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AI-Augmented Intrusion Detection: Advancements in Real-Time Cyber Threat Recognition
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence in Cybersecurity
Abstract
The rapid evolution of cyber threats demands innovative approaches to safeguarding digital infrastructures. AI-augmented intrusion detection systems (IDS) represent a paradigm shift in real-time cyber threat recognition, integrating advanced machine learning algorithms, deep learning architectures, and intelligent data analytics to detect, classify, and mitigate threats with unprecedented speed and accuracy. This study examines recent advancements in AI-driven IDS, focusing on their capacity to process vast, heterogeneous network data streams in real time, identify complex attack patterns, and adapt to emerging threats through continuous learning mechanisms. The integration of anomaly detection, behavioral analysis, and threat intelligence feeds enables these systems to recognize subtle deviations from normal activity, even in encrypted traffic, reducing false positives and enhancing situational awareness. Additionally, the research highlights the role of reinforcement learning in optimizing detection policies and response strategies, ensuring adaptive defense against polymorphic and zero-day attacks. Implementation challenges such as data quality, computational overhead, algorithm interpretability, and adversarial evasion are critically assessed, alongside potential solutions including federated learning, explainable AI, and hybrid signature?anomaly detection models. The study further explores real-world deployments in enterprise, cloud, and IoT environments, illustrating performance metrics such as detection rate, precision, recall, and mean time to detect (MTTD). These case analyses underscore the transformative impact of AI in accelerating intrusion detection response times, minimizing operational disruption, and strengthening cyber resilience. The paper concludes by identifying research gaps and recommending future directions, including energy-efficient AI models, integration with security orchestration and automated response (SOAR) platforms, and the development of standardized benchmarks for AI-based IDS evaluation. By bridging the gap between traditional security paradigms and intelligent automation, AI-augmented intrusion detection systems offer a robust pathway toward proactive, adaptive, and scalable cyber defense in an era of increasingly sophisticated threats.
Keywords
AI-Augmented Intrusion Detection, Real-Time Cyber Threat Recognition, Machine Learning, Deep Learning, Anomaly Detection, Behavioral Analysis, Zero-Day Attacks, Explainable AI, Cybersecurity Resilience, Adaptive Defense Systems
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How to cite this paper
@article{1710369,
author = {Edima David Etim, Iboro Akpan Essien, Joshua Oluwagbenga Ajayi, Eseoghene Daniel Erigha, Ehimah Obuse},
title = {AI-Augmented Intrusion Detection: Advancements in Real-Time Cyber Threat Recognition},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {3},
pages = {225-247},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710369.pdf},
abstract = {The rapid evolution of cyber threats demands innovative approaches to safeguarding digital infrastructures. AI-augmented intrusion detection systems (IDS) represent a paradigm shift in real-time cyber threat recognition, integrating advanced machine learning algorithms, deep learning architectures, and intelligent data analytics to detect, classify, and mitigate threats with unprecedented speed and accuracy. This study examines recent advancements in AI-driven IDS, focusing on their capacity to process vast, heterogeneous network data streams in real time, identify complex attack patterns, and adapt to emerging threats through continuous learning mechanisms. The integration of anomaly detection, behavioral analysis, and threat intelligence feeds enables these systems to recognize subtle deviations from normal activity, even in encrypted traffic, reducing false positives and enhancing situational awareness. Additionally, the research highlights the role of reinforcement learning in optimizing detection policies and response strategies, ensuring adaptive defense against polymorphic and zero-day attacks. Implementation challenges such as data quality, computational overhead, algorithm interpretability, and adversarial evasion are critically assessed, alongside potential solutions including federated learning, explainable AI, and hybrid signature?anomaly detection models. The study further explores real-world deployments in enterprise, cloud, and IoT environments, illustrating performance metrics such as detection rate, precision, recall, and mean time to detect (MTTD). These case analyses underscore the transformative impact of AI in accelerating intrusion detection response times, minimizing operational disruption, and strengthening cyber resilience. The paper concludes by identifying research gaps and recommending future directions, including energy-efficient AI models, integration with security orchestration and automated response (SOAR) platforms, and the development of standardized benchmarks for AI-based IDS evaluation. By bridging the gap between traditional security paradigms and intelligent automation, AI-augmented intrusion detection systems offer a robust pathway toward proactive, adaptive, and scalable cyber defense in an era of increasingly sophisticated threats.},
keywords = {AI-Augmented Intrusion Detection, Real-Time Cyber Threat Recognition, Machine Learning, Deep Learning, Anomaly Detection, Behavioral Analysis, Zero-Day Attacks, Explainable AI, Cybersecurity Resilience, Adaptive Defense Systems},
month = {September},
}