BT-IDS: An anomaly-based Intrusion Detection System for Bluetooth - PowerPoint PPT Presentation

Dec 07, 2023 •153 likes •269 views

BT-IDS: An anomaly-based Intrusion Detection System for Bluetooth Devices Shalaka Satam, Pratik Satam and Salim Hariri Cloud and Autonomic Computing Center Semi Annual IAB Meeting Tucson, Arizona Project Team Members n UA Faculty p Salim

BT-IDS: An anomaly-based Intrusion Detection System for Bluetooth Devices Shalaka Satam, Pratik Satam and Salim Hariri Cloud and Autonomic Computing Center Semi Annual IAB Meeting Tucson, Arizona
Project Team Members n UA Faculty p Salim Hariri n UA Graduate Students p Shalaka Satam p Pratik Satam 2
Background n IoT devices depend heavily on wireless networks for communications. n Bluetooth networks have become the wireless network choice for small range communications. n Bluetooth networks need to be secure against cyberattacks like battery draining attack, DoS, etc. 3
Project Overview 4
Project Tasks: Overview p Task 1: Develop innovative data structures (Bluetooth Flow (BTF)) that can accurately model the Bluetooth normal operations p Task 2: Design an architecture for the Bluetooth Intrusion Detection System (BT-IDS) p Task 3: Build machine learning models that characterize the normal behavior that can be used to detect any attack against Bluetooth network 5
Task 1: Develop BT Flow data structures that model the Bluetooth normal operations • N-gram - A data structure that is used to represent an N state transitions sequence of the BT protocol state diagram into one single unit (N-gram). • BT Flow (BTF) - A collection of Ngrams that follow one specific flow of Bluetooth traffic. 6
Task 2: Design a BT-IDS Architecture Sniffer Module • Its main function is to collect data frames that are transmitted over the Bluetooth network • BT Behavior Analysis Unit (BT-BAU) It performs data-driven analytics to detect any abnormal behavior in BT network operations that can be triggered by BT attacks 7
Task 3: Develop machine learning models that characterize the BT normal operations Preliminary analysis 8
Activities and outcomes p The primary goal of this project is to develop an intrusion detection system (IDS) for Bluetooth network that uses anomaly based detection approach. p The Bluetooth IDS (BT-IDS) can detect and classify accurately new and modified attacks with little or no false positives and false negatives. 9
Deliverables and benefits Deliverables • Midterm and final reports documenting research methods, and evaluation results • One or two scholarly conference and/or journal publications Benefits • Capability to detect and classify attacks against Bluetooth networks. 10
LIFE Form Input Please take a moment to fill out your L.I.F.E. forms. http://www.iucrc.com Select “Cloud and Autonomic Computing Center” then select “IAB” role. What do you like about this project? What would you change? (Please include all relevant feedback.) 11

Recommend

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

PANACEA: AUTOMATING ATTACK CLASSIFICATION FOR ANOMALY-BASED NETWORK INTRUSION DETECTION SYSTEMS

PANACEA: AUTOMATING ATTACK CLASSIFICATION FOR ANOMALY-BASED NETWORK INTRUSION DETECTION SYSTEMS DAMIANO BOLZONI, SANDRO ETALLE AND PIETER HARTEL DISTRIBUTED AND EMBEDDED SECURITY GROUP TWENTE SECURITY LAB 10+ YEARS OF RESEARCH OVER ANOMALY

160 views • 14 slides

Detecting Attacks Anomaly-based Detection Signature-based Signature-based (Misuse)

Detecting Attacks Anomaly-based Detection Signature-based Signature-based (Misuse) Detection Intrusion Detection Host-based Network-based Boriana Ditcheva and Lisa Fowler Active/Passive University of North Carolina at

401 views • 12 slides

Evading Network Anomaly Detection Sytems - Fogla,Lee Divya Muthukumaran Intrusion detection

Evading Network Anomaly Detection Sytems - Fogla,Lee Divya Muthukumaran Intrusion detection Systems Signature Based IDS Monitor packets on the network Compare them against database of signatures/attributes from known threats

476 views • 26 slides

Memristor Based Autoencoder for Unsupervised Real-Time Network Intrusion and Anomaly Detection

Memristor Based Autoencoder for Unsupervised Real-Time Network Intrusion and Anomaly Detection Md. Shahanur Alam, B. Rasitha Fernando, Yassine Jaoudi, Chris Yakopcic, Raqibul Hasan, Tarek M. Taha, and Guru Subramanyam Dept. Of Electrical and

428 views • 19 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 Pennsylvania State University Anomaly Intrusion Detection Systems Anomaly Intrusion Detection Systems Model normal behavior. Attack - Any

417 views • 20 slides

Anomaly Based Intrusion Detection in Distributed Applications without global clock Eric Totel,

Anomaly Based Intrusion Detection in Distributed Applications without global clock Eric Totel, Mouna Hkimi, Michel Hurfin, Mourad Leslous, Yvan Labiche SEC2-2016 5 July 2016 Outline of the Presentation Position of the problem Building

