Authors : Venkateswaran Radhakrishnan; S. Baghavathi Priya; Rajalakshmi. G. R.; Mohammed Ghouse Haneef Maqsood; Rogelio Gutierrez; Chithik Raja Mohamed; Mohamed Ashik

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/mrxkxuh6

Scribd : https://tinyurl.com/yfujfsch

DOI : https://doi.org/10.38124/ijisrt/25nov1032

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Abstract : The swift spread of Internet of Things (IoT), alike the wildfire has forced us all into cybersecurity challenges and therefore requires proper security tools for detecting and responding to real-time threats. Instead, this study moves in a different sequence way-the sequence-based pattern analysis is a novel way to both identify and mitigate the possible cyber threats that are associated with IoT networks. In the first stage of the study, a hybrid approach implementing the sequential pattern mining and machine learning algorithms is followed the extraction of patterns related to the anomalies. At first, data preprocessing is processed via the feature selection scheme to impetus the quality of generated patterns. Thereafter, the frequent sequence mining technique is a move that is it applied; thus, to determine the repetition of attack patterns in the IoT traffic data. Besides, a supervisor learning-based anomaly detection model is installed to classify threats on the basis of temporal sequence anomalies. Further, the reinforcement based learning procured is the one that is adaptively applied such that changes in the cyber threats get the system to learn and ultimately improve its detection accuracy. The plan has been tested on a few large datasets of the IoT network that are the most common, to show its effectiveness by comparison with methods in operation. The study findings in the line of sequence-based pattern analysis; plus, the machine learning are single skills for this set of processes to perform IOT cyberattack detection that enables IoT secure environments

Keywords : Cyber Threat Identification, Iot Security, Sequence -Depending Pattern Analysis, Anomaly Determination, Supervisor Learning, Reinforcement Based Learning, Network Infrastructure Security, Frequently Sequence Mining, Real-Time Threat Avoiding.

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