2021 article

Mining Anomalies in Subspaces of High-Dimensional Time Series for Financial Transactional Data

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021: APPLIED DATA SCIENCE TRACK, PT IV, Vol. 12978, pp. 19–36.

author keywords: Unsupervised anomaly retrieval; High-dimensional time series; Subspace searching; Data mining
TL;DR: A novel and practical unsupervised anomaly retrieval system to retrieve anomalies from a large volume of high dimensional transactional time series and can localize high quality anomaly candidates in seconds, making it practical to use in a production environment. (via Semantic Scholar)
Source: Web Of Science
Added: November 15, 2021

2020 article

Self-Patch: Beyond Patch Tuesday for Containerized Applications

2020 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS (ACSOS 2020), pp. 21–27.

By: O. Tunde-Onadele n, Y. Lin n, J. He n & X. Gu n

author keywords: Container Security; Anomaly Detection; Security Patching
TL;DR: Self-Patch is presented, a new self-triggering patching framework for applications running inside containers that combines light-weight runtime attack detection and dynamic targeted patching to achieve more efficient and effective security protection for containerized applications. (via Semantic Scholar)
Source: Web Of Science
Added: November 29, 2021

2019 article

A Study on Container Vulnerability Exploit Detection

2019 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING (IC2E), pp. 121–127.

By: O. Tunde-Onadele n, J. He n, T. Dai n & X. Gu n

author keywords: Container Security; Anomaly Detection; Machine Learning
TL;DR: This paper implements and evaluates a set of static and dynamic vulnerability attack detection schemes using 28 real world vulnerability exploits that widely exist in docker images and shows that the static vulnerability scanning scheme only detects 3 out of 28 tested vulnerabilities and dynamic anomaly detection schemes detect 22 vulnerability exploits. (via Semantic Scholar)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure (OpenAlex)
Source: Web Of Science
Added: January 6, 2020

2019 article

TFix: Automatic Timeout Bug Fixing in Production Server Systems

2019 39TH IEEE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2019), pp. 612–623.

By: J. He n, T. Dai n & X. Gu n

TL;DR: TFix is presented, an automatic timeout bug fixing system for correcting misused timeout bugs in production systems that adopts a drill-down bug analysis protocol that can narrow down the root cause of a misusedtimeout bug and producing recommendations for correcting theRoot cause. (via Semantic Scholar)
Source: Web Of Science
Added: September 21, 2020

2018 article

DScope: Detecting Real-World Data Corruption Hang Bugs in Cloud Server Systems

PROCEEDINGS OF THE 2018 ACM SYMPOSIUM ON CLOUD COMPUTING (SOCC '18), pp. 313–325.

By: T. Dai n, J. He n, X. Gu n, S. Lu* & P. Wang n

author keywords: static analysis; data corruption; performance bug detection
TL;DR: DScope, a tool that statically detects data-corruption related software hang bugs in cloud server systems, and identifies loops whose exit conditions can be affected by I/O operations through returned data, returned error code, orI/O exception handling. (via Semantic Scholar)
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: March 4, 2019

2018 article

TScope: Automatic Timeout Bug Identification for Server Systems

15TH IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC 2018), pp. 1–10.

By: J. He*, T. Dai* & X. Gu*

TL;DR: TScope leverages kernel-level system call tracing and machine learning based anomaly detection and feature extraction schemes to achieve timeout bug identification and introduces a unique system call selection scheme to achieve higher accuracy than existing generic performance bug detection tools. (via Semantic Scholar)
Source: Web Of Science
Added: December 3, 2018

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