Cybersecurity Threat Hunting: Modern Strategies to Detect and Neutralize Digital Attacks

Abstract

This study presents a comprehensive review of recent advancements in cybersecurity threat hunting, based on peer-reviewed literature published between 2020 and 2025. It identifies critical limitations in existing research, particularly the lack of unified frameworks that integrate IoT-centric threat detection, behavioral analytics, and proactive defense strategies. The key contributions of this work include: (1) the formulation of a structured classification of contemporary cyber-attack types along with their corresponding detection approaches; (2) a comparative analysis demonstrating that AI- and ML-based anomaly detection techniques outperform traditional intrusion detection systems by approximately 20-30% in detecting previously unseen threats; (3) the design of a multi-layered security framework that integrates IDS, SIEM, behavioral analysis, EDR, and Zero Trust principles; and (4) the provision of IoT-focused security recommendations, including secure device authentication, regular firmware updates, and network segmentation practices. In the context of India’s accelerating digital transformation, particularly initiatives such as Digital India, the importance of robust cybersecurity measures continues to grow. Future work will focus on validating the proposed framework through practical implementation using real-world network datasets.

Introduction

Cybersecurity has become increasingly critical due to the rapid expansion of digital systems, IoT devices, and cloud computing, all of which have significantly increased the number of attack surfaces. The COVID-19 pandemic further accelerated cyber threats, with incidents rising sharply as organizations shifted to online platforms. Traditional perimeter-based security methods are no longer sufficient to handle modern, complex, and evolving attacks such as ransomware, DDoS, phishing, and data breaches.

The study identifies a key research gap: existing cybersecurity solutions (such as IDS, SIEM, and machine learning models) often work in isolation and lack a unified, integrated framework that combines behavioral analysis, IoT security, and proactive defense. It emphasizes the growing need for advanced approaches that integrate AI, Zero Trust architecture, behavioral analytics, and emerging technologies like 5G and edge computing.

The research aims to systematically analyze cyber threats, evaluate existing defense systems, explore IoT vulnerabilities, and assess the role of AI/ML in improving threat detection. It also investigates future directions such as automated response systems and scalable security frameworks.

Methodologically, the study uses a Systematic Literature Review (SLR), analyzing research from 2020–2025 using tools like Snort, Splunk, and machine learning models (e.g., SVM, Random Forest, LSTM) on benchmark datasets such as CICIDS 2017 and UNSW-NB15. Evaluation is based on metrics like accuracy, false positive rate, and detection time.

Conclusion

This study presents a comprehensive review of recent advancements in cybersecurity threat hunting, based on peer-reviewed literature published between 2020 and 2025. It identifies critical limitations in existing research, particularly the lack of unified frameworks that integrate IoT-centric threat detection, behavioral analytics, and proactive defense strategies. The key contributions of this work include: (1) the formulation of a structured classification of contemporary cyber-attack types along with their corresponding detection approaches; (2) a comparative analysis demonstrating that AI- and ML-based anomaly detection techniques outperform traditional intrusion detection systems by approximately 20-30% in detecting previously unseen threats; (3) the design of a multi-layered security framework that integrates IDS, SIEM, behavioral analysis, EDR, and Zero Trust principles; and (4) the provision of IoT-focused security recommendations, including secure device authentication, regular firmware updates, and network segmentation practices. In the context of India’s accelerating digital transformation, particularly initiatives such as Digital India, the importance of robust cybersecurity measures continues to grow. Future work will focus on validating the proposed framework through practical implementation using real-world network datasets.

References

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Copyright

Copyright © 2026 Vaishnavi A. Patil, Mrs. Nirmala Shinge. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.