online context aware intelligent anomaly detection
play

Online, Context-aware, Intelligent Anomaly Detection, Causality and - PowerPoint PPT Presentation

Apr 24, 2023 •94 likes •156 views

Online, Context-aware, Intelligent Anomaly Detection, Causality and Consequence Analysis, and Response Suggestion for SCADA Systems Wenyu Ren, Tim Yardley , Klara Nahrstedt University of Illinois Urbana-Champaign, Urbana, Illinois, USA

  1. Online, Context-aware, Intelligent Anomaly Detection, Causality and Consequence Analysis, and Response Suggestion for SCADA Systems Wenyu Ren, Tim Yardley , Klara Nahrstedt University of Illinois Urbana-Champaign, Urbana, Illinois, USA cred-c.org | 1

  2. Motivation • SCADA (Supervisory Control and Data Acquisition) • Widely used in EDS to gather measurement data from field devices and send control commands to them • Vulnerable to various cyberattacks • Heterogenous resource-constrained end devices • legacy control protocols cred-c.org | 2

  3. Motivation Step Path • Gap • Most of existing solutions only focus on Network Traffic Data monitoring and event detection of network state at the transport layer and perform flow-level analysis Event Detection Knowledge • Even solutions which parse the application protocol can usually detect the event only but fail to provide any causes and consequences of the event. cred-c.org | 3

  4. Our Approach Step Path • Objective An online, context-aware, intelligent Network Traffic Data framework for anomaly detection, anomalous event analysis, causal reasoning, consequence indication and response suggestion for SCADA Event Detection Knowledge networks • Feature Causality and • Utilizes not only transport-layer statistics but Consequence Understanding also application-layer statistics Analysis • Analyzes potential causes and consequences • Provides valuable response and recovery plan Response Action Suggestion cred-c.org | 4

  5. Framework Architecture Domain knowledge and cyber-physical model Anomaly Detector Flow-level Module Causes, Causality-based Network Parsed Consequences Anomalies Control-protocol- Traffic Data Analyzer and Suggested level Module Responses Content-level Module cred-c.org | 5

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

ADNA: online, context-aware, intelligent framework for Anomaly Detection aNd Analysis in SCADA

ADNA: online, context-aware, intelligent framework for Anomaly Detection aNd Analysis in SCADA

ADNA: online, context-aware, intelligent framework for Anomaly Detection aNd Analysis in SCADA networks Researchers: Wenyu Ren , Klara Nahrstedt, Tim Yardley Motivation Supervisory Control And Data Acquisition (SCADA) Problem with

1.12k views • 22 slides
Using Machine Learning for Intelligent Storage Performance Anomaly Detection Ramakrishna Vadla,

Using Machine Learning for Intelligent Storage Performance Anomaly Detection Ramakrishna Vadla,

Using Machine Learning for Intelligent Storage Performance Anomaly Detection Ramakrishna Vadla, IBM Archana Chinnaiah, IBM Acknowledgement : Sumant Padbidri, Anbazhagan Mani Agenda Market Estimates & Forecasts Applications in

606 views • 12 slides
Anomaly Detection of Trajectories Junier B. Oliva Anomaly Detection An anomaly (or outlier)

Anomaly Detection of Trajectories Junier B. Oliva Anomaly Detection An anomaly (or outlier)

Anomaly Detection of Trajectories Junier B. Oliva Anomaly Detection An anomaly (or outlier) in a dataset is an instance that is abnormal or unlikely based on the rest of the dataset If the instances in the dataset are labeled as

760 views • 19 slides
What is an anomaly? Alastair Rushworth Data Scientist DataCamp Anomaly Detection in R Defining

What is an anomaly? Alastair Rushworth Data Scientist DataCamp Anomaly Detection in R Defining

DataCamp Anomaly Detection in R ANOMALY DETECTION IN R What is an anomaly? Alastair Rushworth Data Scientist DataCamp Anomaly Detection in R Defining the term anomaly Anomaly: a data point or collection of data points that do not follow the

870 views • 21 slides
Anomaly Detection Jia-Bin Huang Virginia Tech Spring 2019 ECE-5424G / CS-5824 Administrative

Anomaly Detection Jia-Bin Huang Virginia Tech Spring 2019 ECE-5424G / CS-5824 Administrative

