Benjamin M. Ampel
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  • Publications
    • Examining the Robustness of Machine Learning-based Phishing Website Detection: Action-Masked Reinforcement Learning for Automated Red Teaming
    • Large Language Models for Conducting Advanced Text Analytics Information Systems Research
    • Creating Proactive Cyber Threat Intelligence with Hacker Exploit Labels: A Deep Transfer Learning Approach
    • Evading Anti-Phishing Models: A Field Note Documenting an Experience in the Machine Learning Security Evasion Competition 2022
    • Improving Threat Mitigation Through a Cybersecurity Risk Management Framework: A Computational Design Science Approach
    • The 4th Workshop on Artificial Intelligence-enabled Cybersecurity Analytics
    • Benchmarking the Robustness of Phishing Email Detection Systems
    • Disrupting Ransomware Actors on the Bitcoin Blockchain: A Graph Embedding Approach
    • Mapping Exploit Code on Paste Sites to the MITRE ATT&CK Framework: A Multi-label Transformer Approach
    • The Effect of Consensus Algorithm on Ethereum Price and Volume
    • Why Following Friends Can Hurt You: A Replication Study
    • Distilling Contextual Embeddings into a Static Word Embedding for Improving Hacker Forum Analytics
    • Exploring the Evolution of Exploit-sharing Hackers: An Unsupervised Graph Embedding Approach
    • Identifying and Categorizing Malicious Content on Paste Sites: A Neural Topic Modeling Approach
    • Linking Common Vulnerabilities and Exposures to the MITRE ATT&CK Framework: A Self-Distillation Approach
    • The Role of AI Agents for De-Escalating Commitment in Digital Innovation Projects
    • Identifying Vulnerable GitHub Repositories and Users in Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach
    • Labeling Hacker Exploits for Proactive Cyber Threat Intelligence: A Deep Transfer Learning Approach
    • Smart Vulnerability Assessment for Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach
    • Performance Modeling of Hyperledger Sawtooth Blockchain
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scikit-learn

Oct 26, 2023 ยท 1 min read
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scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Last updated on Jun 26, 2025
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Benjamin M. Ampel
Authors
Benjamin M. Ampel
Assistant Professor of Computer Information Systems

← PyTorch Oct 26, 2023

ยฉ 2025 Me. This work is licensed under CC BY NC ND 4.0

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