The Sixth Generation Heterogeneous Network (6G HetNet) is a global interconnected system that serves a myriad variety of applications and services across multiple domains such as satellite, air, ground, and underwater networks. It provides a platform for the development of novel Internet of Things (IoT) applications and services, particularly for the Internet of Vehicles (IoV), which encompasses all devices involved in intra-vehicle and inter-vehicle communications. However, this evolution towards a unified and huge cellular infrastructure creates new security challenges that require an intelligent attack detection framework to safeguard the network against cyber-security threats. This paper proposes a hierarchical attack detection framework for 6G-enabled IoV. This framework relies on the processing capacities of edge nodes to satisfy the main 6G Key Performance Indicators (KPIs), such as trustworthiness, latency, connectivity, data rate and energy consumption. Federated Learning (FL) and non-cooperative gaming are used to train attack models and improve the detection process over time. The cooperative detection process based on FL is executed by security entities, IoV devices, edge servers and Security Information and Event Management (SIEM) to improve the detection accuracy over time. To harden the security of the proposed attack detection framework, a robust Stackelberg security game is developed to identify malicious IoV devices and edge servers, and select suitable IoV devices and edge servers to participate in the training and attack detection processes. The identification and selection process mainly relies on computing a reputation score based on the activities of these IoV devices and edge servers. As compared to current security monitoring and detection solutions, our framework balances detection accuracy and reduced network overhead, specifically as the system scales up, i.e., when the malicious traffic is high. In addition, it mitigates threats from both external and internal adversaries

For better data availability and accessibility while ensuring data secrecy, end-users often tend to outsource their data to the cloud servers in an encrypted form. However, this brings a major challenge to perform the search for some keywords over encrypted content without disclosing any information to unintended entities. This paper proposes a novel expressive authorized keyword search scheme relying on the concept of ciphertext-policy attribute-based encryption. The originality of the proposed scheme is multifold. First, it supports the generic and convenient multi-owner and multi-user scenario, where the encrypted data are outsourced by several data owners and searchable by multiple users. Second, the formal security analysis proves that the proposed scheme is semantically secure against chosen keyword and outsider's keyword guessing attacks. Third, an interactive protocol is introduced which avoids the need of any secure channels between users and service provider. Fourth, due to the concept of bilinear-map accumulator, the system can efficiently revoke users and/or their attributes, and authenticate them prior to launching any expensive search operations. Fifth, conjunctive keyword search is provided thus enabling to search for multiple keywords simultaneously, with minimal cost. Sixth, the performance analysis shows that the proposed scheme outperforms closely-related works.

International audienceSeveral existing access control solutions mainly focus on preserving confidentiality of stored data from unauthorized access and the storage provider. Moreover, to keep sensitive user data confidential against untrusted servers, existing solutions usually apply cryptographic methods by disclosing data decryption keys only to authorized users. However, these solutions inevitably introduce a heavy computation overhead on the data owner for key distribution and data management when fine-grained data access control is desired. In addition, access control policies as well as users' access patterns are also considered as sensitive information that should be protected from the cloud. In this paper, we propose PAbAC, a novel privacy preserving Attribute-based framework, that combines attribute-based encryption and attribute-based signature mechanisms for securely sharing outsourced data via the public cloud. Our proposal is multifold. First, it ensures fine-grained cryptographic access control enforced at the data owner's side, while providing the desired expressiveness of the access control policies. Second, PAbAC preserves users' privacy, while hiding any identifying information used to satisfy the access control. Third, PAbAC is proven to be highly scalable and efficient for sharing outsourced data in remote servers, at both the client and the cloud provider sid