The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Sofie Pollin - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised wireless spectrum Anomaly Detection with interpretable features
    IEEE Transactions on Cognitive Communications and Networking, 2019
    Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie Pollin
    Abstract:

    Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, spectrum Anomaly detector with interpretable features (SAIFE), an adversarial autoencoder (AAE)-based Anomaly detector for wireless spectrum Anomaly Detection using power spectral density (PSD) data. This model achieves an average Anomaly Detection accuracy above 80% at a constant false alarm rate of 1% along with Anomaly localization in an unsupervised setting. In addition, we investigate the model’s capabilities to learn interpretable features, such as signal bandwidth, class, and center frequency in a semi-supervised fashion. Along with Anomaly Detection the model exhibits promising results for lossy PSD data compression up to $120 {\times }$ and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally, the model is tested on data from one of the distributed electrosense sensors over a long term of 500 h showing its Anomaly Detection capabilities.

  • saife unsupervised wireless spectrum Anomaly Detection with interpretable features
    arXiv: Signal Processing, 2018
    Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie Pollin
    Abstract:

    Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based Anomaly detector for wireless spectrum Anomaly Detection using Power Spectral Density (PSD) data which achieves good Anomaly Detection and localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features such as signal bandwidth, class and center frequency in a semi-supervised fashion. Along with Anomaly Detection the model exhibits promising results for lossy PSD data compression up to 120X and semisupervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its Anomaly Detection capabilities.

Alfred O Hero - One of the best experts on this subject based on the ideXlab platform.

  • multicriteria similarity based Anomaly Detection using pareto depth analysis
    IEEE Transactions on Neural Networks, 2016
    Co-Authors: Kojen Hsiao, Jeff Calder, Alfred O Hero
    Abstract:

    We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as Anomaly Detection. Similarity-based Anomaly Detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many Anomaly Detection applications, the Anomaly Detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based Anomaly Detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) Anomaly Detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets.

  • multi criteria Anomaly Detection using pareto depth analysis
    Neural Information Processing Systems, 2012
    Co-Authors: Kojen Hsiao, Jeff Calder, Alfred O Hero
    Abstract:

    We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as Anomaly Detection. In most Anomaly Detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such a case, multiple criteria can be defined, and one can test for anomalies by scalarizing the multiple criteria using a linear combination of them. If the importance of the different criteria are not known in advance, the algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we introduce a novel non-parametric multi-criteria Anomaly Detection method using Pareto depth analysis (PDA). PDA uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach scales linearly in the number of criteria and is provably better than linear combinations of the criteria.

Sreeraj Rajendran - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised wireless spectrum Anomaly Detection with interpretable features
    IEEE Transactions on Cognitive Communications and Networking, 2019
    Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie Pollin
    Abstract:

    Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, spectrum Anomaly detector with interpretable features (SAIFE), an adversarial autoencoder (AAE)-based Anomaly detector for wireless spectrum Anomaly Detection using power spectral density (PSD) data. This model achieves an average Anomaly Detection accuracy above 80% at a constant false alarm rate of 1% along with Anomaly localization in an unsupervised setting. In addition, we investigate the model’s capabilities to learn interpretable features, such as signal bandwidth, class, and center frequency in a semi-supervised fashion. Along with Anomaly Detection the model exhibits promising results for lossy PSD data compression up to $120 {\times }$ and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally, the model is tested on data from one of the distributed electrosense sensors over a long term of 500 h showing its Anomaly Detection capabilities.

  • saife unsupervised wireless spectrum Anomaly Detection with interpretable features
    arXiv: Signal Processing, 2018
    Co-Authors: Sreeraj Rajendran, Wannes Meert, Vincent Lenders, Sofie Pollin
    Abstract:

    Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based Anomaly detector for wireless spectrum Anomaly Detection using Power Spectral Density (PSD) data which achieves good Anomaly Detection and localization in an unsupervised setting. In addition, we investigate the model's capabilities to learn interpretable features such as signal bandwidth, class and center frequency in a semi-supervised fashion. Along with Anomaly Detection the model exhibits promising results for lossy PSD data compression up to 120X and semisupervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its Anomaly Detection capabilities.

Kojen Hsiao - One of the best experts on this subject based on the ideXlab platform.

  • multicriteria similarity based Anomaly Detection using pareto depth analysis
    IEEE Transactions on Neural Networks, 2016
    Co-Authors: Kojen Hsiao, Jeff Calder, Alfred O Hero
    Abstract:

    We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as Anomaly Detection. Similarity-based Anomaly Detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many Anomaly Detection applications, the Anomaly Detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based Anomaly Detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) Anomaly Detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets.

  • multi criteria Anomaly Detection using pareto depth analysis
    Neural Information Processing Systems, 2012
    Co-Authors: Kojen Hsiao, Jeff Calder, Alfred O Hero
    Abstract:

    We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as Anomaly Detection. In most Anomaly Detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such a case, multiple criteria can be defined, and one can test for anomalies by scalarizing the multiple criteria using a linear combination of them. If the importance of the different criteria are not known in advance, the algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we introduce a novel non-parametric multi-criteria Anomaly Detection method using Pareto depth analysis (PDA). PDA uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach scales linearly in the number of criteria and is provably better than linear combinations of the criteria.

Donna K. Slonim - One of the best experts on this subject based on the ideXlab platform.

  • frac a feature modeling approach for semi supervised and unsupervised Anomaly Detection
    Data Mining and Knowledge Discovery, 2012
    Co-Authors: Keith Noto, Carla E. Brodley, Donna K. Slonim
    Abstract:

    Anomaly Detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called "normal" instances). Given a training set of only normal data, the semi-supervised Anomaly Detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion Detection. The unsupervised Anomaly Detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion Detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised Anomaly Detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised Anomaly Detection task, compared to well-known state-of-the-art Anomaly Detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach.