link (CPDLC) system has been proposed for consistent and reliable communication recently, it has some serious drawbacks. In this paper, we highlight the shortcomings of the CPDLC system from a cyber security perspective. We propose a federated learning-based privacy-preserving intrusion detection system (IDS) to protect the CPDLC from uplink and downlink cyber attacks. To ensure a realistic and viable solution, we created our own training dataset by eavesdropping on the air-ground communication at a site near Arlanda airport, Sweden. The anomaly detection model constructed through federated learning has achieved higher accuracy, precision, recall and F1 score as compared to the centrally and locally trained models, enabling higher security. Due to the lower training loss and time, the proposed approach is highly suitable for the sensitive aviation communications.

Ambient Intelligence (AmI) in Internet of Things (IoT) has empowered healthcare professionals to monitor, diagnose, and treat patients remotely. Besides, the AmI-IoT has improved patient engagement and gratification as doctors interactions have become more comfortable and efficient. However, the benefits of the AmI-IoT-based healthcare applications are not availed entirely due to the adversarial threats. IoT networks are prone to cyber attacks due to vulnerable wireless mediums and the absentia of lightweight and robust security protocols. This paper introduces computationally-inexpensive privacy-assuring authentication protocol for AmI-IoT healthcare applications. The use of blockchain & fog computing in the protocol guarantees unforgeability, non-repudiation, transparency, low latency, and efficient bandwidth utilization. The protocol uses physically unclonable functions (PUF), biometrics, and Ethereum powered smart contracts to prevent replay, impersonation, and cloning attacks. Results prove the resource efficiency of the protocol as the smart contract incurs very minimal gas and transaction fees. The Scyther results validate the robustness of the proposed protocol against cyber-attacks. The protocol applies lightweight cryptography primitives (Hash, PUF) instead of conventional public-key cryptography and scalar multiplications. Consequently, the proposed protocol is better than centralized infrastructure-based authentication approaches.