Immune-inspired Network Intrusion Detection System (i-NIDS)

Slides:

Advertisements

Similar presentations

V-Detector: A Negative Selection Algorithm Zhou Ji, advised by Prof. Dasgupta Computer Science Research Day The University of Memphis March 25, 2005.

Advertisements

CIFD: Computational Immunology for Fraud Detection

School of Cybernetics, School of Systems Engineering, University of Reading Presentation Skills Workshop March 22, ‘11 Diagnosis of Breast Cancer by Modular.

SIDD: A Framework for Detecting Sensitive Data Exfiltration by an Insider Attack 42 nd Hawaii International Conference on System Sciences, Electrical.

1 Evolvable Malware Sadia Noreen, Sahafq Murtaza, M. Zubair Shafiq, Muddassar Farooq National University of Computer and Emerging Sciences (FAST-NUCES)

Evolving Neural Networks in Classification Sunghwan Sohn.

Learning Classifier Systems to Intrusion Detection Monu Bambroo 12/01/03.

CS 1 – Introduction to Computer Science Introduction to the wonderful world of Dr. T Dr. Daniel Tauritz.

Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 7: Expert Systems and Artificial Intelligence Decision Support.

Strategic Directions in Real- Time & Embedded Systems Aatash Patel 18 th September, 2001.

CS 1 – Introduction to Computer Science Introduction to the wonderful world of Dr. T Dr. Daniel Tauritz.

Statistical Relational Learning Pedro Domingos Dept. Computer Science & Eng. University of Washington.

USENIX LISA ‘99 Conference © Copyright 1999, Martin Roesch Snort - Lightweight Intrusion Detection for Networks Martin Roesch.

By : Anas Assiri.  Introduction  fraud detection  Immune system  Artificial immune system (AIS)  AISFD  Clonal selection.

Muhammad Shahzad 1, Saira Zahid 1, Syed Ali Khayam 1,2, Muddassar Farooq 1 1 Next Generation Intelligent Networks Research Center National University of.

Revision Michael J. Watts

Artificial Intelligence Lecture No. 28 Dr. Asad Ali Safi Assistant Professor, Department of Computer Science, COMSATS Institute of Information Technology.

Case Based Reasoning Approach to Intrusion Detection Date: 3/14/2005 Dr. Seong-Moo Yoo Information Assurance Engineering Lab Electrical and Computer Engineering.

Department of Information Technology Indian Institute of Information Technology and Management Gwalior AASF hIQ 1 st Nov ‘09 Department of Information.

Grant Dick Department of Information Science, School of Business, University of Otago, Dunedin, NZ 21/11/2009Australasian.

CSI Evolutionary Computation Fall Semester, 2009.

Intrusion Detection Using Hybrid Neural Networks Vishal Sevani ( )

Mehdi Ghayoumi MSB rm 132 Ofc hr: Thur, a Machine Learning.

DoWitcher: Effective Worm Detection and Containment in the Internet Core S. Ranjan et. al in INFOCOM 2007 Presented by: Sailesh Kumar.

RECENT DEVELOPMENTS OF INDUCTION MOTOR DRIVES FAULT DIAGNOSIS USING AI TECHNIQUES 1 Oly Paz.

An Overview of Intrusion Detection Using Soft Computing Archana Sapkota Palden Lama CS591 Fall 2009.

Chapter 5: Implementing Intrusion Prevention

Biologically Inspired Defenses against Computer Viruses International Joint Conference on Artificial Intelligence 95’ J.O. Kephart et al.

1 5. Application Examples 5.1. Programmable compensation for analog circuits (Optimal tuning) 5.2. Programmable delays in high-speed digital circuits (Clock.

Design, Control and Fault Diagnosis of Industrial Drives Department of Electrical and Computer Engineering University of Tehran August 25, 2004 Control.

5. delavnica AVN (algoritmi po vzorih iz narave) Ljubljana, 20. april 2006.

Immune-inspired Network Intrusion Detection System (i-NIDS) 1 Next Generation Intelligent Networks Research Center National University of Computer & Emerging.

Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Evolving Reactive NPCs for the Real-Time Simulation Game.

Antivirus software.

Snort - Lightweight Intrusion Detection for Networks YOUNG Wo Sang Program Committee, PISA

Pac-Man AI using GA. Why Machine Learning in Video Games? Better player experience Agents can adapt to player Increased variety of agent behaviors Ever-changing.

Evolving, Adaptable Visual Processing System Simon Fung-Kee-Fung.

Anomaly Detection. Network Intrusion Detection Techniques. Ştefan-Iulian Handra Dept. of Computer Science Polytechnic University of Timișoara June 2010.

The Utilization of Artificial Intelligence in a Hybrid Intrusion Detection System Authors ： Martin Botha, Rossouw von Solms, Kent Perry, Edwin Loubser.

Chapter 1. Introduction in Creating Brain-like intelligence, Sendhoff et al. Course: Robots Learning from Humans Bae, Eun-bit Otology Laboratory Seoul.

