Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism

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• Bidirectional Long Short-Term Memory (BI-LSTM)
• Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
• Attention Mechanism/Transformer Model
• Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification | Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, Bo Xu
• Taming Recurrent Neural Networks for Better Summarization | Abigail See
• Cybersecurity
  • Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection | Pacific Northwest National Laboratory (PNNL)
  • Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams | Pacific Northwest National Laboratory (PNNL)
  • SafeKit | Pacific Northwest National Laboratory (PNNL) - GitHub

By incorporating attention variants into an RNN (language models) opportunities are created for model introspection and analysis without sacrificing performance. Attention-equipped LSTM models have been used to improve performance on complex sequence modeling tasks. Attention provides a dynamic weighted average of values from different points in a calculation during the processing of a sequence to provide long term context for downstream discriminative or generative prediction. Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection | Western Washington University and Pacific Northwest National Laboratory (PNNL