With the widespread application of unmanned aerial vehicles (UAVs), intrusion detection based on wireless communication traffic has become increasingly critical. While deep learning (DL) models have significantly enhanced anomaly detection accuracy, recent research indicates that they remain vulnerable to adversarial sample attacks, which poses severe threats to the open communication networks used by UAV systems. To address this challenge, an enhanced UAV intrusion detection framework is proposed for defending against adversarial samples, integrating generating adversarial samples with adversarial networks (AdvGAN) and decision boundary reconstruction. Specifically, a convolutional neural network (CNN) is first developed as the baseline intrusion detection model to detect anomalous traffic in UAV wireless communication. Subsequently, to overcome the gradient-dependent perturbation limitations of traditional adversarial attack methods, AdvGAN is employed to analyze real monitored traffic data and generate semantically consistent adversarial samples that retain the intrinsic characteristics of authentic network flow. Finally, a hybrid adversarial-real data training strategy is proposed to dynamically reconstruct the model’s intrusion decision boundaries. This strategy augments the training dataset with AdvGAN-generated adversarial samples, enabling the model to adaptively adjust decision thresholds through iterative backpropagation. On a real-world dataset, the superior performance of proposed method is verified: it maintains high detection accuracy for legitimate traffic while substantially reducing the success rate of adversarial attacks. Experimental results confirm the stealthiness of generated adversarial samples and the robustness of the enhanced detection model, validating the effectiveness of the proposed framework.

With the advancement of NAND flash technology, the increased storage density leads to intensified interference, which in turn raises the error rate during data retrieval. To ensure data reliability, low-density parity-check (LDPC) codes are extensively employed for error correction in NAND flash memory. Although LDPC soft decision decoding offers high error correction capability, it comes with a significant latency. Conversely, hard-decision decoding, although faster, lacks sufficient error correction strength. Consequently, flash memory typically initiates with hard-decision decoding and resorts to multiple soft decision decoding upon failure. To minimize decoding latency, this paper proposes a decoding mechanism based on the double hard decision, called DHD. This DHD scheme improves the Log-Likelihood Ratio (LLR) in the hard decision process. After the first hard decision fails, the read reference voltage (RRV) is adjusted to perform the second hard decision decoding. If the second hard decision also fails, soft decision decoding is then employed. Experimental results demonstrate that when the Raw Bit Error Rate (RBER) is$8.5 \times 10^{-3}$, DHD reduces the Frame Error Rate (FER) by 86.4% compared to the traditional method.

With the rapid development of satellite communication technology, satellite networks have become an important component of global communication infrastructure. However, satellite networks face serious security challenges, particularly in node authentication and key management. Traditional interactive authentication protocols suffer from high latency and poor reliability issues in satellite networks. To address these challenges, this paper proposes a non-interactive authentication protocol for satellite networks based on time-lock encryption and elliptic curve cryptography. A satellite communication scenario has been constructed where the protocol achieves end-to-end communication latency of less than 1.5 seconds, transmission success rate of 100%, RSA-2048 decryption processing time of only 0.1 seconds, and time-lock verification completion within 5 milliseconds. The system employs RSA-2048 keys to provide 112-bit equivalent symmetric security strength, with multi-threaded decryption services maintaining 100% availability. The protocol combines delayed decryption technology, ring signature algorithms, and device fingerprint authentication to achieve secure authentication and key agreement between satellites.

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Things (IoT) components enable effective artificial intelligence (AI)-based models to be built. Beyond ground data sources, autonomous aerial vehicles (AAVs)-based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), have become increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned AAVs. To this end, we propose in this article a federated learning (FL)-based architecture that enables privacy-preserving collaborative machine learning across a federation of independent DaaS providers for the development of IoT applications. Specifically, this work introduces a novel incentive mechanism based on the hedonic coalition formation game to enhance the sustainable efficiency and stability of the FL system. By establishing tailored operational rules and functions, the proposed mechanism enables IoT sensing nodes to autonomously form optimal coalitions with AAVs, thereby ensuring robust collaboration. To deal with incentive mismatches and information asymmetry, we leverage the contract theory and propose a self-disclosure mechanism that guarantees truthful reporting of AAV capabilities while optimizing the global model owner’s profits. The performance-based AAV type is also defined to offer a practical measure for heterogeneous AAVs and serve as the foundation for fair and effective contract design. Simulation results validate the superiority of the proposed approach, demonstrating significant improvements in utility optimization and system stability compared to existing benchmarks.

