Cyber Threat Intelligence (CTI) reports are valuable resources in various applications but manually extracting information from them is time-consuming. Existing approaches for automating extraction require specialized models trained on a substantial corpus. In this paper, we present an efficient methodology for constructing knowledge graphs from CTI by leveraging the Large Language Model (LLM), using ChatGPT for instance. Our approach automatically extracts attack-related entities and their relationships, organizing them within a CTI knowledge graph. We evaluate our approach on 13 CTIs, demonstrating better performance compared to AttacKG and REBEL while requiring less manual intervention and computational resources. This proves the feasibility and suitability of our method in low-resource scenarios, specifically within the domain of cyber threat intelligence.

Entity Alignment (EA) has attracted widespread attention in both academia and industry, which aims to seek entities with same meanings from different Knowledge Graphs (KGs). There are substantial multi-step relation paths between entities in KGs, indicating the semantic relations of entities. However, existing methods rarely consider path information because not all natural paths facilitate for EA judgment. In this paper, we propose a more effective entity alignment framework, RPR-RHGT, which integrates relation and path structure information, as well as the heterogeneous information in KGs. Impressively, an initial reliable path reasoning algorithm is developed to generate the paths favorable for EA task from the relation structures of KGs. This is the first algorithm in the literature to successfully use unrestricted path information. In addition, to efficiently capture heterogeneous features in entity neighborhoods, a relation-aware heterogeneous graph transformer is designed to model the relation and path structures of KGs. Extensive experiments on three well-known datasets show RPR-RHGT significantly outperforms 10 state-of-the-art methods, exceeding the best performing baseline up to 8.62% on Hits@1. We also show its better performance than the baselines on different ratios of training set, and harder datasets.

There has been increasing interest in using Stackelberg game (known as a security game) to allocate limited security resources against different attacker types with a specific probability distribution. However, real problems of this kind often face ambiguous information, such as imprecise, unreliable and absent payoffs, and ambiguous assignments of these payoffs. To this end, based on decision theory and the Dempster–Shafer theory of evidence, this paper proposes a novel framework that can handle these common types of ambiguity. More specifically, this paper deploys the underlying principles of existing rules from decision theory, as a way to characterise different attitudes to ambiguity, during the transformation of ambiguous payoffs into point-valued payoffs. Hence, our framework holds some good properties: (i) it subsumes traditional security games without ambiguous payoffs, (ii) a uniform margin of error will not affect the results and (iii) the influence of complete ignorance can be minimised. Also, our framework is evaluated by using nine different transformation rules, under various conditions and constraints, against 73,000 randomly generated games (a first comprehensive empirical evaluation to date). The evaluation reveals the benefits of each transformation rule and confirms that different rules can model individuals' different attitudes to ambiguity.

This paper proposes a logical model of multi-demand bargaining with integrity constraints. We also construct a simultaneous concession solution to bargaining games of this kind and show that the solution is uniquely characterized by a set of logical properties. Moreover, we prove that the solution also satisfies the most fundamental game theoretic properties such as symmetry and Pareto optimality. In addition, by lots of simulation experiments we study how the number of conflicting demands, bargainers' risk attitude, and bargainer number influence the bargaining success rate and efficiency as well as the agreement quality.

This paper builds a multi-demand bargaining model based on fuzzy rules, and introduces its agreement concept, which satisfies four intuitive properties of consistency, collective rationality, disagreement and minimum concession. In the model, the fuzzy rules are used to calculate how much bargainers should change their preference during a bargaining. Moreover, the psychological experiment are used to elicit the fuzzy rules. In addition, we analyse how bargainers' risk attitude, patience and regret degree influence agreement of our bargaining game, and identify the existence conditions of bargaining agreement.

Nowadays, decisions in estate investment are made by a group of investors with different demands and then how to find an agreement among them become an essential issue. Thus, this paper introduces a fuzzy logic based bargaining model to solve such problems. Moreover, we also do lots of simulation experiments to reveal how bargainers risk attitude, patience and regret degree influence the outcome of a game, and benchmark our model with the previous one. From these experiments, we can conclude that our model can reflect the human intuitions well, has a higher success rate, and bargains more efficiently than the previous one.