aspg-18-3101-1792574024 20261021091344 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 14 1 B. Sowmya Department of Computer Science and Technology, Sri Krishnadevaraya University, Anantapuramu, India Nagendra Muthuluru Department of Computer Science and Technology, Sri Krishnadevaraya University, Anantapuramu, India Internet of Things (IoT) devices are more attractive towards various vulnerable activities and nodes are easily compromised by attackers. The complexity of insecure IoT node installation relies on device heterogeneity and resource constraints because of the network ends and conventional endpoints. This work concentrates on modeling an efficient IoT-based preservation model ( lang=EN-IN style='font-size:10.0pt;line-height:115%;font-family:"Cambria Math",serif; mso-fareast-font-family:Calibri;mso-fareast-theme-font:minor-latin;mso-bidi-font-family: "Times New Roman";mso-ansi-language:EN-IN;mso-fareast-language:EN-US; mso-bidi-language:AR-SA'>iPRES id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> height:13.5pt'> o:title="" chromakey="white"/> ) which is a lightweight approach used for detecting anomaly and performance various analyses at the endpoints. This work integrates linear Support Vector Machine for pattern analysis and adaptive fuzzy rule model for data pattern rule generation to examine malicious network functionality and network traffic. While adopting the rules, the compromised node needs to fulfill the generated rules; when it fails then it is considered as malicious activity. Then, the lang=EN-IN style='font-size:10.0pt;line-height:115%;font-family:"Cambria Math",serif; mso-fareast-font-family:Calibri;mso-fareast-theme-font:minor-latin;mso-bidi-font-family: "Times New Roman";mso-ansi-language:EN-IN;mso-fareast-language:EN-US; mso-bidi-language:AR-SA'>iPRES id="_x0000_i1025" type="#_x0000_t75" style='width:25.5pt;height:13.5pt'> o:title="" chromakey="white"/> models impose network access restrictions on the compromised and terminate the further process. Thus, the nodes are prevented from further network attacks. The evaluation model is done with the use of an online available network dataset and the dataset samples are evaluated in the complex network scenario. The simulation is done in MATLAB 2020a simulation environment and the accuracy attained with this model is higher compared to other approaches. Similarly, other metrics like False Alarm Rate (FAR) are evaluated for predicting malicious network functionality. The significance of the model is evaluated based on the prediction and mitigation of various network attacks. The anticipated model shows a prediction rate of 90.21% for DoS attacks, 89.13% for R2L, 91.65% for probe, and 93.56% for U2R attacks. 2025 102 113 3101 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.140108 https://www.americaspg.com/journal/18/article/3101 https://www.americaspg.com/storage/81723153601.pdf