Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.

Internet of Drones (IoD) has become crucial due to the surge of drone-based applications. In a dynamic wireless network, drones often share data with multiple users, which is vulnerable to various security threats, e.g., eavesdropping, privacy leakage, and impersonation. Ensuring secure sharing of drone operation data is a critical issue. Existing schemes employ blockchain to ensure accountability in untrusted IoD contexts. However, drones perform excessive operations, e.g., consensus and homomorphic encryption, leading to impractical drone computation overhead. Furthermore, these existing schemes perform point-to-multipoint (P2MP) data sharing by using identity-based broadcast encryption (IBBE) as it has stateless receiver property, i.e., drones can directly establish sharing groups based on the receiver identities. However, these IBBE-based schemes need to preset the sharing group capacity, reducing sharing efficiency and flexibility in dynamic IoD networks. Also, identities in sharing groups probably result in receiver privacy breaches. In this paper, we propose a blockchain-assisted data-sharing scheme with accountability and privacy-preserving for IoD networks. We utilize on-chain immutable smart contracts and short signatures to construct a lightweight accountability mechanism that can verify and punish the above misbehavior. We develop an efficient privacy-preserving IBBE algorithm for P2MP sharing of drone operation data that enables dynamic group sharing. We prove that our algorithm is secure and receiver anonymity against chosen ciphertext attacks. The experiment results show that our scheme outperforms existing schemes in computational and communication overhead. In real-world logistics scenarios, the wireless data rate of 50 Mbps can satisfy the communication requirements for ciphertext lengths up to 19.5 KB. For a sharing group of 60 receivers, drone encryption costs only about 101 ms. The decryption cost remains constant and takes about 3.1 ms.

Crowd flow anomaly detection (CFAD) plays a vital role in ensuring public safety due to its capability to distinguish abnormal crowd movement behaviors from the norm. However, the influence of coexisting spatiotemporal factors presents a substantial challenge in capturing the dynamic normal pattern. Moreover, the inherent similarities within the original crowd flow data necessitate the creation of a discriminative feature space. To address this, we propose ProtoDetect , a novel method that learns distinguishable representations (named as prototypes) of the influencing factors. It subsequently identifies anomalous samples by comparing them with their normal counterparts, estimated based on the prototypes. Experimental evaluations on three real-world datasets demonstrate ProtoDetect’s consistent superior performance in CFAD. The source code is available at https://github.com/yupwang/ProtoDetect.

In vehicular cloud computing (VCC), cloud servers provide enormous storage and powerful computing capacity to Vehicular Ad-hoc Networks (VANETs). Resource-constrained vehicles outsource data to vehicular cloud platforms for timely traffic safety services, e.g., navigation, accident alarms, etc. Auditing the authenticity of data has become a critical issue in outsourcing data to untrusted servers. Existing data audit methods encode all data with error correction codes (ECC) techniques that retrieve corrupted data by downloading all data. The communication overhead of such methods is $O(n)$ ( $n$ is the number of data blocks) which is unbearable for vehicles with limited resources. In addition, these schemes employ an inaccurate privacy-preserving model. This will lead to data leakage in the third-party audit process. Although they use randomness to confuse parts of the proof that is used to prove the data state, a small amount of information is still distinguishable. For such, in this paper, we propose a practical data audit scheme with retrievability and indistinguishable privacy-preserving to efficiently audit the state of outsourced data. We improve the Invertible Bloom Filter (IBF) to compress redundancy locally, which can retrieve corrupted data without prior context. Furthermore, we define an indistinguishable privacy-preserving model to capture the complete semantics of repeated audit attacks and achieve indistinguishability in the audit. We prove that our scheme is secure against adaptive chosen message attacks and is indistinguishable privacy-preserving against repeated audit attacks. The experiment results demonstrate that for 1.9GB data when $\sqrt[]{n}$ blocks are corrupted, auditors complete a check in 3.31 seconds with 99% confidence, and vehicles retrieve corrupted data in 3.16 seconds with 16.67MB communication overhead.

Ensuring the integrity of remote data is the prerequisite for implementing cloud-edge computing. Traditional data integrity verification schemes make users spend a lot of time regularly checking their data, which is not suitable for large-scale IoT (Internet of Things) data. On the other hand, the introduction of a third-party auditor (TPA) may bring about greater privacy and security issues. We use blockchain to address the problem of TPA. However, implementing dynamic integrity verification with blockchain is a bigger challenge due to the low throughput and poor scalability of blockchain. More importantly, whether there is a security problem with blockchain-based integrity verification is not yet known. In this paper, we propose a scalable blockchain-based integrity verification scheme that implements fully dynamic operations and blockless verification. The scheme builds scalable homomorphic verification tags based on ZSS (Zhang-Safavi-Susilo) short signatures. We exploit smart contract technology to replace TPA for integrity verification tasks, which not only eliminates the risk of privacy leakage but also resists collusion attacks. Furthermore, we formally define a blockchain-based security model and prove that our scheme is secure under the security assumption of cryptographic primitives. Finally, the mathematical analysis of our scheme shows that both the communication complexity and the communication complexity of an audit are , in which is the number of challenge blocks. We compare our scheme with other schemes, and the results show that our scheme has the lowest time consumption to complete an audit.

Cross-chain bridges connect two heterogeneous and independent blockchains, in which router contracts interact with external modules to perform functions, e.g., locking assets. However, numerous attacks exploit external interaction vulnerabilities (EIVs) in router contracts. Detecting EIVs presents two key challenges: (1) extracting complete data dependencies, and (2) identifying access controls for off-chain entities. Existing approaches, which transform contracts into intermediate representations for analysis, often miss critical execution logic (e.g., path conditions) and data flows (e.g., data flows at the instruction parameter level). Additionally, the path explosion severely impacts analysis efficiency. In this paper, we systematically analyze real-world attacks and extract the precise features of two typical EIVs. We propose BridgeGuard, a static analysis framework for detecting EIVs. Specifically, we present a symbolic dataflow analysis approach to capture complete execution logic and fine-grained data flows. We then identify three common authentication patterns for off-chain entities and deploy a heuristic-driven pruning method to solve the path explosion. We implement an automated analysis prototype. The experimental results show that for analyzing 1,035 wild contracts with an average scale of 15,914 bytes, BridgeGuard achieves 87.4% average code coverage. For detecting two typical EIVs, BridgeGuard achieves F1-scores above 0.93.