Reinforcement Learning for Adaptive Cybersecurity: AI-Driven Threat Detection and Response Mechanisms

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@article{1704836,
      author = {Md Mostafijur Rahman, Mohammad Shahadat Hossain, Md Mashfiquer Rahman, Md Shafiq Ullah, Sharmin Nahar; Md Mostafizur Rahman},
      title = {Reinforcement Learning for Adaptive Cybersecurity: AI-Driven Threat Detection and Response Mechanisms},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {1},
      pages = {721-732},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704836.pdf},
      abstract = {The focus of this paper explores how reinforcement learning (RL) helps adaptive cybersecurity systems function better through enhanced procedures for threat detection and reaction mechanisms. Consistent threat pattern analysis follows an evaluation of RL systems to build self-operating security mechanisms that enhance resilience. The research examines the application of RL algorithms on simulated and real-world data to forecast, detect, and counteract security issues within dynamic operational domains. Research conclusions demonstrate that Real-time Learning demonstrates effective outcomes for instant decisions while enhancing both threat recognition precision and lowering erroneous alerts beyond traditional systems. The article presents a study for AI-based approaches while explaining how RL systems detect present threats and protect against potentially new sophisticated adversaries.},
      keywords = {Reinforcement Learning, Cybersecurity Systems, Threat Detection, Adaptive Defense, Machine Learning, Intrusion Detection},
      month = {July},
  }