Using sequential drift detection to test the API economy

• URL: http://arxiv.org/abs/2111.05136v1
• Date: Tue, 9 Nov 2021 13:24:19 GMT
• Title: Using sequential drift detection to test the API economy
• Authors: Samuel Ackerman, Parijat Dube, Eitan Farchi
• Abstract summary: API economy refers to the widespread integration of API (advanced programming interface) It is desirable to monitor the usage patterns and identify when the system is used in a way that was never used before. In this work we analyze both histograms and call graph of API usage to determine if the usage patterns of the system has shifted.
• Score: 4.056434158960926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The API economy refers to the widespread integration of API (advanced programming interface) microservices, where software applications can communicate with each other, as a crucial element in business models and functions. The number of possible ways in which such a system could be used is huge. It is thus desirable to monitor the usage patterns and identify when the system is used in a way that was never used before. This provides a warning to the system analysts and they can ensure uninterrupted operation of the system. In this work we analyze both histograms and call graph of API usage to determine if the usage patterns of the system has shifted. We compare the application of nonparametric statistical and Bayesian sequential analysis to the problem. This is done in a way that overcomes the issue of repeated statistical tests and insures statistical significance of the alerts. The technique was simulated and tested and proven effective in detecting the drift in various scenarios. We also mention modifications to the technique to decrease its memory so that it can respond more quickly when the distribution drift occurs at a delay from when monitoring begins.

Related papers

• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4 [45.935748395725206]
  We introduce a prompt engineering-assisted malware dynamic analysis using GPT-4. In this method, GPT-4 is employed to create explanatory text for each API call within the API sequence. BERT is used to obtain the representation of the text, from which we derive the representation of the API sequence.
  arXiv  Detail & Related papers  (2023-12-13T17:39:44Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• Lightweight Automated Feature Monitoring for Data Streams [1.4658400971135652]
  We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets. It monitors all features that are used by the system, while providing an interpretable features ranking whenever an alarm occurs. This illustrates how FM eliminates the need to add custom signals to detect specific types of problems and that monitoring the available space of features is often enough.
  arXiv  Detail & Related papers  (2022-07-18T14:38:11Z)
• Detecting Anomalous Event Sequences with Temporal Point Processes [28.997992932163008]
  We frame the problem of detecting anomalous continuous-time event sequences as out-of-distribution (OoD) detection for temporal point processes (TPPs) First, we show how this problem can be approached using goodness-of-fit (GoF) tests. We then demonstrate the limitations of popular GoF statistics for TPPs and propose a new test that addresses these shortcomings.
  arXiv  Detail & Related papers  (2021-06-08T15:50:12Z)
• Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models [59.04636530383049]
  Anomalies or failures in large computer systems, such as the cloud, have an impact on a large number of users. We propose a framework for anomaly detection in log data, as a major troubleshooting source of system information.
  arXiv  Detail & Related papers  (2021-02-23T09:17:05Z)
• Behavioral Model Inference of Black-box Software using Deep Neural Networks [1.6593369275241105]
  Many software engineering tasks, such as testing, and anomaly detection can benefit from the ability to infer a behavioral model of the software. Most existing inference approaches assume access to code to collect execution sequences. We show how this approach can be used to accurately detect state changes, and how the inferred models can be successfully applied to transfer-learning scenarios.
  arXiv  Detail & Related papers  (2021-01-13T09:23:37Z)
• Hybrid Deep Neural Networks to Infer State Models of Black-Box Systems [2.294541416972175]
  We propose a hybrid deep neural network that accepts as input a set of time series, one per input/output signal of the system. We have applied our approach on a real UAV auto-pilot solution from our industry partner with half a million lines of C code.
  arXiv  Detail & Related papers  (2020-08-26T23:24:34Z)
• Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation [69.34035527763916]
  Change point detection (CPD) aims to locate abrupt property changes in time series data. Recent CPD methods demonstrated the potential of using deep learning techniques, but often lack the ability to identify more subtle changes in the autocorrelation statistics of the signal. We employ an autoencoder-based methodology with a novel loss function, through which the used autoencoders learn a partially time-invariant representation that is tailored for CPD.
  arXiv  Detail & Related papers  (2020-08-21T15:03:21Z)
• Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction [58.98112070128482]
  We propose a lightweight solution for series prediction based on historic observations. It consists of a heterogeneous ensemble method composed of two models - a neural network and a mean predictor. It achieves an overall $R2$ score of 0.10 on the available FedCSIS 2020 challenge dataset.
  arXiv  Detail & Related papers  (2020-07-07T15:44:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.