While 5G can facilitate high-speed Internet access and make over-the-horizon control a reality for unmanned aerial vehicles (UAVs; also known as drones), there are also potential security and privacy considerations, for example, authentication among drones. Centralized authentication approaches not only suffer from a single point of failure but they are also incapable of cross-domain authentication. This complicates the cooperation of drones from different domains. To address these limitations, a blockchain-based cross-domain authentication scheme for intelligent 5G-enabled Internet of drones is proposed in this article. Our approach employs multiple signatures based on threshold sharing to build an identity federation for collaborative domains. This allows us to support domain joining and exiting. Reliable communication between cross-domain devices is achieved by utilizing smart contract for authentication. The session keys are negotiated to secure subsequent communication between two parties. Our security and performance evaluations show that the proposed scheme is resistant to common attacks targeting Internet of Things (IoT) devices (including drones), as well as demonstrating its effectiveness and efficiency.

Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as drones) are widely applied in various industries. However, the open nature of 5G networks threatens the safe sharing of data. In particular, privacy leakage can lead to serious losses for users. As a new machine learning paradigm, federated learning (FL) avoids privacy leakage by allowing data models to be shared instead of raw data. Unfortunately, the traditional FL framework is strongly dependent on a centralized aggregation server, which will cause the system to crash if the server is compromised. Unauthorized participants may launch poisoning attacks, thereby reducing the usability of models. In addition, communication barriers hinder collaboration among a large number of cross-domain devices for learning. To address the abovementioned issues, a blockchain-empowered decentralized horizontal FL framework is proposed. The authentication of cross-domain UAVs is accomplished through multisignature smart contracts. Global model updates are computed by using these smart contracts instead of a centralized server. Extensive experimental results show that the proposed scheme achieves high efficiency of cross-domain authentication and good accuracy.

The Internet of Health Things (IoHT) is a medical concept that describes uniquely identifiable devices connected to the Internet that can communicate with each other. As one of the most important components of smart health monitoring and improvement systems, the IoHT presents numerous challenges, among which cybersecurity is a priority. As a well-received security solution to achieve fine-grained access control, ciphertext-policy weighted attribute-based encryption (CP-WABE) has the potential to ensure data security in the IoHT. However, many issues remain, such as inflexibility, poor computational capability, and insufficient storage efficiency in attributes comparison. To address these issues, we propose a novel access policy expression method using 0-1 coding technology. Based on this method, a flexible and efficient CP-WABE is constructed for the IoHT. Our scheme supports not only weighted attributes but also any form of comparison of weighted attributes. Furthermore, we use offline/online encryption and outsourced decryption technology to ensure that the scheme can run on an inefficient IoT terminal. Both theoretical and experimental analyses show that our scheme is more efficient and feasible than other schemes. Moreover, security analysis indicates that our scheme achieves security against a chosen-plaintext attack.

DNS amplification attacks utilize IP address spoofing and large numbers of open recursive DNS servers to perform the bandwidth consumption attack. During an attack, it ceaselessly fabricates DNS queries to the exploited open recursive DNS servers, and all the responses, often with larger size than the query messages, are reflected to the single victim due to the source IP address spoofing. While it is difficult to defend against this attack from the root causes by eliminating the open recursive DNS servers and IP spoofing for the whole Internet, in this paper, we take a different methodology to defend against it at the leaf router of victim's ISP or organization. We propose an efficient and low-cost hardware approach to first detect the DNS amplification attack accurately and responsively. Once the attack is confirmed, our approach is then activated to filter out all the illegitimate DNS responses by using a two-Bloom filter solution. We demonstrate that the memory cost of our approach is feasible for the hardware implementation even up to the OC-768 link. Through trace-driven simulations, it is shown that our approach is effective in both the detecting and filtering phases.

The $k$-nearest neighbors (KNN) classification algorithm is widely used in machine learning for its simplicity and effectiveness in non-parametric settings. However, when applied to sensitive data, such as medical or financial records, ensuring the privacy of both the training dataset and user queries is crucial. Existing privacy-preserving KNN (PP-KNN) classification schemes often suffer from inefficiencies due to the sequential nature of operations like top-$k$ selection and majority voting. These operations require chains of dependent secure comparisons, resulting in communication rounds that scale with either the dataset size $n$ or the neighborhood size $k$ (e.g., $\mathcal {O}(n+k\log _{}{n})$ or $\mathcal {O}(kn)$). To address these challenges, we propose a constant-round PP-KNN classification scheme based on function secret sharing (FSS) in a non-colluding two-server setting. Specifically, we employ an FSS-based batch comparison algorithm to parallelize the secure comparisons in both top-$k$ selection and majority voting, thereby transforming iterative comparison chains into stage-wise constant-round evaluations. This design enables the entire classification pipeline to complete in a fixed 10 rounds, achieving a round complexity independent of both $n$ and $k$. Formal security analysis shows that the proposed scheme is secure under the semi-honest adversarial model. Furthermore, performance evaluations demonstrate that our approach achieves competitive computation and communication efficiency compared with existing solutions.

Recently in IEEE Access (DOI: 10.1109/ACCESS.2018.2890565), Li et al.[1] proposed a secure outsourcing algorithm for modular exponentiation in one single untrusted server model and a new method of generating transformation keys. They claimed that their solution can securely outsource encryption and decryption to untrusted ESP (encryption service providers) and DSP (decryption service providers), leaving only a constant number of simple operations for the DO (data owner) and eligible users to perform locally. In addition, both DO and qualified users can check the correctness of the results returned from ESP and DSP, respectively. Although the authors provide security proofs for their scheme, unfortunately, after carefully observing their scheme, we find that the scheme has security vulnerabilities. These vulnerabilities allow the adversary to generate the sub-key for any attribute and replace ciphertext sub-item, which result in the adversary to be able to break their scheme. In response to this problem, we propose an improved solution and proved its security.