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.

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.