374 views • 26 slides

Styles of Intrusion Detection Misuse intrusion detection Try to detect things known to be

Styles of Intrusion Detection Misuse intrusion detection Try to detect things known to be bad Anomaly intrusion detection Try to detect deviations from normal behavior Specification intrusion detection Try to detect

627 views • 15 slides

Building a provenance-based intrusion detection system Thomas Pasquier, University of Bristol

Building a provenance-based intrusion detection system Thomas Pasquier, University of Bristol Toshiba, 26/11/2020 1 Talk loosely based on following publications Han et al. UNICORN: Revisiting Host-Based Intrusion Detection in the Age of

783 views • 55 slides

360 Unsupervised Anomaly-based Intrusion Detection Stefano Zanero , Ph.D. Stefano Zanero ,

Politecnico di Milano Dip. Elettronica e Informazione Milano, Italy 360 Unsupervised Anomaly-based Intrusion Detection Stefano Zanero , Ph.D. Stefano Zanero , Ph.D. Post-doc Researcher, Politecnico di Milano CTO & Founder, Secure

522 views • 39 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 Prior Work in Network Anomaly Detection The 1999 DARPA Intrusion Detection Evaluation Packet Header Anomaly Detection (PHAD) Application

575 views • 18 slides

Intrusion Detection System Amir Hossein Payberah payberah@yahoo.com 1 Contents Intrusion

Intrusion Detection System Amir Hossein Payberah payberah@yahoo.com 1 Contents Intrusion Detection Systems Tripwire Snort 2 IDS (Definition) Intrusion Detection is the process of monitoring the events occurring in a computer

571 views • 30 slides

Intrusion Detection Distributed Host-Based Network-Based ITS335: IT Security Honeypots

ITS335 Intrusion Detection Intruders Intrusion Detection Host-Based Intrusion Detection Distributed Host-Based Network-Based ITS335: IT Security Honeypots Summary Sirindhorn International Institute of Technology Thammasat University

585 views • 30 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) 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

Customizing and Evolving Intrusion Detection A static, globally useful intrusion detection

Customizing and Evolving Intrusion Detection A static, globally useful intrusion detection solution is impossible Good behavior on one system is bad behavior on another Behaviors change and new vulnerabilities are discovered

700 views • 23 slides

Poisoning Attacks on Federated Le Learning-based In Intrusion Detection System Thien Duc

Poisoning Attacks on Federated Le Learning-based In Intrusion Detection System Thien Duc Nguyen, Phillip Rieger, Markus Miettinen, Ahmad-Reza Sadeghi Typical IoT Devices 2 IoT The S stands for Security 3 Mirai: Largest Disruptive

509 views • 33 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 Motivation Developing an anomaly detection system Anomaly detection vs. supervised learning Choosing what features

514 views • 34 slides

Signature Based Intrusion Detection Systems Philip Chan CS 598 MCC Spring 2013 Intrusion

Signature Based Intrusion Detection Systems Philip Chan CS 598 MCC Spring 2013 Intrusion Detection Systems Detect malicious Raise alarms activities/attacks Alert administrators Hacking/ unauthorized access Trigger defense

217 views • 21 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, 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

Kargus: A Highly scalable Software based Intrusion Detection System M. Asim Jamshed * ,

Kargus: A Highly scalable Software based Intrusion Detection System M. Asim Jamshed * , Jihyung Lee , Sangwoo Moon , InsuYun * , Deokjin Kim , Sungryoul Lee , Yung Yi , KyoungSoo Park * * Networked & Distributed

776 views • 41 slides

A NEW HOD BASED INTRUSION DETECTION SYSTEM FOR WIRELESS SENSOR NETWORK IK2206: Internet Security

A NEW HOD BASED INTRUSION DETECTION SYSTEM FOR WIRELESS SENSOR NETWORK IK2206: Internet Security and Privacy Sumanta Saha Md. Safiqul Islam Md. Sakhawat Hossen W IRELESS A D H OC N ETWORK Wireless ad hoc networks are autonomous nodes that

372 views • 15 slides

Constrained approximate search in misuse-based intrusion detection Ambika Shrestha Chitrakar

Constrained approximate search in misuse-based intrusion detection Ambika Shrestha Chitrakar Supervisor: Prof. Slobodan Petrovic FINSE 10th of may 2017 Contents Introduction Snort: a misuse-based intrusion detection Problem

247 views • 14 slides

Intrusion Detection Principles Basics Models of Intrusion Detection

Intrusion Detection Principles Basics Models of Intrusion Detection Architecture of an IDS Architecture of an IDS Organization Incident Response Slide #22-1 FEARLESS engineering Principles of Intrusion Detection

744 views • 40 slides

An Ontology Based Anomaly Detection System for Vehicular Communications Quentin Ricard, Philippe

An Ontology Based Anomaly Detection System for Vehicular Communications Quentin Ricard, Philippe Owezarski qricard@laas.fr owe@laas.fr ERTS - 2020 10th European Congress on Embedded Real Time Software and Systems January 29, 2020 Laboratoire

1.32k views • 22 slides