Nature and natural organisms have always inspired researchers and scientists for solving real world issues. And Computer security is no exception. Artificial Immune System inspired from natural Immune System works efficiently for detecting intrusion in a network. Two layers of defenses: innate system and adaptive system are implemented in this proposed methodology where the innate system mimics the natural Innate Immune System to form the first line of defense. The adaptive system imitates the Adaptive Immune System by incorporating the T-cell and B-cell defensive mechanisms. The results exhibit that the proposed methodology works efficiently for detecting intrusion after inducing malicious attacks on the network system.

We present a generalizable classification approach that leverages Large Language Models (LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We design a multi-faceted prompt to extract a textual explanation of the reasoning that connects visible cues to underlying social meanings. These extracted explanations or rationales serve as augmentations to the conversational text to facilitate dialogue understanding and transfer. Our empirical results over 2,340 experimental settings demonstrate the significant positive impact of adding these rationales. Our findings hold true for in-domain classification, zero-shot, and few-shot domain transfer for two different social meaning detection tasks, each spanning two different corpora.