Anomaly detection is a critical step towards building a secure and trustworthy system. The primary purpose of a system log is to record system states and significant events at various critical points to help debug system failures and perform root cause analysis. Such log data is universally available in nearly all computer systems. Log data is an important and valuable resource for understanding system status and performance issues; therefore, the various system logs are naturally excellent source of information for online monitoring and anomaly detection. We propose DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), to model a system log as a natural language sequence. This allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution. In addition, we demonstrate how to incrementally update the DeepLog model in an online fashion so that it can adapt to new log patterns over time. Furthermore, DeepLog constructs workflows from the underlying system log so that once an anomaly is detected, users can diagnose the detected anomaly and perform root cause analysis effectively. Extensive experimental evaluations over large log data have shown that DeepLog has outperformed other existing log-based anomaly detection methods based on traditional data mining methodologies.

https://dl.acm.org/doi/pdf/10.1145/3133956.3134015

DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning

Surprisingly, console logs rarely help operators detect problems in large-scale datacenter services, for they often consist of the voluminous intermixing of messages from many software components written by independent developers We propose a general methodology to mine this rich source of information to automatically detect system runtime problems We first parse console logs by combining source code analysis with information retrieval to create composite features We then analyze these features using machine learning to detect operational problems We show that our method enables analyses that are impossible with previous methods because of its superior ability to create sophisticated features We also show how to distill the results of our analysis to an operator-friendly one-page decision tree showing the critical messages associated with the detected problems We validate our approach using the Darkstar online game server and the Hadoop File System, where we detect numerous real problems with high accuracy and few false positives In the Hadoop case, we are able to analyze 24 million lines of console logs in 3 minutes Our methodology works on textual console logs of any size and requires no changes to the service software, no human input, and no knowledge of the software's internals

https://people.eecs.berkeley.edu/~jordan/papers/xu-etal-sosp09.pdf

Detecting large-scale system problems by mining console logs

Surprisingly, console logs rarely help operators detect problems in large-scale datacenter services, for they often consist of the voluminous intermixing of messages from many software components written by independent developers. We propose a general methodology to mine this rich source of information to automatically detect system runtime problems. We use a combination of program analysis and information retrieval techniques to transform free-text console logs into numerical features, which captures sequences of events in the system. We then analyze these features using machine learning to detect operational problems. We also show how to distill the results of our analysis to an operator-friendly one-page decision tree showing the critical messages associated with the detected problems. In addition, we extend our methods to online problem detection where the sequences of events are continuously generated as data streams.

/pdf/detecting-large-scale-system-problems-by-mining-console-logs-5eadzzpdkv.pdf

Detecting Large-Scale System Problems by Mining Console Logs

This survey attempts to provide a comprehensive and structured overview of the existing research for the problem of detecting anomalies in discrete/symbolic sequences. The objective is to provide a global understanding of the sequence anomaly detection problem and how existing techniques relate to each other. The key contribution of this survey is the classification of the existing research into three distinct categories, based on the problem formulation that they are trying to solve. These problem formulations are: 1) identifying anomalous sequences with respect to a database of normal sequences; 2) identifying an anomalous subsequence within a long sequence; and 3) identifying a pattern in a sequence whose frequency of occurrence is anomalous. We show how each of these problem formulations is characteristically distinct from each other and discuss their relevance in various application domains. We review techniques from many disparate and disconnected application domains that address each of these formulations. Within each problem formulation, we group techniques into categories based on the nature of the underlying algorithm. For each category, we provide a basic anomaly detection technique, and show how the existing techniques are variants of the basic technique. This approach shows how different techniques within a category are related or different from each other. Our categorization reveals new variants and combinations that have not been investigated before for anomaly detection. We also provide a discussion of relative strengths and weaknesses of different techniques. We show how techniques developed for one problem formulation can be adapted to solve a different formulation, thereby providing several novel adaptations to solve the different problem formulations. We also highlight the applicability of the techniques that handle discrete sequences to other related areas such as online anomaly detection and time series anomaly detection.

/pdf/anomaly-detection-for-discrete-sequences-a-survey-4gylbpv23q.pdf

Anomaly Detection for Discrete Sequences: A Survey

If we hope to automatically detect and diagnose failures in large-scale computer systems, we must study real deployed systems and the data they generate. Progress has been hampered by the inaccessibility of empirical data. This paper addresses that dearth by examining system logs from five supercomputers, with the aim of providing useful insight and direction for future research into the use of such logs. We present details about the systems, methods of log collection, and how alerts were identified; propose a simpler and more effective filtering algorithm; and define operational context to encompass the crucial information that we found to be currently missing from most logs. The machines we consider (and the number of processors) are: Blue Gene/L (131072), Red Storm (10880), Thunderbird (9024), Spirit (1028), and Liberty (512). This is the first study of raw system logs from multiple supercomputers.

/pdf/what-supercomputers-say-a-study-of-five-system-logs-1rwf6iuf3m.pdf

What Supercomputers Say: A Study of Five System Logs

Syslog monitoring technologies have recently received vast attentions in the areas of network management and network monitoring They are used to address a wide range of important issues including network failure symptom detection and event correlation discovery Syslogs are intrinsically dynamic in the sense that they form a time series and that their behavior may change over time This paper proposes a new methodology of dynamic syslog mining in order to detect failure symptoms with higher confidence and to discover sequential alarm patterns among computer devices The key ideas of dynamic syslog mining are 1) to represent syslog behavior using a mixture of Hidden Markov Models, 2) to adaptively learn the model using an on-line discounting learning algorithm in combination with dynamic selection of the optimal number of mixture components, and 3) to give anomaly scores using universal test statistics with a dynamically optimized threshold Using real syslog data we demonstrate the validity of our methodology in the scenarios of failure symptom detection, emerging pattern identification, and correlation discovery

Dynamic syslog mining for network failure monitoring

A model detection apparatus comprises a number of estimate parameter memories (32-1 ~ 32-K) for storing mutually different distribution estimate parameters representing occurrences of input data. A number of distribution estimators (31-1 ~ 31-K) are respectively associated with the parameter memories for producing distribution estimate parameters from data stored in the associated parameter memories and from a series of input data, and updating the associated parameter memories with the produced parameters. A model series memory (34) stores candidate models corresponding in number to the parameter memories. A model series estimator (33) produces candidate models using the series of input data, the stored distribution estimate parameters and the stored candidate models, and updates the model series memory (34) with the produced candidate models. An optimal model series calculator (35) calculates an optimal series of models from the candidate models stored in the model series memory.

Dynamic model detecting apparatus

It becomes increasingly important to detect intrusions with unknown patterns in order to protect our business from cyber terrorism threats. This paper introduces data mining technologies designed for this purpose; SmartSifter (outlier detection engine), ChangeFinder (change-point detection engine), AccessTracer (anomalous behavior detection engine). All of them are able to learn statistical patterns of logs adaptively and to detect intrusions as statistical anomalies relative to the learned patterns. We briefly overview the principles of these engines and illustrate their applications to network intrusion detection, worm detection, and masquerader detection.

Data mining for security

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yuko Maruyama

Author Tools

Chat about Author

Papers

Dynamic syslog mining for network failure monitoring

Dynamic model detecting apparatus

Data mining for security