We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byKari Foulks
Modified over 2 years ago
Architecture For An Artificial Immune System S. A. Hofmeyr and S. Forrest
What Do They Do? Mimic Immunse System functionality and method Apply method to Intrusion Detection
How Do They Do It? They detail the ARTIS system Adapt and apply it, to create LISYS
ARTIS Detector –Detector Trainer –Activation Threshold –Lifespan Memory Detector Costimulation
Why Is It Good? Robust –Diverse, Distributed, Dynamic Adaptable Autonomous
LISYS Detector –Datapath triple (src_ip,dest_ip,port) Detection Nodes on each internal machine
LISYS in action
Does It Work? Claimed: –Robust –Controlled (Tunable) –Scalable –Accurate –Adaptable –Lightweight
What Doesn’t It Do? Pass around memory detectors Respond to a detected “nonself”
How Can We Apply It To A.C.? Architecture? Methods? Inspiration?
Immunity by Design: An Artificial Immune System Paper: Steven A. Hofmeyr, Stephanie Forrest Presentation: Joseph Niehaus.
Distributed Network Intrusion Detection An Immunological Approach Steven Hofmeyr Stephanie Forrest Patrik D’haeseleer Dept. of Computer Science University.
Computation in the Wild Staphanie Forrest Justin Balthrop Matthew Glickman David Ackley Presented by Montana Low.
1 Principles of a Computer Immune System Anil Somayaji, Steven Hofmeyr, & Stephanie Forrest Presented by: Jesus Morales.
V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science Research Day The University of Memphis March 25, 2005.
Rogério de LemosDEFINE – Pisa, November 2002 – 1 Proactive Computing: Artificial Immune Systems Rogério de Lemos University of Kent at Canterbury Brian.
` Question: How do immune systems achieve such remarkable scalability? Approach: Simulate lymphoid compartments, fixed circulatory networks, cytokine communication.
Sensor B Sensor A Sensor C Sensor D Sensor E Lightweight Mining Techniques Time Frame: 10 Time Threshold: 20.
$100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300.
Anomaly Detection in Data Docent Xiao-Zhi Gao
A. How does life arise from the nonliving? 1.Generate a molecular proto-organism in vitro. 2.Achieve the transition to life in an artificial chemistry.
Artificial Immune Systems Andrew Watkins. Why the Immune System? Recognition –Anomaly detection –Noise tolerance Robustness Feature extraction Diversity.
Immune System Metaphors Applied to Intrusion Detection and Related Problems by Ian Nunn, SCS, Carleton University
Artificial Immune Systems Our body’s immune system is a perfect example of a learning system. It is able to distinguish between good cells and potentially.
Presentation By SANJOG BHATTA Student ID : July 1’ 2009.
Artificial Immune System-Based Mobile Node Movement Peter Matthews.
Click to edit master text Click to edit Master text styles Second level Third level Fourth level Fifth level Artificial Immune Systems Dr Uwe Aickelin.
Nasraoui, Gonzalez, Cardona, Dasgupta: Scalable Artificial Immune System Based Data Mining NSF-NGDM, Nov. 1-3, 2002, Baltimore, MD Artificial Immune Systems.
Surface Defect Inspection: an Artificial Immune Approach Dr. Hong Zheng and Dr. Saeid Nahavandi School of Engineering and Technology.
` Research 2: Information Diversity through Information Flow Subgoal: Systematically and precisely measure program diversity by measuring the information.
CS 1 – Introduction to Computer Science Introduction to the wonderful world of Dr. T Dr. Daniel Tauritz.
A Blackboard-Based Learning Intrusion Detection System: A New Approach
By : Anas Assiri. Introduction fraud detection Immune system Artificial immune system (AIS) AISFD Clonal selection.
Artificial Intelligence Center,
Subgoal: conduct an in-depth study of critical representation, operator and other choices used for evolutionary program repair at the source code level.
Mobile Agent Systems. Mobility Mobile Agents A Mobile Agent is a software agent that exists in a software Environment and can migrate from machine to.
Heterogeneous Technology Alliance Design of ultra-low-power µ-controler in near-threshold voltage.
CHAPTER 12 ADVANCED INTELLIGENT SYSTEMS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang.
Sensitive Data In a Wired World Negative Representations of Data Stephanie Forrest Dept. of Computer Science Univ. of New Mexico Albuquerque, NM
Introduction to MIMD architectures
16: Distributed Systems1 DISTRIBUTED SYSTEM STRUCTURES NETWORK OPERATING SYSTEMS The users are aware of the physical structure of the network. Each site.
Probabilistic Data Aggregation Ling Huang, Ben Zhao, Anthony Joseph Sahara Retreat January, 2004.
Introduction to Neural Network Justin Jansen December 9 th 2002.
Situation We now accept that grammar is not restricted to writing but is present in speech. Problem This can lead to assumptions that there is one kind.
Unit 3 & 4 Biology An Introduction.. VCE Biology Unit 1 Unity and Diversity –Cells in Action –Functioning Organisms Unit 2 Organisms and their Environment.
Anomaly Based Intrusion Detection System
Mobile Agents in Wireless Sensor Networks Ivan Vukasinovic Zoran Babovic Goran Rakocevic.
Operating Systems Distributed-System Structures. Topics –Network-Operating Systems –Distributed-Operating Systems –Remote Services –Robustness –Design.
1 BY: Nazanin Asadi Zohre Molaei Isfahan University of Technology.
Comments on Networking and Security - Challenges for Environmental Observatories Arthur C. Sanderson Rensselaer Polytechnic Institute NSF Workshop on Cyberinfrastructure.
Zhang Fu, Marina Papatriantafilou, Philippas Tsigas Chalmers University of Technology, Sweden 1 ACM SAC 2010 ACM SAC 2011.
Artificial Immune Systems: An Emerging Technology Dr. Jonathan Timmis Computing Laboratory University of Kent at Canterbury England. UK.
Domestic Nuclear Detection Office (DNDO) NITRD Workshop What are the Biggest Opportunities in Networking Problem? Sept. 20, 2012 Timothy Ashenfelter, PhD.
Enabling Self-management of Component-based High-performance Scientific Applications Hua (Maria) Liu and Manish Parashar The Applied Software Systems Laboratory.
Presentation on Clustering Paper: Cluster-based Scalable Network Services; Fox, Gribble et. al Internet Services Suman K. Grandhi Pratish Halady.
Improving Software Quality with Generic Autonomics Support Richard Anthony The University of Greenwich.
Network Operating Systems Users are aware of multiplicity of machines. Access to resources of various machines is done explicitly by: –Logging into the.
Shared Nothing Architecture Allen Archer. What is Shared Nothing architecture? It is a distributed architecture in which each node is independent and.
Emerging and Evolving Cyber Threats Require Sophisticated Response and Protection Capabilities Advanced Algorithms Cyber Attack Detection and Machine.
Lecture 11 Intrusion Detection (cont)
© 2017 SlidePlayer.com Inc. All rights reserved.