Calculation Algorithm for Network Flow Parameters Entropy in Anomaly Detection



Similar documents
Development of a Network Intrusion Detection System

ADRISYA: A FLOW BASED ANOMALY DETECTION SYSTEM FOR SLOW AND FAST SCAN

Intrusion Detection & SNORT. Fakrul Alam fakrul@bdhbu.com

FUZZY DATA MINING AND GENETIC ALGORITHMS APPLIED TO INTRUSION DETECTION

Configuring Personal Firewalls and Understanding IDS. Securing Networks Chapter 3 Part 2 of 4 CA M S Mehta, FCA

Question 1. [7 points] Consider the following scenario and assume host H s routing table is the one given below:

Distributed Denial of Service (DDoS)

Network Monitoring On Large Networks. Yao Chuan Han (TWCERT/CC)

Introduction to Network Discovery and Identity

This Lecture. The Internet and Sockets. The Start If everyone just sends a small packet of data, they can all use the line at the same.

Hillstone T-Series Intelligent Next-Generation Firewall Whitepaper: Abnormal Behavior Analysis

Chapter 9 Firewalls and Intrusion Prevention Systems

Conclusions and Future Directions

CSCI 4250/6250 Fall 2015 Computer and Networks Security

A TWO LEVEL ARCHITECTURE USING CONSENSUS METHOD FOR GLOBAL DECISION MAKING AGAINST DDoS ATTACKS

Are Second Generation Firewalls Good for Industrial Control Systems?

System Specification. Author: CMU Team

A Systemfor Scanning Traffic Detection in 3G WCDMA Network

AUTONOMOUS NETWORK SECURITY FOR DETECTION OF NETWORK ATTACKS

A Small-time Scale Netflow-based Anomaly Traffic Detecting Method Using MapReduce

A Review of Anomaly Detection Techniques in Network Intrusion Detection System

Appendix A: Configuring Firewalls for a VPN Server Running Windows Server 2003

CS 356 Lecture 19 and 20 Firewalls and Intrusion Prevention. Spring 2013

Security threats and network. Software firewall. Hardware firewall. Firewalls

Lehrstuhl für Informatik 4 Kommunikation und verteilte Systeme. Firewall

IDS Categories. Sensor Types Host-based (HIDS) sensors collect data from hosts for

Slow Port Scanning Detection

Lab Configure Intrusion Prevention on the PIX Security Appliance

CTS2134 Introduction to Networking. Module Network Security

A LITERATURE REVIEW OF NETWORK MONITORING THROUGH VISUALISATION AND THE INETVIS TOOL

Intrusion Detection Systems and Supporting Tools. Ian Welch NWEN 405 Week 12

Intrusion Detection Systems with Correlation Capabilities

Monitoring sítí pomocí NetFlow dat od paketů ke strategiím

BotHunter: Detecting Malware Infection Through IDS-Driven Dialog Correlation

Detecting Anomalies in Network Traffic Using Maximum Entropy Estimation

Application of Data Mining Techniques in Intrusion Detection

Firewalls, Tunnels, and Network Intrusion Detection. Firewalls

CS Computer and Network Security: Intrusion Detection

Intrusion Detection System in Campus Network: SNORT the most powerful Open Source Network Security Tool

Introducing IBM s Advanced Threat Protection Platform

Firewalls, Tunnels, and Network Intrusion Detection

MINDS: A NEW APPROACH TO THE INFORMATION SECURITY PROCESS

Implementing Large-Scale Autonomic Server Monitoring Using Process Query Systems. Christopher Roblee Vincent Berk George Cybenko

NetFlow use cases. ICmyNet / NetVizura. Miloš Zeković, milos.zekovic@soneco.rs. ICmyNet Chief Customer Officer Soneco d.o.o.

An Efficient and Reliable DDoS Attack Detection Using a Fast Entropy Computation Method

IBM Security IBM Corporation IBM Corporation

Keywords Attack model, DDoS, Host Scan, Port Scan

How To Protect A Network From Attack From A Hacker (Hbss)

Overview of Network Security The need for network security Desirable security properties Common vulnerabilities Security policy designs

Intrusion Detection Systems

A Biologically Inspired Approach to Network Vulnerability Identification

Flow Based Traffic Analysis

Joint Entropy Analysis Model for DDoS Attack Detection

Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms

Virtual Private Networks

Intrusion Log Sharing University of Wisconsin-Madison

CIS 4361: Applied Security Lab 4

Introducing FortiDDoS. Mar, 2013

Detecting Network Anomalies. Anant Shah

Time has something to tell us about Network Address Translation

Classic IOS Firewall using CBACs Cisco and/or its affiliates. All rights reserved. 1

The Integration of SNORT with K-Means Clustering Algorithm to Detect New Attack

Analysis of SIP Traffic Behavior with NetFlow-based Statistical Information

Lab Objectives & Turn In

IDS / IPS. James E. Thiel S.W.A.T.

Banking Security using Honeypot

Introduction... Error! Bookmark not defined. Intrusion detection & prevention principles... Error! Bookmark not defined.