Anomaly Detection Jia-Bin Huang Virginia Tech Spring 2019 ECE-5424G / CS-5824 Administrative Anomaly Detection Motivation Developing an anomaly detection system Anomaly detection vs. supervised learning Choosing what features

514 views • 34 slides
Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier

Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier

Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier Detection Automatically identify data points that are somehow different from the rest Working assumption: There are considerably

984 views • 72 slides
Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier

Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier

Data Mining II Anomaly Detection Heiko Paulheim Anomaly Detection Also known as Outlier Detection Automatically identify data points that are somehow different from the rest Working assumption: There are considerably

905 views • 74 slides
Inter-Arrival Curves for Multi-Mode and Online Anomaly Detection Mahmoud Salem, Mark Crowley,

Inter-Arrival Curves for Multi-Mode and Online Anomaly Detection Mahmoud Salem, Mark Crowley,

Work-in-Progress Session Inter-Arrival Curves for Multi-Mode and Online Anomaly Detection Mahmoud Salem, Mark Crowley, and Sebastian Fischmeister 2 Inter-arrival Curves for Anomaly Detection [1] Inter-arrival curves make good features for

514 views • 8 slides
Anomaly Detection for the CERN Large Hadron Collider injection magnets Armin Halilovic KU

Anomaly Detection for the CERN Large Hadron Collider injection magnets Armin Halilovic KU

Anomaly Detection for the CERN Large Hadron Collider injection magnets Armin Halilovic KU Leuven - Department of Computer Science In cooperation with CERN 2018-07-27 0 Outline 1 Context 2 Data 3 Preprocessing 4 Anomaly Detection 5

792 views • 51 slides
Learning Rules for Anomaly Detection (LERAD) of Hostile Network Traffic Matt Mahoney Overview

Learning Rules for Anomaly Detection (LERAD) of Hostile Network Traffic Matt Mahoney Overview

Learning Rules for Anomaly Detection (LERAD) of Hostile Network Traffic Matt Mahoney Overview Prior Work in Network Anomaly Detection The 1999 DARPA Intrusion Detection Evaluation Packet Header Anomaly Detection (PHAD) Application

575 views • 18 slides
<Title> Yiqun Hu, SP Group Agenda Condition monitoring & anomaly detection

<Title> Yiqun Hu, SP Group Agenda Condition monitoring & anomaly detection

Data Council Singapre 2019 Autoencoder Forest for Anomaly Detection from IoT Time Series <Title> Yiqun Hu, SP Group Agenda Condition monitoring & anomaly detection Autoencoder for anomaly detection Autoencoder

540 views • 27 slides
Sensitivity of PCA for Traffic Anomaly Detection Evaluating the robustness of current best

Sensitivity of PCA for Traffic Anomaly Detection Evaluating the robustness of current best

Sensitivity of PCA for Traffic Anomaly Detection Evaluating the robustness of current best practices Haakon Ringberg 1 , Augustin Soule 2 , Jennifer Rexford 1 , Christophe Diot 2 1 Princeton University, 2 Thomson Research Outline Context

598 views • 15 slides
In Incorporating Feedback in into Tree-based Anomaly Detection Shubhomoy Das, Weng-Keen Wong,

In Incorporating Feedback in into Tree-based Anomaly Detection Shubhomoy Das, Weng-Keen Wong,

In Incorporating Feedback in into Tree-based Anomaly Detection Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G. Dietterich and Md Amran Siddiqui School of EECS Anomaly Detection Goal: Identify rare or strange objects 2 Anomaly

756 views • 51 slides
Dataflow Anomaly Detection Presented By Archana Viswanath Computer Science and Engineering The

Dataflow Anomaly Detection Presented By Archana Viswanath Computer Science and Engineering The

Dataflow Anomaly Detection Presented By Archana Viswanath Computer Science and Engineering The Pennsylvania State University Anomaly Intrusion Detection Systems Anomaly Intrusion Detection Systems Model normal behavior. Attack - Any

417 views • 20 slides
Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection Jiong Zhang and

Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection Jiong Zhang and

Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection Jiong Zhang and Mohammad Zulkernine School of Computing Queens University, Kingston Ontario, Canada K7L 3N6 {zhang, mzulker} @cs.queensu.ca Abstract- Anomaly

284 views • 6 slides
Efficient Anomaly Detection by Isolation using Nearest Neighbour Ensemble Tharindu

