14 results about "Sybil attack" patented technology

The application provides a Sybil attack identification method and device for intelligent networked vehicles, wherein the method comprises the following steps: obtaining three identity-unknown nodes including a malicious node, a Sybil node and a normal node, and reference signal receiving power (RSRP) sequences of the three identity-unknown nodes; finding mutation points of the RSRP sequences of the three identity-unknown nodes by using a Bernaola Galvn segmentation algorithm, and determining the RSRP sequence of the malicious node according to the number of the mutation points; calculating the average value of the difference between the RSRP sequence of the malicious node and the RSRP sequences of the other two identity-unknown nodes, adding the average value of the difference to the RSRP sequence of the malicious node, and obtaining the power-restored RSRP sequence of the malicious node; and calculating the similarity between the power-restored RSRP sequence of the malicious node and the RSRP sequences of the other two identity-unknown nodes corresponding to the power-restored RSRP sequence of the malicious node, and determining the Sybil node according to the similarity. By using the method, the malicious node that initiates a Sybil attack and the Sybil node generated by the malicious node can be identified in the intelligent networked vehicle.

The Global Influence Ledger (GIL) is a distributed ledger system and method for securely recording and verifying influence, trust, reputation, and governance events in a decentralized network. It includes a smart schema layer for data structuring with time-based decay rules, an event logging module for timestamping and hashing, a distributed node framework for consensus verification, an identity anchoring layer with privacy-preserving proofs and biometric hashing to prevent Sybil attacks, and a permissioned access interface for querying and auditing with Merkle proofs. The system ensures immutability, transparency, and interoperability, preventing fraud and enabling portable reputation across ecosystems.

The invention discloses a Sybil attack dynamic defense method and system in an electric power carbon transaction block chain. The method comprises the following steps: initializing a system, and initializing a block chain network and model parameters; sybil attack detection is carried out, data are preprocessed, and different node types are identified by using GAT and a multi-scale parallel convolution technology in combination with AFF; performing node state evaluation, and performing node comprehensive scoring and grading according to various indexes such as a detection result; and performing dynamic response, and executing main node election, malicious node isolation, victim node score compensation and conservative communication mode triggering according to an evaluation result. According to the method, attack detection and dynamic defense based on node state evaluation are combined, so that the sybil attack detection accuracy of the electric power carbon transaction block chain is improved, the defense capability of the electric power carbon transaction block chain to the sybil attack is enhanced at a relatively high malicious node rejection rate, and the safety of the electric power carbon transaction is guaranteed.

The present invention relates to the field of drone technology, and specifically discloses a method and system for defending against Sybil attacks in a drone self-organizing network. The method comprises data collection and feature extraction, deep learning model training, Sybil attack detection, secure routing and network management, dynamic adjustment and feedback; the system comprises a routing module and a Sybil attack detection module. By introducing deep learning technology and a comprehensive security assessment mechanism, the method provides more intelligent, accurate and adaptive security protection for the drone self-organizing network, effectively improving the detection capability of Sybil attack behaviors and the overall security of the network, thereby solving the problem that nodes in the drone self-organizing network are vulnerable to Sybil attacks.

The invention discloses an efficient privacy protection authentication scheme for an edge-assisted Internet of Things security warning system, belongs to the technical field of Internet of Things privacy protection, and is based on an improved linkable group signature mechanism and a security warning system architecture. The security warning system architecture comprises a trusted authorization mechanism TA, edge nodes ENs and equipment. Comprising a system initialization stage, an equipment registration stage, a message transmission stage, a verification and decryption stage and a malicious tracing stage. According to the efficient privacy protection authentication scheme for the edge-assisted Internet of Things security warning system, balance between anonymity and responsibility of the edge nodes can be realized, key challenges of maintaining privacy while ensuring security early warning integrity are completed, Sybil attacks are effectively resisted, and the security early warning system has the advantages that the security early warning integrity is ensured, the security early warning safety is ensured, and the security early warning safety is ensured. And safety measures in the system are further enhanced.

