With the exponential increase in information security risks, ensuring the safety of aircraft heavily relies on the accurate performance of risk assessment. However, experts possess a limited understanding of fundamental security elements, such as assets, threats, and vulnerabilities, due to the confidentiality of airborne networks, resulting in cognitive uncertainty. Therefore, the Pythagorean fuzzy Analytic Hierarchy Process (AHP) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to address the expert cognitive uncertainty during information security risk assessment for airborne networks. First, Pythagorean fuzzy AHP is employed to construct an index system and quantify the pairwise comparison matrix for determining the index weights, which is used to solve the expert cognitive uncertainty in the process of evaluating the index system weight of airborne networks. Second, Pythagorean fuzzy the TOPSIS to an Ideal Solution is utilized to assess the risk prioritization of airborne networks using the Pythagorean fuzzy weighted distance measure, which is used to address the cognitive uncertainty in the evaluation process of various indicators in airborne network threat scenarios. Finally, a comparative analysis was conducted. The proposed method demonstrated the highest Kendall coordination coefficient of 0.952. This finding indicates superior consistency and confirms the efficacy of the method in addressing expert cognition during information security risk assessment for airborne networks.

With the continuous development of Airborne Wireless Sensor Networks (AWSN) in airplane monitoring systems, security isolation is facing increasingly serious insider threats. Due to the real-time bi-directional data exchange, attackers can exploit compromised nodes as the springboard to infiltrate the aircraft control domain and airline information services domain, thus stealing sensitive data or doing damage. However, existing zero-trust schemes have centralized deployment bottlenecks and static identity authentication problems, so it is difficult to combat insider threats caused by compromised nodes in the AWSN environments. In this paper, we propose a distributed zero-trust scheme with dynamic identity authentication called DzTrust in airborne wireless sensor networks. Distributed cross-domain deployment of zero-trust engines around dense AWSN nodes can decentralize the zero-trust processing load of centralized deployment. A lightweight Certificateless Aggregation Signcryption (CLASC) model suitable for AWSN is constructed to ensure the secure transmission of information flow. Meanwhile, we maintain a Regularized Behaviors Data Blockchain (RBD_chain) to securely store the behavioral data of AWSN nodes, which facilitates traceability and prevents information tampering. Incorporating dynamic trust evaluation with certificateless authentication, a dynamic identity authentication method is designed, which effectively identifies and mitigates compromised nodes. Security analysis shows that the DzTrust scheme has high security against eavesdropping attack, replay attack, impersonation attack, and man in the middle attack. Performance analysis shows that the DzTrust scheme has good performance in identifying compromised nodes, as well as high computational and communication efficiency.

System wide information management (SWIM) involves civil aviation system control, intelligence, alarm, traffic, and other data. These data are transmitted in various forms, making SWIM system vulnerable to sensitive information leakage, data tampering, denial, and other security threats. In this article, an attribute-based air traffic management (ATM) information access control scheme is proposed to solve the security threat of SWIM. An improved extensible access control markup language (XACML) authorization model is established, combining linear secret sharing scheme (LSSS) matrix structure and monotone span program (MSP); an attribute association algorithm is designed to establish the attribute association relationship between services and users. Experimental results show that the attribute association algorithm improves the time complexity, but the algorithm can support richer policy representation capability, and the proposed ATM information access control scheme is more efficient and can effectively reduce the space cost. This scheme can achieve more fine-grained and flexible access control.

In wide-area distributed scenarios, it is particularly important to carry out information security situational awareness for the air traffic management (ATM) system with integrated air-ground structure. The operation data of the communication, navigation and surveillance (CNS) equipment of ATM system have the characteristics of multi-dimension, complexity, and strong correlation. In the process of situation awareness feature extraction, there are problems such as poor model accuracy, weak feature expression ability, and low classification performance. A feature association algorithm is designed to solve the above problems. Based on this algorithm, a deep-related sparse autoencoder (DRSAE) model based on improved sparse autoencoder is established. In DRSAE model, L₁ regularization and KullbackLeibler divergence (KLD) sparsity terms are used to penalize the parameters of the encoder network, and the quantity of hidden layers is increased to allow the model to optimize the global encoder network by iteratively training a single encoder. Moreover, the proposed DRSAE model and other feature extraction models such as principal component analysis (PCA), autoencoder (AE), and sparse autoencoder (SAE) are compared and evaluated by using the support vector machine (SVM) classifier. Compared with other feature extraction models, it is found that the proposed DRSAE model has good robustness in feature extraction of ATM system, and the obtained features have strong expression ability, which enhances the classification performance of the model and is convenient for situation awareness.

This study addresses threat scenario identification and quantitative evaluation in the context of the information security risk assessment process for airborne networks. A method integrating an improved system-theoretic process analysis (STPA) and the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. A safety control interaction structure is first established based on the system-level loss definition to identify unsafe control actions and derive STPA-Sec threat conditions. Then, the opinion aggregation method based on the weakest t-norm is introduced to address the uncertainty from insufficient expert consensus and opinion deviation. To address the intrinsic correlations among evaluation indicators, the criteria importance through intercriteria correlation (CRITIC) method is applied to determine indicator weights. These weights are subsequently incorporated into the TOPSIS framework to assess the identified threat scenarios. Comparative analysis confirms the effectiveness of the proposed approach. The results show that the improved STPA-TOPSIS method enables the reliable identification of security threats in airborne networks and supports the prioritization of threat scenarios by severity, facilitating the implementation of targeted mitigation strategies.

The system wide information management (SWIM) system serves as an advanced platform for managing and sharing diverse aviation data, such as aeronautical, flight, and meteorological information. With the exponential growth of aviation data, ensuring data security and privacy within SWIM has become increasingly important. Ciphertext-policy attribute-based encryption (CP-ABE) is widely regarded as an effective cryptographic technique for ensuring secure and private data access. However, traditional CP-ABE schemes rely on computationally expensive bilinear pairings, which are unsuitable for resource-constrained aviation Internet of Things (IoT) application scenarios in SWIM, such as Aircraft Access to SWIM (AAtS). To address this limitation, we propose a lightweight multiauthority CP-ABE scheme based on dynamic attributes. This scheme replaces bilinear pairing operations with efficient scalar multiplication on elliptic curve points and outsources most decryption operations to outsourcing decryption nodes, thereby significantly reducing computational overhead. In addition, this scheme introduces a dynamic attribute mechanism to support fine-grained access control, and outsources data verification and attribute revocation to the blockchain to ensure data integrity. Security analysis shows that our scheme achieves indistinguishability under adaptive chosen-ciphertext attacks (IND-CCAs) and is resistant to collusion attacks. Experimental evaluations further demonstrate that our scheme significantly enhances computational and communication efficiency.