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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Ab Ghani, Hadhrami
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Computer Science & IT Engineering

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Enhancing attack detection in IoT through integration of weighted emphasis formula with XGBoost Al Amien, Januar; Ab Ghani, Hadhrami; Md Saleh, Nurul Izrin; Soni, Soni
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp641-648

Abstract

This research addresses the challenge of detecting attacks in the internet of things (IoT) environment, where minority classes often go unnoticed due to the dominance of majority classes. The primary objective is to introduce and integrate the imbalance ratio formula (IRF) into the XGBoost algorithm, aiming to provide greater emphasis on minority classes and ensure the model's focus on attack detection, particularly in binary and multiclass scenarios. Experimental validation using the IoTID20 dataset demonstrates the significant enhancement in attack detection accuracy achieved by integrating IRF into XGBoost. This enhancement contributes to the consistent improvement in distinguishing attacks from normal traffic, thereby resulting in a more reliable attack detection system in complex IoT environments. Moreover, the implementation of IRF enhances the robustness of the XGBoost model, enabling effective handling of imbalanced datasets commonly encountered in IoT security applications. This approach advances intrusion detection systems by addressing the challenge of class imbalance, leading to more accurate and efficient detection of malicious activities in IoT networks. The practical implications of these findings include the enhancement of cybersecurity measures in IoT deployments, potentially mitigating the risks associated with cyber threats in interconnected smart environments.
Leveraging artificial intelligence through long short-term memory approach for correcting faults in Chinese language sentences Che Lah, Muhammad Afiq; Ab Ghani, Hadhrami; Md Saleh, Nurul Izrin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1799-1808

Abstract

This research focus on leveraging artificial intelligence (AI) to manage the challenges faced by non-native speakers in correcting faults and misconstructions in Chinese language sentences. Learners commonly struggle with mispronunciation, incorrect character usage, improper sentence structures, and grammatical mistakes. To tackle these issues, this study generally aims to improve and optimize AI for correcting faults in Chinese language for non-native speakers. This project employs long short-term memory (LSTM) approach based on Hanyu Shuiping Kaoshi (HSK) word ordering errors (WOE) dataset. The effectiveness of leveraging LSTM in detecting and correcting errors in Chinese language sentence have been demonstrated. LSTM shows the capability to be learn Chinese sentence structure, identify mistakes, and correct them. In summary, this research seeks to benefits the power of AI to provide innovative solutions for detecting, correcting faults and misconstructions in Chinese language sentences. This paper essentially useful for those who wish to learn how to correct their Chinese writing and enhance language proficiency among non-native speakers.
Co-Authors Al Amien, Januar Che Lah, Muhammad Afiq Md Saleh, Nurul Izrin Soni