Authors:
Preeti,Rajender Nath,DOI NO:
https://doi.org/10.26782/jmcms.2025.12.00004Keywords:
DDoS attacks,Gradient Boosting (GB),IoT security,long short-term memory (LSTM),Abstract
The Internet of Things (IoT) links billions of devices, boosts innovation, shares information effortlessly, and is reshaping various industries. The most common Distributed Denial of Service (DDoS) attacks target all layers in the IoT architecture. Even though easy to execute, these sorts of attacks may severely harm targeted systems and networks. This Novel hybrid model uses Bagged Long Short-Term Memory (LSTM) and Gradient Boosting (GB) to address large dimensionality, various feature dimensions, low classification accuracy, and high false positive rates in raw traffic data to improve IoT security against DDoS attacks. To reduce input information redundancy, the Boruta-Pearson Feature Selector (BPFS) gathers key features as model inputs. The Bagged-LSTM design minimises variance to detect anomalies, while Gradient Boosting improves prediction accuracy. The CIC-ISD2017 and CIC DDoS2019 datasets were used to test the hybrid model. Experimental results show that the recommended model outperforms current models with an accuracy of 99%. It is impossible to completely protect your server from these threats, but by using the techniques discussed here, these attacks can be prevented, and the server can focus on fulfilling legitimate requests rather than unauthentic ones.Refference:
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