Browsing by Author "Taha, Kamal"

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    Clustering the dominant defective patterns in semiconductor wafer maps
    (IEEE, 2017-10-30) Taha, Kamal; Salah, K; Yoo, P D
    Identifying defect patterns on wafers is crucial for understanding the root causes and for attributing such patterns to specific steps in the fabrication process. We propose in this paper a system called DDPfinder that clusters the patterns of defective chips on wafers based on their spatial dependence across wafer maps. Such clustering enables the identification of the dominant defect patterns. DDPfinder clusters chip defects based on how dominant are their spatial patterns across all wafer maps. A chip defect is considered dominant, if: (1) it has a systematic defect pattern arising from a specific assignable cause, and (2) it displays spatial dependence across a larger number of wafer maps when compared with other defects. The spatial dependence of a chip defect is determined based on the contiguity ratio of the defect pattern across wafer maps. DDPfinder uses the dominant chip defects to serve as seeds for clustering the patterns of defective chips. This clustering procedure allows process engineers to prioritize their investigation of chip defects based on the dominance status of their clusters. It allows them to pay more attention to the ongoing manufacturing processes that caused the dominant defects. We evaluated the quality and performance of DDPfinder by comparing it experimentally with eight existing clustering models. Results showed marked improvement.
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    IMPACT: Impersonation attack detection via edge computing using deep auto encoder and feature abstraction
    (IEEE, 2020-04-02) Lee, Seo Jin; Yoo, Paul D.; Asyhari, A. Taufiq; Jhi, Yoonchan; Chermak, Lounis; Yeun, Chan Yeob; Taha, Kamal
    An ever-increasing number of computing devices interconnected through wireless networks encapsulated in the cyber-physical-social systems and a significant amount of sensitive network data transmitted among them have raised security and privacy concerns. Intrusion detection system (IDS) is known as an effective defence mechanism and most recently machine learning (ML) methods are used for its development. However, Internet of Things (IoT) devices often have limited computational resources such as limited energy source, computational power and memory, thus, traditional ML-based IDS that require extensive computational resources are not suitable for running on such devices. This study thus is to design and develop a lightweight ML-based IDS tailored for the resource-constrained devices. Specifically, the study proposes a lightweight ML-based IDS model namely IMPACT (IMPersonation Attack deteCTion using deep auto-encoder and feature abstraction). This is based on deep feature learning with gradient-based linear Support Vector Machine (SVM) to deploy and run on resource-constrained devices by reducing the number of features through feature extraction and selection using a stacked autoencoder (SAE), mutual information (MI) and C4.8 wrapper. The IMPACT is trained on Aegean Wi-Fi Intrusion Dataset (AWID) to detect impersonation attack. Numerical results show that the proposed IMPACT achieved 98.22% accuracy with 97.64% detection rate and 1.20% false alarm rate and outperformed existing state-of-the-art benchmark models. Another key contribution of this study is the investigation of the features in AWID dataset for its usability for further development of IDS.