Network Intrusion Detection Using Random Forests

Feb 14, 2013

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PST2005. Jiong Zhang and Mohammad Zulkernine. 2. Outline. MotivationIntrusion detection systemData mining meets intrusion detectionProposed architectureChallenges and solutionsExperimental resultsConclusion and future work. PST2005. Jiong Zhang and Mohammad Zulkernine. 3. Motivation. Intrusion

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Network Intrusion Detection Using Random Forests

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Intrusion Detection using Honeypots

Intrusion Detection using Honeypots. Patrick Brannan Honeyd with virtual machines. What is a honeypot?. A closely monitored network decoy serving several purposes Distract adversaries from vulnerable machines Provide early warning (new attack &exploits)

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Random Forests

Random Forests. Paper presentation for CSI5388 PENGCHENG XI Mar. 23, 2005. Reference. Leo Breiman, Random Forests , Machine Learning, 45, 5-32, 2001 Leo Breiman (Professor Emeritus at UCB) is a member of the National Academy of Sciences. Abstract.

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Network Intrusion Detection and Mitigation

Network Intrusion Detection and Mitigation. Yan Chen Northwestern Lab for Internet and Security Technology (LIST) Department of Computer Science Northwestern University http://list.cs.northwestern.edu. Our Theme. Internet is becoming a new infrastructure for service delivery

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Kernel Mode Network Intrusion Detection

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Network Intrusion Detection Systems

Network Intrusion Detection Systems. Slides by: MM Clements A Adekunle The University of Greenwich. Intrusion Detection. Detection and protection from attacks against networks Three types of network attacks Reconnaissance Access Denial of service. Intrusion detection system (IDS).

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Network Intrusion Detection

Network Intrusion Detection. By Biju Varghese Siva Jambulingam Rohan Belani. Team Roles. Theory of Network Intrusion Detection Systems - Siva Problems in Network Intrusion Detection Systems - Biju Description of the Different Network Intrusion Detection Systems - Rohan. Principle.

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Network-Based Intrusion Detection

FORE SEC Academy Security Essentials (III). Network-Based Intrusion Detection. Need for Network-based Intrusion Detection. Most attacks come from the Internet Detecting these attacks allows a site to tune defenses If we correlate data from a large number of sources we increase our

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Reduce Instrumentation Predictors Using Random Forests

Reduce Instrumentation Predictors Using Random Forests. Presented By Bin Zhao Department of Computer Science University of Maryland May 3 2005. Motivation. Crash report – too late to collect program information until the program crashes

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Intrusion Detection & Network Forensics. Lucius L. Millinder Jr. security@secureitconsulting.us Chief Technology Officer Secure-IT Consulting, Inc. An ounce of prevention is worth a pound of detection. Why Talk about IDS?. Emerging new technology Very interesting ...but...

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Network Intrusion Detection System (NIDS)

Network Intrusion Detection System (NIDS). Somesh Jha. NIDS. Inspect packets at certain vantage points Behind the routers Look for malicious or anomalous behavior Much more fine-grained than firewalls Example: drop a packet whose payload “matches” a certain string. Classification of NIDS.

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Network Security and Intrusion Detection

Network Security and Intrusion Detection. Survey of the Art and Practice Dr. Michah Lerner AT&T Labs 15-August-2000. Outline. Model Principles Assumptions Methods Products. No silver bullets. Published sources only. Note: this talk describes some attack models.

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Intrusion Detection

Intrusion Detection. Cyber Security Spring 2005. Reading material. Chapter 25 from Computer Security, Matt Bishop Cisco Security Agent http://cisco.com/en/US/products/sw/secursw/ps5057/products_white_paper0900aecd8020f448.shtml Dark address space

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Network Intrusion Detection Systems

Network Intrusion Detection Systems. Ali Shayan October 2008. Introduction. Intrusion – an attempt to compromise or misuse a computer system or network.

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Network Intrusion Detection Using Random Forests

Network Intrusion Detection Using Random Forests. Jiong Zhang Mohammad Zulkernine School of Computing Queen's University Kingston, Ontario, Canada. Outline. Motivation Intrusion detection system Data mining meets intrusion detection Proposed architecture Challenges and solutions

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Layered Approach using Conditional Random Fields For Intrusion Detection

Layered Approach using Conditional Random Fields For Intrusion Detection. ABSTRACT:.

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Network Intrusion Detection System

Network Intrusion Detection System. NAIST. Omar ISMAIL. Internet Engineering Lab Graduate School of Information Science Nara Institute of Science and Technology Nara, Japan. 1. 20 th June 2003. Outline. NAIST. What is NIDS ?. Why it is important?. Snort. Deployment at AI3.

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Gnort : High Performance Network Intrusion Detection Using Graphics Processors

Gnort : High Performance Network Intrusion Detection Using Graphics Processors. Date:101/2/15 Publisher:ICS Author: Giorgos Vasiliadis , Spiros Antonatos , Michalis Polychronakis , Evangelos P. Markatos , and Sotiris Ioannidis Presenter : Shi- qu Yu. Introduction.

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Network Intrusion Detection Systems

Network Intrusion Detection Systems. MM Clements A Adekunle. Lecture Overview. Taxonomy of intrusion detection system Promiscuous & Inline Mode Protection: IDS, IPS IDS and IPS Deployment Considerations & example Cisco IDS family Snort IDS/IPS Vulnerabilities How to protect IDS?

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Intrusion Detection

Intrusion Detection. CS461/ECE422 Spring 2012. Reading Material. Chapter 8 of the text. Outline. What are intrusions? Who are intruders Host-based Intrusion Detection Network-based Intrusion Detection. What is an Intrusion?. Who are the Intruders. Hackers Possibly benign

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Intrusion Detection

Intrusion Detection. snort. Snort. Get snort Installation Configure Setup logs Rules Start. Get snort source. http://www.snort.org/ tar.gz Source http://www.snort.org/dl/current/snort-2.8.2.3.tar.gz Rpm's – Redhat Package Manager yum install snort Apt-get – Debian package manager

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Network Intrusion Detection Systems

Network Intrusion Detection Systems. Beyond packet filtering. Goal of NIDS. Detect attacks as they happen: Real-time monitoring of networks Provide information about attacks that have succeeded: Forensic analysis Passive systems: monitoring and reporting

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RANDOM FORESTS

RANDOM FORESTS. https://www.stat.berkeley.edu/~breiman/RandomForests/ https://web.csulb.edu/~tebert/teaching/lectures/551/random_forest.pdf http://www.math.usu.edu/adele/RandomForests/Ovronnaz.pdf https://www.princeton.edu/~aylinc/files/CS613-15-04.pdf. Ensemble methods.

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Network Intrusion Detection Using Random Forests

Network Intrusion Detection Using Random Forests

Presentation Transcript

Intrusion Detection using Honeypots

Random Forests

Network Intrusion Detection and Mitigation

Kernel Mode Network Intrusion Detection

Network Intrusion Detection Systems

Network Intrusion Detection

Network-Based Intrusion Detection

Reduce Instrumentation Predictors Using Random Forests

Intrusion Detection &amp; Network Forensics

Network Intrusion Detection System (NIDS)

Network Security and Intrusion Detection

Intrusion Detection

Network Intrusion Detection Systems

Network Intrusion Detection Using Random Forests

Layered Approach using Conditional Random Fields For Intrusion Detection

Network Intrusion Detection System

Gnort : High Performance Network Intrusion Detection Using Graphics Processors

Network Intrusion Detection Systems

Intrusion Detection

Intrusion Detection

Network Intrusion Detection Systems

RANDOM FORESTS

Intrusion Detection & Network Forensics