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The exponential growth of digital content has generated massive textual datasets, necessitating the use of advanced analytical approaches. Large Language Models (LLMs) have emerged as tools that are capable of processing and extracting insights from massive unstructured textual datasets. However, how to leverage LLMs for text analytics Information Systems (IS) research is currently unclear. To assist the IS community in understanding how to operationalize LLMs, we propose a Text Analytics for Information Systems Research (TAISR) framework. Our proposed framework provides detailed recommendations grounded in IS and LLM literature on how to conduct meaningful text analytics IS research for design science, behavioral, and econometric streams. We conducted three business intelligence case studies using our TAISR framework to demonstrate its application in several IS research contexts. We also outline the potential challenges and limitations of adopting LLMs for IS. By offering a systematic approach and evidence of its utility, our TAISR framework contributes to future IS research streams looking to incorporate powerful LLMs for text analytics. 

Although machine learning-based anti-phishing detectors have provided promising results in phishing website detection, they remain vulnerable to evasion attacks. The Machine Learning Security Evasion Competition 2022 (MLSEC 2022) provides researchers and practitioners with the opportunity to deploy evasion attacks against anti-phishing machine learning models in real-world settings. In this field note, we share our experience participating in MLSEC 2022. We manipulated the source code of ten phishing HTML pages provided by the competition using obfuscation techniques to evade anti-phishing models. Our evasion attacks employing a benign overlap strategy achieved third place in the competition with 46 out of a potential 80 points. The results of our MLSEC 2022 performance can provide valuable insights for research seeking to robustify machine learning-based anti-phishing detectors. 

The rapid proliferation of complex information systems has been met by an ever-increasing quantity of exploits that can cause irreparable cyber breaches. To mitigate these cyber threats, academia and industry have placed a significant focus on proactively identifying and labeling exploits developed by the international hacker community. However, prevailing approaches for labeling exploits in hacker forums do not leverage metadata from exploit darknet markets or public exploit repositories to enhance labeling performance. In this study, we adopted the computational design science paradigm to develop a novel information technology artifact, the deep transfer learning exploit labeler (DTL-EL). DTL-EL incorporates a pre-initialization design, multi-layer deep transfer learning (DTL), and a self-attention mechanism to automatically label exploits in hacker forums. We rigorously evaluated the proposed DTL-EL against state-of-the-art non-DTL benchmark methods based in classical machine learning and deep learning. Results suggest that the proposed DTL-EL significantly outperforms benchmark methods based on accuracy, precision, recall, and F1-score. Our proposed DTL-EL framework provides important practical implications for key stakeholders such as cybersecurity managers, analysts, and educators. 

Hacker forums provide malicious actors with a large database of tutorials, goods, and assets to leverage for cyber-attacks. Careful research of these forums can provide tremendous benefit to the cybersecurity community through trend identification and exploit categorization. This study aims to provide a novel static word embedding, Hack2Vec, to improve performance on hacker forum classification tasks. Our proposed Hack2Vec model distills contextual representations from the seminal pre-trained language model BERT to a continuous bag-of-words model to create a highly targeted hacker forum static word embedding. The results of our experimental design indicate that Hack2Vec improves performance over prominent embeddings in accuracy, precision, recall, and F1-score for a benchmark hacker forum classification task.