Anantavrasilp, Isara

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Anantavrasilp, Isara
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isara.an@kmitl.ac.th

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    Analysis of optimal sensor positions for activity classification and application on a different data collection scenario
    (2017-04-05)
    Pannurat, Natthapon
    ;
    Thiemjarus, Surapa
    ;
    Nantajeewarawat, Ekawit
    ;
    Anantavrasilp, Isara
    This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted, and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and a dataset collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values greater than 96%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.
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    Robust image encryption method with cipher stream chaining process
    (2019-02-01)
    Tep, Sovan
    ;
    Anantavrasilp, Isara
    A new image encryption algorithm that uses one dimensional logistic map combined with perceptron model is proposed. The algorithm uses logistic map to produce pseudo random sequences, which are used sequences of keys to specify the weights of the perceptron. The, perceptron is used to encrypt the pixels of the image. The approach is also equipped with the novel Cipher Stream Chaining Process (CSCP), making it highly sensitive to given image. Our work is evaluated against histogram analysis, information entropy, key sensitivity analysis. Experiment results show that, the cipher image does not give out any information on the plain image and the algorithm is highly sensitive to plain image and key.
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    The effect of sizes of the feature sets on intrusion detection performances
    (2017-12-28)
    Kuy, Yoekleng
    ;
    Anantavrasilp, Isara
    Adaptive Intrusion Detection System (IDS) is a class of IDS that uses observed flows behaviors to detect malicious activities - usually with the aids of machine learning techniques. Most researches in this field focus on which features to be used or which classification methods to be employed. However, none have studied the impact of number of opted features on the accuracies of the anomaly detection or the smallest set of features that should be employed. This paper attempts to address these issues. We have applied feature selection algorithm, ReliefF [1] on NSL-KDD dataset [2] to select 10 most discriminative features out of 41 features. Then several machine learning algorithms are employed to classify normal and anomaly flows (both binary and multiple classes) using different set of features, each with different sizes. Experiment results show that >95% accuracies can be achieved with only 4-5 features and accuracy does not improve significantly after 6-7 features. We have also compared our results with other works and show that our work yields better results using the lower or the same number of features.