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825 result(s) for "Logic of events"
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Formal analysis of signal protocol based on logic of events theory
The Signal is an end-to-end encrypted communication protocol composed of a double ratchet (DR) protocol and an extended triple Diffie-Hellman (X3DH) protocol. Its complex ratchet structure and the characteristics of protocol composition make it challenging to realize formal analysis. A formal analysis method based on logic of events theory (LoET) is proposed to conduct a security analysis of the Signal protocol. The method includes inference rules with key relation and key chain as the core to realize the formal analysis of ratchet structure, and the inference relation between sub-protocols is established by putting forward the composition theorem. The proposed method achieves a formal analysis of Signal, revealing that it does not satisfy a strong authentication property during the X3DH phase. The results show that the LoET-based method can be effectively applied in the formal analysis of Signal protocols, thus promoting the application and development of these protocols with ratchet structure and composition properties.
Formal Analysis of EAP-TLS Protocol Based on Logic of Events
The Extensible Authentication Protocol–Transport Layer Security (EAP-TLS) is a critical authentication protocol for wireless networks and secure IoT communications. However, it faces significant challenges from man-in-the-middle attacks, including message tampering, replay, and certificate forgery. Although model checking techniques have been applied to verify its security properties, the complexity of the EAP-TLS handshake often prevents accurate formal modeling; existing studies rarely assess the communication overhead of protocol enhancements. Moreover, traditional Logic of Events Theory (LoET) struggles to handle transport-layer protocols like EAP-TLS due to their intricate interaction processes. This study proposes a novel formal analysis approach, extending LoET by expanding five event classes, formulating corresponding rules, and introducing new axioms. Formal verification reveals attack paths involving plaintext theft, message tampering, and entity impersonation. The research proposes an enhanced strategy to mitigate these vulnerabilities through hash merging, encryption, and signature methods, alongside analyzing their communication costs to ensure feasibility. Using the extended LoET, the improved protocol is rigorously proven to satisfy strong authentication, thereby enhancing practical security. The proposed method achieves a time complexity of O(n2) and demonstrates superior performance in resisting state explosion compared with related approaches, thus establishing a more efficient and robust framework for EAP-TLS security analysis.
Formal Analysis of Ratchet Protocols Based on Logic of Events
Ratchet protocols are a class of secure protocols based on ratcheting encryption mechanisms, widely employed in instant messaging. Against the backdrop of frequent incidents of communication privacy breaches, ratchet protocols have become a vital technology for ensuring secure end-to-end communication. This paper presents a formal analysis framework for ratchet protocols grounded in Logic of Events theory (LoET). We further extend LoET by introducing dedicated Diffie–Hellman (DH) and ratchet event classes, along with tailored axioms and inference rules, to support precise modeling of ratcheted encryption. Using the Signal protocol as a case study, we construct a bidirectional authentication model and rigorously prove that both its symmetric and asymmetric ratchet phases satisfy strong authentication properties. Compared with existing formal approaches, our method enables more expressive modeling of key update sequences and supports structured reasoning over causality and authentication flows. The proposed framework lays a theoretical foundation for analyzing the security of modern ratcheted protocols and holds potential for future automated verification.
Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
Security analysis of composite protocols is a critical issue in the field of network security. In complex network environments, the traditional approach of analyzing a single protocol becomes inadequate when dealing with scenarios involving multiple interactions and combinations of protocols. To address this challenge, this paper extends the Logic of Events Theory (LoET) and proposes a method for proving the security of composite protocols. Building upon the Logic of Events Theory, we introduce sequential composition rules, ordering rules, and relevant axioms. We incorporate the concept of invariants and formally abstract the DTLS-SRTP protocol, thereby verifying the mutual authentication and confidentiality of the two sub-protocols. In conclusion, our study demonstrates that the extended Logic of Events Theory offers an effective means of verifying the security of composite protocols.
Proving Mutual Authentication Property of Industrial Internet of Things Multi-Factor Authentication Protocol Based on Logic of Events
Security protocols are the basis of modern network communication, proving that the security problem of protocols is one of the hot research topics today. The data in industrial IoT are usually transmitted through insecure channels, which brings certain security risks. The Logic of Events is a formal method for proving the security properties of protocols based on event systems. The new theoretical extension is based on the Logic of Events theory, which proposes new event classes Compurte, TimeGap, Construct, and Reconstruct and an axiom AxiomRe and related inference rules for malicious attacks and security privacy issues in emerging protocols, as well as extending the matching descriptions of protocol behaviours in complex cryptographic algorithms and information sharing techniques for applications for the formal analysis of authentication protocols for the Industrial Internet of Things. Finally, formal analysis is carried out using the example of a secure multi-factor authentication protocol for the industrial IoT, which proves the security of the protocol.
