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Attention-based encoder-decoder recurrent neural networks for HTTP Payload Anomaly Detection 19

Attention-based Encoder-Decoder Recurrent Neural Networks fo...

引用

19th IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA)

作者： Wu, Shang Wang, Yijie Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9781665435741

Attack payloads are often short segments hidden in HTTP requests;thus they can be found by HTTP payload anomaly detection. Deep learning models learn data features during training without manual feature extraction, and better performance has received more attention. recurrent neural Network models process sequences directly, which are widely used in payload anomaly detection. However, due to the gradient vanishing problem, RNN has limits on processing the long sequences. Meanwhile, RNN uses its final hidden state for detection and pays more attention to the content of the end of the payload. Besides, deep learning generally lacks interpretability. The paper proposes an unsupervised deep learning model for HTTP payload Anomaly Detection, namely Attention-based encoder-decoder recurrent neural networks Anomaly Detection model (AEDRAD). AEDRAD utilizes the encoder-decoder RNN and attention mechanism to detect anomalies by reconstructing the original sequences. AEDRAD filters the fields of HTTP protocol that cannot exist anomalies, focusing on the suspicious segments. Through the encoder-decoder network, the normal payload can be well-reconstructed while the anomaly payload fails. With the attention mechanism, AEDRAD generates practical features for further anomaly detection from a global perspective. Meanwhile, it marks abnormal fragments visually, which is conducive to a subsequent analysis by experts. The experimental results show that AEDRAD significantly outperforms three state-of-the-art unsupervised algorithms on two real datasets.

关键词： HTTP Payload Anomaly Detection Network Security Deep learning encoder-decoder recurrent neural networks Attention Mechanism

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AUTOMATIC SINGING TRANSCRIPTION BASED ON encoder-decoder recurrent neural networks WITH A WEAKLY-SUPERVISED ATTENTION MECHANISM 44

AUTOMATIC SINGING TRANSCRIPTION BASED ON ENCODER-DECODER REC...

引用

44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Nishikimi, Ryo Nakamura, Eita Fukayama, Satoru Goto, Masataka Yoshii, Kazuyoshi Kyoto Univ Grad Sch Informat Kyoto Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9781479981311

This paper describes neural singing transcription that estimates a sequence of musical notes directly from the audio signal of singing voice in an end-to-end manner without time-aligned training data. A conventional approach to singing transcription is to perform vocal F0 estimation followed by musical note estimation. The performance of this approach, however, is severely limited because the F0 estimation errors propagate to the note estimation step and rich acoustic information cannot be used. In addition, it is difficult and time-consuming to split continuous signals of singing voices into segments corresponding to musical notes for making precise time-aligned transcriptions. To solve these problems, we use an encoder-decoder model with an attention mechanism that can automatically learn an input-output alignment and mapping, even from non-aligned training data. The main challenge of our study is to estimate temporal categories (note values) in addition to instantaneous categories (pitches). We thus propose a novel loss function for the attention weights of time-aligned notes for semi-supervised alignment training. By gradually reducing the weight of the loss function, a better input-output alignment can be learned much more quickly. We showed that our method performed well for isolated singing voice in popular music.

关键词： Automatic singing transcription end-to-end learning sequence-to-sequence learning encoder-decoder recurrent neural networks attention mechanism

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END-TO-END MELODY NOTE TRANSCRIPTION BASED ON A BEAT-SYNCHRONOUS ATTENTION MECHANISM

END-TO-END MELODY NOTE TRANSCRIPTION BASED ON A BEAT-SYNCHRO...

引用

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

作者： Nishikimi, Ryo Nakamura, Eita Goto, Masataka Yoshii, Kazuyoshi Kyoto Univ Grad Sch Informat Kyoto Japan Natl Inst Adv Ind Sci & Technol Tokyo Japan

ISBN: (纸本)9781728111230

This paper describes an end-to-end audio-to-symbolic singing transcription method for mixtures of vocal and accompaniment parts. Given audio signals with non-aligned melody scores, we aim to train a recurrent neural network that takes as input a magnitude spectrogram and outputs a sequence of melody notes represented by pairs of pitches and note values (durations). A promising approach to such sequence-to-sequence learning (joint input-to-output alignment and mapping) is to use an encoder-decoder model with an attention mechanism. This approach, however, cannot be used straightforwardly for singing transcription because a note-level decoder fails to estimate note values from latent representations obtained by a frame-level encoder that is good at extracting instantaneous features, but poor at extracting temporal features. To solve this problem, we focus on tatums instead of notes as output units and propose a tatum-level decoder that sequentially outputs tatum-level score segments represented by note pitches, note onset frags, and beat and downbeat flags. We then propose a beat-synchronous attention mechanism constrained in order to monotonically align tatum-level scores with input audio signals with a steady increment. The experimental results showed that the proposed method can be trained successfully from non-aligned data thanks to the beat-synchronous attention mechanism.

关键词： Automatic singing transcription end-to-end learning sequence-to-sequence learning encoder-decoder recurrent neural networks attention mechanism

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