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Comprehensive Survey and Implementation Framework for Dynamic Attack Detection in IOT using Explainable Artificial Intelligence (XAI)
Authors
Ankita G. Bhagat,Dr. Sushil V. Kulkarni
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Keywords
Internet of Things (IoT), Intrusion Detection System (IDS), Cybersquatting, Explainable Artificial Intelligence (XAI), Machine Learning, Deep Learning, SHAP, LIME, Dynamic Attack Detection
Abstract
The Internet of Things (IoT) has become a fundamental component of modern digital infrastructures, enabling interconnected devices to exchange information and support intelligent services across diverse sectors, including healthcare, smart cities, industrial automation, and transportation. While the widespread adoption of IoT technologies has created significant opportunities for innovation and efficiency, it has also introduced serious security concerns. The heterogeneous nature of IoT devices, limited computational resources, and continuous connectivity make these environments attractive targets for counterattacks such as Distributed Denial-of-Service (DDoS), botnet activities, spoofing, malware infections, and data tampering.
To address these threats, researchers have increasingly employed Machine Learning (ML) and Deep Learning (DL) techniques within Intrusion Detection Systems (IDS). Although these approaches have demonstrated promising detection capabilities, many of the developed models operate as complex black-box systems, making it difficult for security analysts to understand the reasoning behind their predictions. The lack of transparency can reduce trust in automated security mechanisms and limit their adoption in critical applications where accountability and interchangeability are essential.
Explainable Artificial Intelligence (XAI) has emerged as a valuable approach for enhancing the transparency and reliability of AI-based cybersquatting solutions. By providing understandable explanations for model decisions, XAI techniques enable security professionals to identify the factors influencing attack detection outcomes and validate system behavior. This review paper examines recent advancements in dynamic attack detection for IoT environments and explores the integration of explainable methods such as Shapely Additive explanation s (SHAP) and Local Interpreter Model-Agnostic Explanations (LIME) into intrusion detection frameworks. The paper further reviews widely used datasets, machine learning and deep learning models, performance evaluation metrics, and key research challenges. Finally, it highlights emerging trends and future research opportunities aimed at developing adaptive, accurate, and trustworthy IoT security systems capable of addressing evolving caber threats
IJCRT's Publication Details
Unique Identification Number - IJCRT2606505
Paper ID - 310598
Author type - Indian Author
Page Number(s) - e680-e684
Pubished in - Volume 14 | Issue 6 | June 2026
DOI (Digital Object Identifier) -
No Of Downloads - 63
Author Country - India, 431517, AMBAJOGAI, AMBAJOGAI, 431517, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Ankita G. Bhagat,Dr. Sushil V. Kulkarni, "Comprehensive Survey and Implementation Framework for Dynamic Attack Detection in IOT using Explainable Artificial Intelligence (XAI)", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 6, pp.e680-e684, June 2026, Available at :http://www.ijcrt.org/papers/IJCRT2606505.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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