The invention relates to a decentralized identity authentication method and system of a light and simple framework, and the method comprises the steps: generating a public and private key pair locally, constructing a DID identifier, assembling a DID document, and uploading the DID document to a decentralized storage; oIDC authentication or payment signature KYC authentication with a third-party platform specified by a user is carried out, and Web2 authentication information is returned to realize DID binding; and based on the Web2 authentication information, verification is carried out through a local zero-knowledge proof circuit, and the DID identifier and a document address are bound and linked to realize identity authentication. Compared with the prior art, the method has the advantages of reduced deployment cost and threshold, identity migration from Web2 to Web3, strong anti-Sybil attack ability, full stack decentralization, strong standard compatibility and ecological interoperability, good privacy protection depth and flexibility and the like.

This invention belongs to the field of distributed artificial intelligence and decentralized network technology, and solves the problems of trust cold start, Sybil attacks, and cross-domain trust breakdown in A2A (Agent-to-Agent) economic networks. Based on the DHUC self-other-fault model, a trust weight formula is proposed: W_trust=ε×C_obs×10 3 ×(1+HCC_valid / HCC_total)×G_heritage achieves zero-collateral trust establishment; the uniqueness of the 428571 seven-dimensional genes is used to calculate the witch weight W_sybil=(C×S×G) / 10□, identifying and isolating cloning attacks; cross-chain trust lossless transfer is achieved through Trust Capsule and zero-knowledge proof; a triadic consensus mechanism is adopted to coordinate multi-agent cross-domain collaboration. This invention constructs a meta-layer trust infrastructure for A2A networks, applicable to Sigil Wen-style Automaton interaction networks, Justin Sun's Web 4.0 multi-chain AI infrastructure, and ERC-8004 standard agent payment networks, defending against dimensionality fall attacks and genetic contamination.

The application discloses a double-layer filtering cooperative federated learning method, system, device and storage medium. The method divides the model parameters into two parts, which are used for privacy protection and attack detection respectively. The server uses clean tail parameters for the first layer of malicious behavior detection, and applies strong privacy protection generated by multi-party cooperation to the head parameters. The first layer of filtering screens regular malicious attacks based on a dynamic norm boundary, and the second layer of filtering finds cooperative traces of Sybil attacks in locally encrypted data. This scheme solves the problem that encrypting or adding noise to the entire model in the prior art will disturb the model update features, can effectively distinguish between normal updates and malicious attacks, avoids relying on a single indicator to make it impossible to cope with adaptive attacks, and can accurately identify Sybil attacks, forming a set of federated learning technical solutions with strong privacy protection and accurate malicious attack identification capability.

The invention discloses a verifiable certificate decentralized issuing and security verification scheme in a self-sovereignty identity system, and belongs to the technical field of password protocol design and privacy protection. The method comprises a system initialization stage, a verifiable voucher issuing stage, a selective disclosure and verification stage and a verifiable voucher revocation stage, and is characterized in that decentralized verifiable voucher issuing is realized by using a threshold Point-Sanders signature technology; when the user performs selective disclosure by using the verifiable certificate, pseudo identities are generated based on disclosure attributes and timestamps, and different pseudo identities are generated in each disclosure to resist long-term tracking attacks; and the verifier confirms the legality of the user identity by verifying the false identity and the signature. According to the method, security threats such as long-term tracking, Sybil attacks and replay attacks are effectively resisted while a selective disclosure function is ensured, and the privacy protection capability and practicability of a self-sovereignty identity system are improved.

The invention discloses a trusted DHT network protocol architecture based on TEE authentication. A TEE authentication module is responsible for initializing a TEE environment, generating an identity certificate of a node, executing remote certification and verifying an authentication report of the node; the trusted routing management module maintains a trust weighted K-Buck structure, manages a connection relationship between nodes, and dynamically adjusts a routing strategy according to a trust score; the trust evaluation module collects node behavior data, calculates and updates trust scores of the nodes, and realizes a time attenuation mechanism of trust; the security protocol execution module realizes node addition, searching, storage and retrieval of a core protocol, and a TEE authentication and trust evaluation mechanism is integrated in each link; the defense mechanism module is used for realizing a defense algorithm aiming at specific security threats of the Sybil attack and the Eclipse attack; and the performance optimization module manages the authentication cache, executes hierarchical query optimization and adaptive parameter adjustment, and improves the system performance.

