The rise of online social platforms has enhanced connectivity and access to information. Still, it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order. In this paper, we introduce a unified framework for defining and classifying malicious social bots along three dimensions: behavior, interaction, and operation. We then present a comprehensive review of social bot detection methods, tracing their evolution from traditional machine learning techniques to deep learning architectures and graph neural networks, with particular emphasis on recent advances in group-level detection. We also explore the emerging paradigm of Large Language Model (LLM) based bot detection. This paper reviews the current state of research, identifies key challenges, and outlines future directions. It provides a cohesive foundation for building more robust detection frameworks to counter the evolving threats posed by malicious social bots.