The automation of penetration testing has long been constrained by the semantic-operational divide between human expert cognition and machine-executable workflows. We present COAPT, a novel architecture that bridges this gap through large language model (LLM) driven cognitive planning, enabling autonomous execution of full-cycle penetration testing aligned with the penetration testing execution standard (PTES) and MITRE adversarial tactics, techniques, and common knowledge (ATT&CK) framework. COAPT introduces three key innovations: (1) A cognitive planning architecture combining retrieval-augmented generation with chain-of-thought reasoning, grounding decisions in verified cybersecurity knowledge; (2) Formal semantic interfaces that translate strategic intent into executable commands for security tools through machine-actionable contracts; (3) Hierarchical multi-agent coordination that maintains tactical consistency across reconnaissance, exploitation, privilege escalation, and defense recommendation phases. Evaluated across diverse penetration testing scenarios, COAPT demonstrates superior performance over state-of-the-art tools in vulnerability exploitation success rates and operational efficiency, while significantly reducing LLM hallucination risks through its knowledge-grounded reasoning approach. The architecture’s ability to autonomously chain multi-stage attacks and generate ATT&CK-mapped defense strategies establishes a new paradigm for cybersecurity automation that preserves human expert workflows at machine execution scales.

In recent years, e-sports has rapidly developed, and the industry has produced large amounts of data with specifications, and these data are easily to be obtained. Due to the above characteristics, data mining and deep learning methods can be used to guide players and develop appropriate strategies to win games. As one of the world’s most famous e-sports events, Dota2 has a large audience base and a good game system. A victory in a game is often associated with a hero’s match, and players are often unable to pick the best lineup to compete. To solve this problem, in this paper, we present an improved bidirectional Long Short-Term Memory (LSTM) neural network model for Dota2 lineup recommendations. The model uses the Continuous Bag Of Words (CBOW) model in the Word2vec model to generate hero vectors. The CBOW model can predict the context of a word in a sentence. Accordingly, a word is transformed into a hero, a sentence into a lineup, and a word vector into a hero vector, the model applied in this article recommends the last hero according to the first four heroes selected first, thereby solving a series of recommendation problems.

Social network services can not only help people form relationships and make new friends and partners, but also assist in processing personal information, sharing knowledge, and managing social relationships. Social networks achieve valuable communication and collaboration, bring additional business opportunities, and have great social value. Research on social network problems is effective by using assumption, definition, analysis, modeling, and optimization strategies. In this paper, we survey the existing problems of game theory applied to social networks and classify their application scenarios into four categories: information diffusion, behavior analysis, community detection, and information security. Readers can clearly master knowledge application in every category. Finally, we discuss certain limitations of game theory on the basis of research in recent years and propose future directions of social network research.