Efficient Anomaly Detection by Isolation using Nearest Neighbour Ensemble Tharindu

Information Technology Efficient Anomaly Detection by Isolation using Nearest Neighbour Ensemble Tharindu Rukshan Bandaragoda Kai Ming Ting David Albrecht Fei Tony Liu Jonathan R. Wells Outline Overview of anomaly detection

657 views • 41 slides
Anomalies, Chern-Simons Terms, and Black Hole Entropy ! Tatsuo Azeyanagi (ENS) ! ! Based on

Anomalies, Chern-Simons Terms, and Black Hole Entropy ! Tatsuo Azeyanagi (ENS) ! ! Based on

Anomalies, Chern-Simons Terms, and Black Hole Entropy ! Tatsuo Azeyanagi (ENS) ! ! Based on arXiv:1311.2940, 1407.6364 and to appear ! with R. Loganayagam (IAS), G.S. Ng (McGill), M.J. Rodriguez (Harvard) ! Workshop Holographic Methods for

464 views • 33 slides
Anomaly and gaugino mediation Supergravity mediation X is in the hidden sector, M P l

Anomaly and gaugino mediation Supergravity mediation X is in the hidden sector, M P l

Anomaly and gaugino mediation Supergravity mediation X is in the hidden sector, M P l suppressed couplings W = W hid ( X ) + W vis ( ) c i j e V i + . . . i Pl X X j j f = M 2 YM 2 + i 4 k

574 views • 29 slides
StrobeLight: Lightweight Availability Mapping and Anomaly Detection James Mickens, John Douceur,

StrobeLight: Lightweight Availability Mapping and Anomaly Detection James Mickens, John Douceur,

StrobeLight: Lightweight Availability Mapping and Anomaly Detection James Mickens, John Douceur, Bill Bolosky Brian Noble At any given moment, how can we tell which enterprise machines are online and network-reachable? Mobile AJAX

603 views • 34 slides
from System Logs through Deep Learning Min Du , Feifei Li, Guineng Zheng, Vivek Srikumar

from System Logs through Deep Learning Min Du , Feifei Li, Guineng Zheng, Vivek Srikumar

DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning Min Du , Feifei Li, Guineng Zheng, Vivek Srikumar University of Utah Background 2 Background System Event Log 3 Background System Event Log Available

1.08k views • 106 slides
Anomalies and Topological phases in QFT Kazuya Yonekura, Kyushu University 2 Introduction

Anomalies and Topological phases in QFT Kazuya Yonekura, Kyushu University 2 Introduction

1 Anomalies and Topological phases in QFT Kazuya Yonekura, Kyushu University 2 Introduction Symmetry and Anomaly: I hope I dont need to explain the importance of these concepts in QFT. However, what is symmetry, and what is anomaly? /

618 views • 60 slides
The Location and Evolution of the South Atlantic Anomaly as Observed by SOLSTICE Laura

The Location and Evolution of the South Atlantic Anomaly as Observed by SOLSTICE Laura

The Location and Evolution of the South Atlantic Anomaly as Observed by SOLSTICE Laura OConnor (University of Michigan) Mentor: Marty Snow (Laboratory for Atmospheric and Space Physics) Outline Background information Description of

421 views • 30 slides
40 years of the Weyl anomaly M. J. Duff Physics Department Imperial College London CFT Beyond

40 years of the Weyl anomaly M. J. Duff Physics Department Imperial College London CFT Beyond

40 years of the Weyl anomaly M. J. Duff Physics Department Imperial College London CFT Beyond Two Dimensions Texas A&M March 2012 1 / 50 Abstract Classically, Weyl invariance S ( g , ) = S ( g , ) under = ( x )

881 views • 50 slides
STAD-HD: Spatial Temporal Anomaly Detection for Heterogeneous Data through Visual Analytics

STAD-HD: Spatial Temporal Anomaly Detection for Heterogeneous Data through Visual Analytics

STAD-HD: Spatial Temporal Anomaly Detection for Heterogeneous Data through Visual Analytics Solution for 2016 VAST Challenge MC2 & MC3 Yu Zhang 1 , Guozheng Li 1 , Chufan Lai 1 , Qiangqiang Liu 1 , Shuai Chen 1 , Lu Feng 1 , Tangzhi Ye 1 ,

430 views • 29 slides

More recommend