In the current era when the Internet of Things (IoT) is booming and various smart devices are closely interconnected to build a complex network system, the demand for efficient control and resource management strategies is extremely urgent. Proximal Policy Optimization (PPO), as a highly representative algorithm in deep reinforcement learning, has great potential in aspects such as precise control of IoT devices, rational resource allocation, and intelligent interaction. However, in the practical applications of the IoT, PPO mostly exists as a black-box model, which is vulnerable to adversarial attacks. Moreover, the continuous action space scenarios applicable to PPO further increase the difficulty of analysis and protection. Therefore, this paper innovatively proposes a State-Attention Adversarial Attack (S3A) for black-box PPO models in continuous action space scenarios. This method is based on the model’s attention to states, has a low cost, integrates the parts of model extraction, action clustering, and decision-making interference of the model under specific states, and has a clear interference purpose. By constructing an IoT-related victim model in the MuJoCo environment provided by the Gym library and implementing adversarial attacks, experiments have found that under four repetitions in five simulation environments, S3A reduces the final reward of the victim model by an average of 48.5%, which strongly verifies the effectiveness and influence of this attack method.

Recent research on adversarial attacks shows that natural language processing models are vulnerable to adversarial examples. Current methods usually prove to be inadequate to successfully mislead the model while maintaining high semantic retention and fluency of the adversarial examples. These methods have difficulty in finding the global optimal solution among various, feasible possibilities of minor changes. Consequently, developing textual adversarial attacks that can produce high-quality adversarial examples continues to be a challenging endeavor. This paper proposes Hamiltonian text adversarial attack (HTAA); primarily, it generates a certain number of adversarial candidates with the guidance of limiting perturbations and gradient projections, expanding the searching scope. Besides, it utilises Hamiltonian simulation to efficiently select the final adversarial example that is closer to the global optimum among the candidates. Extensive experiments show that the proposed method improves semantic similarity and fluency, raising by 5% and 12.3% an average respectively; at the same time, it maintains a high successfully attack rate of more than 90% on four different datasets against the target model.

Abstract In recent years, meaningful visual image encryption schemes that the plain image is compressed and encrypted and then hidden into the carrier image have received increasing attention. This paper proposes a new meaningful visual image encryption scheme, which consists of three stages: compression (compression network)—encryption (2D‐SLC hyperchaotic map)—hiding (matrix encoding). First, the advantages of deep learning are explored. It can compress the width, height, channel, and pixel values of the plain image simultaneously. Second, a new 2D‐SLC hyperchaotic map is designed to ensure security. It has a larger chaotic space and better randomness. Finally, to obtain a high‐quality cipher image, the secure secret image is hidden in the grey carrier image by matrix encoding. The scheme can compress and encrypt the grey or colour plain image and then hide it in a grey carrier image. In addition, the theoretical peak signal‐to‐noise ratio (PSNR) between the cipher image and the carrier image is improved from 40.9292 to 42.1785 dB. The total running time is only about 0.35, 0.87 and 3.1 s for a 256 × 256, 512 × 512 and 1024 × 1024 grey or colour plain image, respectively.

Privacy protection and incentive mechanism are two fundamental problems in federated learning (FL), which aim at protecting the privacy of data owners and stimulating them to share more resources, respectively. Recent works have proposed differential privacy (DP) based privacy-preserving incentive mechanisms to solve both problems simultaneously. However, almost all of them took the privacy level as the only incentive item, without considering other factors, such as data quantity and quality. Moreover, an untrusted server can further infer sensitive information from the bids that reflect the true costs of data owners. To solve these problems, in this paper, we propose a dual-privacy preserving and quality-aware incentive mechanism, PrivAim, for federated learning. Specifically, it utilizes differential privacy to protect the local models and true costs against the untrusted parameter server, and carefully designs a multi-dimensional reverse auction mechanism to incentivize data owners with high quality and low cost to participate in FL without knowing the true bids. We theoretically prove that PrivAim satisfies$\Delta b$-truthfulness, individual rational, computational efficiency, and differential privacy. Extensive experiments show that PrivAim can effectively protect bid privacy, and achieve at least 21% and 6% improvement on social welfare and model accuracy, respectively, compared to the state-of-the-art.

The Internet of vehicles (IoV) has a substantial impact on traffic efficiency improvement and accidents avoidance. Due to restricted resources, vehicles must share observed data with RSUs and other vehicles to execute some time-tolerant computing tasks. However, data provided by vehicles cannot always be trusted due to the presence of attackers. Fake messages could have catastrophic ramifications, such as vehicle collisions. Furthermore, extensive data sharing might cause channel congestion, resulting in the loss of vital messages during delivery. To overcome the aforementioned issues, we propose TRUCON, a blockchain-based trusted data sharing mechanism with congestion control in IoV. Firstly, we propose a Kademlia algorithm-based traffic data forwarding method to control channel congestion state. By adjusting the bucket size and distance threshold, source vehicles can limit the number of reference vehicles forwarded. Secondly, we present a cuckoo filter-based traffic data deduplication and discrimination approach. To avoid repetitive sharing, vehicles and RSUs can check their local filters to verify if the current data report has been shared. Based on the foregoing, we propose a blockchain-based trust management mechanism with congestion control. RSUs serve as full nodes while vehicles are light nodes in the blockchain. Finally, we develop a trust management prototype system with congestion control that incorporates both on-chain and off-chain parts. It signifies that our scheme is both feasible and effective.