October 20-23rd, 2015 Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features Joshua Saxe, Dr. Konstantin Berlin Invincea.

McAfee Support | McAfee Helpline Number

mcafee technical support number 1 (800) Mcafee customer service

Snort – IDS / IPS.

Professor Tzong-Chen Wu

Machine Learning for Computer Security

Evolvable Malware Sadia Noreen, Sahafq Murtaza, M. Zubair Shafiq, Muddassar Farooq National University of Computer and Emerging Sciences (FAST-NUCES) Next.

Norton Antivirus Installation, Activation & Malware Support

Human-Competitive Drum Track Generation in NEAT Drummer

A lustrum of malware network communication: Evolution & insights

Enterprise Botnet Detection and Mitigation System

Rationality and Power: the “gap in the middle” in ICT

Soft Computing Introduction.

Sistem Kecerdasan Buatan

Restricted Boltzmann Machines for Classification

Bioagents and Biorobots David Kadleček, Michal Petrus, Pavel Nahodil

Introduction to Soft Computing

Behavior based User Authentication on Smart Phones

An Incremental Self-Improvement Hybrid Intrusion Detection System Mahbod Tavallaee, Wei Lu, and Ali A. Ghorbani Faculty of Computer Science, UNB Fredericton.

George Rush Modified by Dr. T

THE INTERNET MOTION SENSOR: A Distributed Blackhole Monitoring System

Remah Alshinina and Khaled Elleithy DISCRIMINATOR NETWORK

George Rush Modified by Dr. T

Sampling based Mission Planning for Multiple Robots

Intrusion Detection system

World-Leading Research with Real-World Impact!

Adversarial Learning for Security System

Research Direction Introduction

Introduction to Internet Worm

Introduction to Neural Networks

Presentation transcript:

Immune-inspired Network Intrusion Detection System (i-NIDS) GECCO HUMIES - 2008 Immune-inspired Network Intrusion Detection System (i-NIDS) M. Zubair Shafiq1, Syed Ali Khayam2, Muddassar Farooq1 1 Next Generation Intelligent Networks Research Center National University of Computer & Emerging Sciences Islamabad, Pakistan http://www.nexginrc.org 2 School of Electrical Engineering & Computer Sciences National University of Sciences & Technology Rawalpindi, Pakistan http://wisnet.niit.edu.pk

Human^ machine competitive Introduction Simple Human competitive Human^ machine competitive

Unfortunately, most computer viruses are not so courteous!

Threat numbers show the story of what’s happening?

These are Commercial Software… Norton AV Command AV McAfee AV Chernobyl-1.4 Not detected F0sf0r0 Hare Z0mbie-6.b 468% increase in malware attacks from 2006-2007 Signature matching! Size of signature database cannot scale! Inability to detect zero-day (novel) attacks!

Motivation for current work A self-healing, self-defending and living artificial immune system Proactive defense against zero-day attacks Mapping concepts from A-life and evolution

Immune inspired Network Intrusion Detection System Alarm Output Adaptive Immune System/ Innate Immune System Negative Selection Dendritic Cell Algorithm Intelligent Statistical Features Memory of Markov Chain Multi resolution session rate Entropy of IP address Divergence of port distribution Network Traffic Stream

Human^machine Competitive Results Detector TP rate (%) FP rate (%) [Classical Bio-inspired Detector] Naïve RVNS 53.5 7.9 Naïve DCA 61.6 5.8 [State-of-the-art Statistical Detector] Rate Limiting 84.4 1.4 Maximum Entropy 83.1 4.2 [Immune inspired NIDS] i-RVNS 94.9 0.2 i-DCA 94.6 0.1

Engineered System Patent pending Complete version will be ready in 1 year time; free download US$200,000 grant to develop the final product from the National ICT R&D fund, Government of Pakistan

Why the best? In a nutshell… 1. Hard problem in hard domain; impossible for a human to solve 2. Evolved system better than human developed, commercial anti-virus software 3. Evolved system better than state-of-the-art statistical malware detectors 4. Hybrid of statistical-immune detectors; best of both worlds 5. Engineered product; open-source initiative

Publications (BEST PAPER NOMINATION) A Comparative Study of Fuzzy Inference Systems, Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Portscan Detection M. Zubair Shafiq, Muddassar Farooq and Syed Ali Khayam In M. Giacobini et al.(Eds.), Proceedings of Applications of Evolutionary Computing, EvoWorkshops 2007 (EuroGP-EvoCoMnet), Volume 4974 of Lecture Notes in Computer Science, pp. 48–57, Springer Verlag, Napoli, Italy, March,2008. (BEST PAPER NOMINATION) Improving the Accuracy of Immune-inspired Malware Detectors by using Intelligent Features M. Zubair Shafiq, Syed Ali Khayam and Muddassar Farooq In Genetic and Evolutionary Conference (GECCO), July, 2008, Atlanta, USA.

Unt ze Dream vill finally kome True!