The invention provides an Internet of Vehicles malicious node detection method based on a multilevel reputation evaluation model, and belongs to the technical field of Internet of Vehicles security. The technical problems of low malicious vehicle detection precision, response lag and insufficient adaptability in the Internet of Vehicles environment are solved. According to the technical scheme, the method comprises the following steps: S1, quickly and preliminarily screening vehicle identities based on a multi-dimensional reputation index; s2, updating the direct reputation value of the vehicle; s3, updating the indirect reputation value of the vehicle; s4, updating the comprehensive reputation value of the vehicle; and S5, accurately judging the identity of the suspicious vehicle based on the comprehensive reputation value. The malicious node detection method based on the multilevel reputation evaluation model has the advantages that false identity camouflage in Sybil attacks and malicious recommendation behaviors in reputation manipulation attacks can be effectively inhibited, and screening efficiency and detection precision are both considered.

The invention discloses a manufacturing information credible traceability data evidence storage method based on a federated learning block chain, and designs a set of new security aggregation strategies to prevent the accuracy of manufacturing information traceability global model aggregation from being affected by model poisoning attack initiated by a toxic local model constructed by malicious client nodes. The security of the federal learning process and the reliability of credible quality tracing of manufacturing information are improved; a committee consensus mechanism based on a verifiable random function is reconstructed, the defense capability for Sybil attacks is enhanced, the network communication overhead and consensus delay are reduced as much as possible, and the iteration efficiency of a manufacturing information tracing global model is improved; moreover, an optimization means is also adopted for storage of model related data, so that the efficiency and expandability of collaborative operation between the model and the block chain are improved. The invention provides a solid and complete technical solution for realizing safe, reliable, efficient and credible traceable manufacturing information quality management in a complex supply chain.

This invention proposes a traceable, decentralized digital identity authentication method and system that supports Sybil attack resistance. The method includes a supervisory committee module, a user module, a certificate authority (CA) module, and a blockchain module. The supervisory committee module generates public and private keys. The user module obtains a traceability string, a unique string, and user attribute information of the user to be authenticated, and encrypts the traceability string. The CA module generates a credential based on the user attribute information and the encrypted traceability string. The user module generates a unique identifier, a traceability identifier, a Sybil attack-resistant zero-knowledge proof, and a traceability-guaranteed zero-knowledge proof. The blockchain module verifies the two zero-knowledge proofs. If verification succeeds, decentralized digital identity authentication is performed; if verification fails, decentralized digital identity authentication is not performed and identity authentication fails. This invention ensures user privacy and security, and ensures that the DID cannot be tampered with and is not controlled by a third-party central organization.

The invention particularly relates to an anonymous voucher identifier security construction method for realizing user inherent identity information binding, and belongs to the field of cryptography and security multi-party computing. Comprising the steps that a user generates a Pedersen commitment based on inherent identity information and signs the Pedersen commitment through a UIP; encrypting identity information by using a committee public key, and generating an auxiliary mask and a zero-knowledge proof; after the committee verifies, each member generates a voucher fragment based on the private key share and attaches zero knowledge proof; threshold number fragments are collected, and an anonymous voucher identifier is reconstructed through Lagrange interpolation; performing de-duplication detection based on the identifier to prevent repeated application; and if necessary, the committee performs threshold decryption to track malicious users. On the premise of protecting user privacy, unique certificate generation and safe duplicate removal are achieved, Sybil attacks are effectively resisted, controllable tracking is supported, anonymity and auditing performance are both considered, calculation expenditure is remarkably reduced, and expandability and deployability of the system are improved.