Formal Security Analysis of ECC-Based RFID in Logic of Events Theory
Radio frequency identification (RFID) is a crucial component of the Internet of Things (IoT), and RFID using elliptic curve Cryptography (ECC) is a public key cryptosystem authentication approach that tackles the problem of electronic tag data encryption in RFID systems. The commercialisation and large-scale deployment of RFID systems has raised a number of security-related issues that suggest the need for security protocols. Logic of events theory (LoET) is a formal method for constructing and reasoning about distributed systems and protocols involving security concepts. This paper proposes three event classes, Compute, Retrieve, and Generate, and related axioms and inference rules to formally abstract the ECC session key generation function and formally institute the authentication process of both parties, and the ex-tended LoET is used to analyse the security properties of ECC-based RFID security protocols. Under reasonable assumptions, an ECC-based RFID mutual authentication scheme is shown to satisfy the strong mutual authentication feature. It is shown that extended logic of events theory may be used to prove the security properties of this class of ECC-based RFID protocols.
Bridging a gap: in search of an analytical tool capturing teachers’ perceptions of their own teaching
Computing and computers are introduced in school as important examples of technology, sometimes as a subject matter of their own, and sometimes they are used as tools for other subjects. All in all, one might even say that learning about computing and computers is part of learning about technology. Lately, many countries have implemented programming in their curricula as a means to address society’s dependence on, and need for programming knowledge and code. Programming is a fairly new school subject without educational traditions and, due to the rapid technological development, in constant change. This means that most programming teachers must decide for themselves what and how to teach. In this study, programming teachers’ teaching is studied. With the aim of exploring the connection/possible gap between teacher’s intentions and the teacher’s instructional practice , an expansion of the conceptual apparatus of phenomenography and variation theory is tested. In the article, phenomenography and variation theory and the suggested supplementary theoretical tool (Georg Henrik von Wright’s model of logic of events) are briefly presented and then deployed upon one selected case. Findings reveal that teachers’ intentions (reflected in their actions) include an emphasis (of teachers’ side) on the importance of balancing theory and practice, using different learning strategies, encouraging learning by trial-and-error and fostering collaboration between students for a deeper understanding of concepts. In conclusion, logic of events interpretations proves to be useful as a complementary tool to the conceptual apparatus of phenomenography.
Towards a Unified Temporal and Event Logic Paradigm for Multi-Hop Path Reasoning in Knowledge Graphs
Path reasoning in knowledge graphs is a pivotal task for uncovering complex relational patterns and facilitating advanced inference processes. It also holds significant potential in domains such as power electronics, where real-time reasoning over dynamic, evolving data is essential for advancing topology design and application systems. Despite its importance, traditional approaches often encounter substantial limitations when applied to dynamic, time-sensitive scenarios. These models typically fail to adequately capture intricate logical dependencies and demonstrate suboptimal performance in data-constrained environments. To address these challenges, we introduce Path-Reasoning Logic (PRlogic), an innovative framework that seamlessly integrates rule-based logical reasoning with cutting-edge neural network methodologies. PRlogic enhances path inference by leveraging a context-aware logical association network adept at handling temporal and event-driven attributes, enabling improved reasoning for dynamic systems such as IoT-based power electronics and smart grids. This adaptability allows the framework to better accommodate evolving knowledge structures, significantly improving reasoning accuracy under resource-scarce conditions. Furthermore, PRlogic employs a multi-stage refinement strategy, harmonizing logic-based rules with learned contextual representations to achieve heightened robustness and scalability. Comprehensive experiments on widely-recognized benchmark datasets validate the superiority of PRlogic, demonstrating its consistent outperformance of existing models in path reasoning tasks. These results underscore the efficacy of incorporating logic-driven mechanisms into knowledge graph reasoning and highlight PRlogic’s potential as a powerful solution for applications in dynamic data environments.
Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis
The advance of knowledge graphs can bring tangible benefits to the fault detection of industrial robots. However, the construction of the KG for industrial robot fault detection is still in its infancy. In this paper, we propose a top-down approach to constructing a knowledge graph from robot fault logs. We define the event argument classes for fault phenomena and fault cause events as well as their relationship. Then, we develop the event logic ontology model. In order to construct the event logic knowledge extraction dataset, the ontology is used to label the entity and relationship of the fault detection event argument in the corpus. Additionally, due to the small size of the corpus, many professional terms, and sparse entities, a model for recognizing entities for robot fault detection is proposed. The accuracy of the entity boundary determination of the model is improved by combining multiple text features and using the relationship information. Compared with other methods, this method can significantly improve the performance of entity recognition of the dataset.