How To - Configure Virtual Host using FQDN How To Configure Virtual Host using FQDN

CIT 380: Securing Computer Systems

Assets, Groups & Networks

Intrusion Forecasting Framework for Early Warning System against Cyber Attack

An Open Source IPS. IIT Network Security Project Project Team: Mike Smith, Sean Durkin, Kaebin Tan

Two State Intrusion Detection System Against DDos Attack in Wireless Network

A Frequency-Based Approach to Intrusion Detection

Cisco IPS Tuning Overview

Detecting Flooding Attacks Using Power Divergence

On A Network Forensics Model For Information Security

Methods for Firewall Policy Detection and Prevention

Supporting Document Mandatory Technical Document. Evaluation Activities for Stateful Traffic Filter Firewalls cpp. February Version 1.

Signal Processing Methods for Denial of Service Attack Detection

CSC574 - Computer and Network Security Module: Intrusion Detection

Network Based Intrusion Detection Using Honey pot Deception

Security in IPv6. Basic Security Requirements and Techniques. Confidentiality. Integrity

How To Prevent DoS and DDoS Attacks using Cyberoam

Adaptive Network Intrusion Detection System using a Hybrid Approach

Edge Configuration Series Reporting Overview

Transcription:

Calculation Algorithm for Network Flow Parameters Entropy in Anomaly Detection Theory, practice, applications Oleg Gudkov, BMSTU IT Security for the Next Generation International Round, Delft University of Technology 11-13 May, 2012 The Netherlands

Intrusion Detection Brief review of the intrusion detection methods

Signature based method This method can detect only known attacks Do well PAGE 3

Selected network traffic parameters deviation discovering relative to defined normal values

Network traffic representation Network traffic representation PAGE 5

Scanning process Problem PAGE 6

Scanning process Problem There is network packets sequence PAGE 7

Scanning process Which packets can be regarded as scanning? PAGE 8

Scanning process And in this case? PAGE 9

Scanning process And in this? PAGE 10

Network traffic parameters Network traffic parameters which used to detect anomalies: Intencity One should define threshold of network packets per every single period of time Probability Hypothesys of network traffic distribution law are examinated Entropy Information entropy of different network packets parameters is been analyzed. PAGE 11

Entropy alteration while scanning Representative example of destination IP address entropy alteration while scanning with nmap. nmap -v scanme.org -T3 Parameter «-T3» is used to reduce intencity of scanning. So there is both scanning and "normal" packets in the marked area. Entropy alteration example while scanning with the nmap packet number PAGE 12

Entropy calculation in practice Theory and practice

Theory Entropy calculation in practice Entropy according to Shennon is: Where p i - probability of member a i of the set A={a 1, a 2,, a m }. In this case during anomaly detection process: There exist m different values of the network packet selected parameter. Each network packet in the sequence has probability of occurance. Entropy calculates according to formula above. PAGE 14

Network packets parameters selection Shape Parameter value Source IP:192.168.0.1 Source IP: 38.117.98.208 Source IP: 8.8.8.8 PAGE 15

Network packets parameters selection Shape Parameter value Destination port: 53 Destination port: 21 Destination port: 137 PAGE 16

Network packets parameter selection Shape Parameter value 192.168.0.1:52 38.117.98.208:21 8.8.8.8:137 PAGE 17

Network packets parameters selection Entropy calculation practice Network packets parameters used for analyze entropy Network addresses (source and destination) Ports Another network packets fields and combinations. PAGE 18

Main tasks Entropy calculation practice For each network packet 1. Calculate entropy PAGE 19

Main tasks Entropy calculation practice For each network packet 1. Calculate entropy 2. It is necessary to calculate it FAST!!! PAGE 20

Entropy calculation PAGE 21

Entropy calculation p i =? PAGE 22

Entropy calculation We should use probability estimation PAGE 23

Entropy calculation Select calculation window: W={ω 1, ω 2,,ω N } N elements PAGE 24

Entropy calculation N elements PAGE 25

Entropy calculation N elements PAGE 26

Sequential entropy calculation H 2 H 1 There should every time calculate an entropy value for each window PAGE 27

Entropies difference calculation Proposal: we should calculate entropy difference not entropy for each window H N = H 2 - H 1 H 2 H 1 PAGE 28

Entropies difference calculation Proposal: we should calculate entropies difference W 2 W 1 Hatched and non-hatched letters - probabilities of selected parameters value for windows W 1 and W 2 respectively PAGE 29

Reduce amount of calculations Notice: only elements in and out affect the difference in amount of elements of every type in every following windows. out W 2 W 1 in PAGE 30

Preliminary data for algorithm Entropy calculation practice Array of elements - values of selected network packet parameter Hash-array, maps number of elements for each parameter value Value Number of elements 3 3 6 PAGE 31

Algorithm first step Entropy calculation practice 1. Fill elements with the initial values 2. Fill hash-array with the number of elements from the initial values Value Number of elements 3 3 6 3. Calculate entropy H 0 for the initial values PAGE 32

Main algorithm step Entropy calculation practice 1. For every new parameter value calculate entropies difference 2. Calculate entropy value H i+1 N=H i N+ H N 3. Update hash-array Value Number of elements 3 4 5 PAGE 33

Algorithm rate comparison Entropy calculation practice Comparison for the same data of 22501 packets. Time in second obtained with time utility. Window size (number of packets) Entropies difference algorithm Sequential entropy calculation algorithm 10 0.046 s. 0.122 s. 50 0.050 s. 0.480 s. 100 0.046 s. 0.915 s. 500 0.051 s. 3.644 s. 1000 0.046 s. 6.738 s. PAGE 34

Thank You Oleg Gudkov, BMSTU IT Security for the Next Generation International Round, Delft University of Technology 11-13 May, 2012 The Netherlands