Internet of Things (IoT) is the development and extension of computer, Internet, and mobile communication network and other related technologies, and in the new era of development, it increasingly shows its important role. To play the role of the Internet of Things, it is especially important to strengthen the network communication information security system construction, which is an important foundation for the Internet of Things business relying on Internet technology. Therefore, the communication protocol between IoT devices is a point that cannot be ignored, especially in recent years; the emergence of a large number of botnet and malicious communication has seriously threatened the communication security between connected devices. Therefore, it is necessary to identify these unknown protocols by reverse analysis. Although the development of protocol analysis technology has been quite mature, it is impossible to identify and analyze the unknown protocols of pure bitstreams with zero a priori knowledge using existing protocol analysis tools. In this paper, we make improvements to the existing protocol analysis algorithm, summarize and learn from the experience and knowledge of our predecessors, improve the algorithm ideas based on the Apriori algorithm idea, and perform feature string finding under the idea of composite features of CFI (Combined Frequent Items) algorithm. The advantages of existing algorithm ideas are combined together to finally propose a more efficient OFS (Optimal Feature Strings) algorithm with better performance in the face of bitstream protocol feature extraction problems.

Android system has been one of the main targets of hacker attacks for a long time. At present, it is faced with security risks such as privilege escalation attacks, image tampering, and malicious programs. In view of the above risks, the current detection of the application layer can no longer guarantee the security of the Android system. The security of mobile terminals needs to be fully protected from the bottom to the top, and the consistency test of the hardware system is realized from the hardware layer of the terminal. However, there is not a complete set of security measures to ensure the reliability and integrity of the Android system at present. Therefore, from the perspective of trusted computing, this paper proposes and implements a trusted static measurement method of the Android system based on TrustZone to protect the integrity of the system layer and provide a trusted underlying environment for the detection of the Android application layer. This paper analyzes from two aspects of security and efficiency. The experimental results show that this method can detect the Android system layer privilege escalation attack and discover the rootkit that breaks the integrity of the Android kernel in time during the startup process, and the performance loss of this method is within the acceptable range.

Mobile botnet has recently evolved due to the rapid growth of smartphone technologies. Unlike legacy botnets, mobile devices are characterized by limited power capacity, calculation capabilities, and wide communication methods. As such, the logical topology structure and communication mode have to be redesigned for mobile botnets to narrow energy gap and lower the reduction speed of nodes. In this paper, we try to design a novel Gini‐based energy balancing scheme (Gleer) for the atomic network, which is a fundamental component of the heterogeneous multilayer mobile botnet. Firstly, for each operation cycle, we utilize the dynamic energy threshold to categorize atomic network into two groups. Then, the Gini coefficient is introduced to estimate botnet energy gap and to regulate the probability for each node to be picked as a region C&C server. Experimental results indicate that our proposed method can effectively prolong the botnet lifetime and prevent the reduction of network size. Meanwhile, the stealthiness of botnet with Gleer scheme is analyzed from users’ perspective, and results show that the proposed scheme works well in the reduction of user’ detection awareness.

Botnet has become a popular technique for deploying Internet crimes. The command of botnet has evolved into a major way for attackers to launch Distributed Denial of Service attacks on network servers. Modelized analysis methods need to be studied for botnet attacks implements, defense, and prediction. In this paper, we propose a novel game theory-based model to describe the scenario, in which the botmaster launching Distributed Denial of Service attacks using a botnet while the defender equipped a firewall defending. In our model, we consider the following: firstly, the botmaster and the defender can be rational or irrational; secondly, the interaction between the botmaster and the defender is modeled as a dynamic game; thirdly, their supporting or not self-learning databases. We detail the analysis of eight sub-scenarios for the assumptions and give an easy-to-use algorithm for adjustment of offensive and defensive strategy. We use the OPNET to validate our model and its effectiveness. The experiment result shows that our strategy can improve the firewall abilities to lower false alarm rate FR and improve the botmaster lower exposure rate of botnet to avoid detection. Furthermore, the model is helpful to evaluate defense ability of the defender towards current botmaster attacks by analyzing attack log in sandbox. Copyright © 2016 John Wiley & Sons, Ltd.