LEADER 04332nam 22006015 450 001 9911022159003321 005 20250820130228.0 010 $a3-658-48778-X 024 7 $a10.1007/978-3-658-48778-2 035 $a(CKB)40402044800041 035 $a(MiAaPQ)EBC32268150 035 $a(Au-PeEL)EBL32268150 035 $a(DE-He213)978-3-658-48778-2 035 $a(OCoLC)1534811837 035 $a(EXLCZ)9940402044800041 100 $a20250820d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning in Textual Low-Data Regimes for Cybersecurity /$fby Markus Bayer 205 $a1st ed. 2025. 210 1$aWiesbaden :$cSpringer Fachmedien Wiesbaden :$cImprint: Springer Vieweg,$d2025. 215 $a1 online resource (445 pages) 225 1 $aTechnology, Peace and Security I Technologie, Frieden und Sicherheit,$x3004-9326 311 08$a3-658-48777-1 327 $aIntroduction -- Research Design -- Findings -- Discussion -- Conclusion -- Information Overload in Crisis Management: Bilingual Evaluation of Embedding Models for Clustering Social Media Posts in Emergencies -- ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios -- A Survey on Data Augmentation for Text Classification -- Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers -- Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter -- CySecBERT: A Domain-Adapted Language Model for the Cybersecurity Domain -- Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence -- XAI-Attack: Utilizing Explainable AI to Find Incorrectly Learned Patterns for Black-Box Adversarial Example Creation. 330 $aIn today's fast-paced cybersecurity landscape, professionals are increasingly challenged by the vast volumes of cyber threat data, making it difficult to identify and mitigate threats effectively. Traditional clustering methods help in broadly categorizing threats but fall short when it comes to the fine-grained analysis necessary for precise threat management. Supervised machine learning offers a potential solution, but the rapidly changing nature of cyber threats renders static models ineffective and the creation of new models too labor-intensive. This book addresses these challenges by introducing innovative low-data regime methods that enhance the machine learning process with minimal labeled data. The proposed approach spans four key stages: Data Acquisition: Leveraging active learning with advanced models like GPT-4 to optimize data labeling. Preprocessing: Utilizing GPT-2 and GPT-3 for data augmentation to enrich and diversify datasets. Model Selection: Developing a specialized cybersecurity language model and using multi-level transfer learning. Prediction: Introducing a novel adversarial example generation method, grounded in explainable AI, to improve model accuracy and resilience. About the Author Dr. rer. nat. Markus Bayer is a research associate and post-doctoral researcher at the Chair of Science and Technology for Peace and Security (PEASEC) in the Department of Computer Science at the Technical University of Darmstadt. 410 0$aTechnology, Peace and Security I Technologie, Frieden und Sicherheit,$x3004-9326 606 $aEngineering mathematics 606 $aEngineering$xData processing 606 $aMachine learning 606 $aData protection 606 $aMathematical and Computational Engineering Applications 606 $aMachine Learning 606 $aData and Information Security 615 0$aEngineering mathematics. 615 0$aEngineering$xData processing. 615 0$aMachine learning. 615 0$aData protection. 615 14$aMathematical and Computational Engineering Applications. 615 24$aMachine Learning. 615 24$aData and Information Security. 676 $a620 700 $aBayer$b Markus$01427596 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911022159003321 996 $aDeep Learning in Textual Low-Data Regimes for Cybersecurity$94429453 997 $aUNINA