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All Journal Bulletin of Electrical Engineering and Informatics Journal of Robotics and Control (JRC)
Almseidin, Mohammad
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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EI-FRI: Extended Incircle Fuzzy Rule Interpolation for Multidimensional Antecedents, Multiple Fuzzy Rules, and Extrapolation Using Total Weight Measurement and Shift Ratio Alzubi, Maen; Almseidin, Mohammad; Kovacs, Szilveszter; Al-Sawwa, Jamil; Alkasassbeh, Mouhammd
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.20515

Abstract

Traditional fuzzy reasoning techniques demand a condensed fuzzy rule base to conclude a result. Still, due to incomplete data or a deficiency of expertise and knowledge, dense rule bases are not always available. Fuzzy interpolation methods have been widely explored to reasonably allow the interpolation of a fuzzy result using the closest current rules. Fuzzy rule interpolation is a type of fuzzy inference system in which conclusions can be obtained even with a few fuzzy rules. This benefit could be used to adapt the FRI to different application areas that suffer from a lack of knowledge. Alzubi et al. [17] offered a novel interpolative method that uses a weighted average based on the center point of the Incircle of the fuzzy sets. Nevertheless, the interpolated observation does not completely define the actual observation that is provided. In our offered extension to this method, a modification weight measure calculation and a shift technique are included to guarantee that the center point of the observation and the interpolated observation are mapped together. This weight measure calculation and shift technique enabled the capability of extrapolation to be conducted implicitly, which is also improves the performance results of the algorithm in the presence of multiple fuzzy rules and multidimensional priors.
A stacked ensemble approach to identify internet of things network attacks through traffic analysis Rawashdeh, Adnan; Alkasassbeh, Mouhammd; Alauthman, Mohammad; Almseidin, Mohammad
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7811

Abstract

The internet of things (IoT) has increased exponentially in connected devices worldwide in recent years. However, this rapid growth also introduces significant security challenges since many IoT devices have vulnerabilities that can be exploited for cyber-attacks. Anomaly detection using machine learning algorithms shows promise for identifying abnormal network traffic indicative of IoT attacks. This paper proposes an ensemble learning framework for anomaly detection in IoT networks. A systematic literature review analyzes recent research applying machine learning for IoT security. Subsequently, a novel stacked ensemble model is presented, combining multiple base classifiers (random forest, neural network, support vector machine (SVM)) and meta-classifiers (gradient boosting) for improved performance. The model is evaluated on the IoTID20 dataset, using network traffic features to detect anomalies across binary, multi-class, and multi-label classifications. Experimental results demonstrate that the ensemble model achieved 99.7% accuracy and F1 score for binary classification, 99.5% accuracy for multi-class, and 91.2% accuracy for multi-label classification, outperforming previous methods. The model provides an effective anomaly detection approach to identify malicious activities and mitigate IoT security threats.
Co-Authors Al-Sawwa, Jamil Alauthman, Mohammad AlKasassbeh, Mouhammd Alzubi, Maen Kovacs, Szilveszter Rawashdeh, Adnan