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公开(公告)号：US20230362180A1
公开(公告)日：2023-11-09
申请号：US17739968
申请日：2022-05-09
CPC分类号： H04L63/1425 , G06N20/20
摘要： Techniques for implementing a semi-supervised framework for purpose-oriented anomaly detection are provided. In one technique, a data item in inputted into an unsupervised anomaly detection model, which generates first output. Based on the first output, it is determined whether the data item represents an anomaly. In response to determining that the data item represents an anomaly, the data item is inputted into a supervised classification model, which generates second output that indicates whether the data item is unknown. In response to determining that the data item is unknown, a training instance is generated based on the data item. The supervised classification model is updated based on the training instance.
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公开(公告)号：US20240345811A1
公开(公告)日：2024-10-17
申请号：US18202756
申请日：2023-05-26
发明人： Arno Schneuwly , Saeid Allahdadian , Pritam Dash , Matteo Casserini , Felix Schmidt , Eric Sedlar
IPC分类号： G06F8/36 , G06F16/955 , G06F40/40
CPC分类号： G06F8/36 , G06F16/955 , G06F40/40
摘要： Herein for each source logic in a corpus, a computer stores an identifier of the source logic and operates a logic encoder that infers a distinct fixed-size encoded logic that represents the variable-size source logic. At build time, a multidimensional index is generated and populated based on the encoded logics that represent the source logics in the corpus. At runtime, a user may edit and select a new source logic such as in a text editor or an integrated development environment (IDE). The logic encoder infers a new encoded logic that represents the new source logic. The multidimensional index accepts the new encoded logic as a lookup key and automatically selects and returns a result subset of encoded logics that represent similar source logics in the corpus. For display, the multidimensional index may select and return only encoded logics that are the few nearest neighbors to the new encoded logic.
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公开(公告)号：US20240345815A1
公开(公告)日：2024-10-17
申请号：US18202564
申请日：2023-05-26
IPC分类号： G06F8/41
CPC分类号： G06F8/427
摘要： In an embodiment, a computer stores and operates a logic encoder that is an artificial neural network that infers a fixed-size encoded logic from textual or tokenized source logic. Without machine learning, a special parser generates a parse tree that represents the source logic and a fixed-size correctly encoded tree that represents the parse tree. For finetuning the logic encoder, an encoded tree generator is an artificial neural network that accepts the fixed-size encoded logic as input and responsively infers a fixed-size incorrectly encoded tree that represents the parse tree. The neural weights of the logic encoder (and optionally of the encoded tree generator) are adjusted based on backpropagation of error (i.e. loss) as a numerically measured difference between the fixed-size incorrectly encoded tree and the fixed-size correctly encoded tree.
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公开(公告)号：US12020131B2
公开(公告)日：2024-06-25
申请号：US17221212
申请日：2021-04-02
发明人： Saeid Allahdadian , Amin Suzani , Milos Vasic , Matteo Casserini , Andrew Brownsword , Felix Schmidt , Nipun Agarwal
IPC分类号： G06N20/20 , G06N3/04 , G06N3/0442 , G06N3/045 , G06N3/0495 , G06N3/08 , G06N3/088 , G06N20/00
CPC分类号： G06N20/20 , G06N3/04 , G06N3/0495 , G06N3/08 , G06N3/088 , G06N3/0442 , G06N3/045 , G06N20/00
摘要： Techniques are provided for sparse ensembling of unsupervised machine learning models. In an embodiment, the proposed architecture is composed of multiple unsupervised machine learning models that each produce a score as output and a gating network that analyzes the inputs and outputs of the unsupervised machine learning models to select an optimal ensemble of unsupervised machine learning models. The gating network is trained to choose a minimal number of the multiple unsupervised machine learning models whose scores are combined to create a final score that matches or closely resembles a final score that is computed using all the scores of the multiple unsupervised machine learning models.
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5.发明授权公开(公告)号：US11704386B2
公开(公告)日：2023-07-18
申请号：US17199563
申请日：2021-03-12
发明人： Amin Suzani , Saeid Allahdadian , Milos Vasic , Matteo Casserini , Hamed Ahmadi , Felix Schmidt , Andrew Brownsword , Nipun Agarwal
IPC分类号： G06F18/214 , G06N20/00 , G06V10/75 , G06F18/23
CPC分类号： G06F18/214 , G06F18/23 , G06N20/00 , G06V10/758
摘要： Herein are feature extraction mechanisms that receive parsed log messages as inputs and transform them into numerical feature vectors for machine learning models (MLMs). In an embodiment, a computer extracts fields from a log message. Each field specifies a name, a text value, and a type. For each field, a field transformer for the field is dynamically selected based the field's name and/or the field's type. The field transformer converts the field's text value into a value of the field's type. A feature encoder for the value of the field's type is dynamically selected based on the field's type and/or a range of the field's values that occur in a training corpus of an MLM. From the feature encoder, an encoding of the value of the field's typed is stored into a feature vector. Based on the MLM and the feature vector, the log message is detected as anomalous.
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公开(公告)号：US20220188694A1
公开(公告)日：2022-06-16
申请号：US17122401
申请日：2020-12-15
发明人： Amin Suzani , Matteo Casserini , Milos Vasic , Saeid Allahdadian , Andrew Brownsword , Hamed Ahmadi , Felix Schmidt , Nipun Agarwal
摘要： Approaches herein relate to model decay of an anomaly detector due to concept drift. Herein are machine learning techniques for dynamically self-tuning an anomaly score threshold. In an embodiment in a production environment, a computer receives an item in a stream of items. A machine learning (ML) model hosted by the computer infers by calculation an anomaly score for the item. Whether the item is anomalous or not is decided based on the anomaly score and an adaptive anomaly threshold that dynamically fluctuates. A moving standard deviation of anomaly scores is adjusted based on a moving average of anomaly scores. The moving average of anomaly scores is then adjusted based on the anomaly score. The adaptive anomaly threshold is then adjusted based on the moving average of anomaly scores and the moving standard deviation of anomaly scores.
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