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Digital Forensics. Dr. Bhavani Thuraisingham The University of Texas at Dallas Network and Application Forensics October 8, 2010. Network Forensics. Network Forensics Network Attacks Security Measures Network Forensics and Tools Types of Networks Other info

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Digital forensics

Digital Forensics

Dr. Bhavani Thuraisingham

The University of Texas at Dallas

Network and Application Forensics

October 8, 2010


Network forensics

Network Forensics

  • Network Forensics

    • Network Attacks

    • Security Measures

    • Network Forensics and Tools

    • Types of Networks

    • Other info

  • Summary/Conclusion and Links

  • Special presentation of network forensic

  • http://www.infragard.net/library/congress_05/computer_forensics/network_primer.pdf


Network attacks

Network Attacks

  • Denial of service Denial of service attacks cause the service or program to cease functioning or prevent others from making use of the service or program.

  • These may be performed at the network layer by sending carefully crafted and malicious datagrams that cause network connections to fail.

  • They may also be performed at the application layer, where carefully crafted application commands are given to a program that cause it to become extremely busy or stop functioning.

  • Preventing suspicious network traffic from reaching hosts and preventing suspicious program commands and requests are the best ways of minimizing the risk of a denial of service attack.

  • It is useful to know the details of the attack method, so you should educate yourself about each new attack as it gets publicized.


Network attacks1

Network Attacks

  • Spoofing This type of attack causes a host or application to mimic the actions of another.

  • Typically the attacker pretends to be an innocent host by following IP addresses in network packets.

  • For example, a well-documented exploit of the BSD rlogin service can use this method to mimic a TCP connection from another host by guessing TCP sequence numbers.

  • To protect against this type of attack, verify the authenticity of datagrams and commands.

  • Prevent datagram routing with invalid source addresses. Introduce unpredictablility into connection control mechanisms, such as TCP sequence numbers and the allocation of dynamic port addresses.


Network attacks2

Network Attacks

  • Eavesdropping This is the simplest type of attack.

  • A host is configured to "listen" to and capture data not belonging to it. Carefully written eavesdropping programs can take usernames and passwords from user login network connections.

  • Broadcast networks like Ethernet are especially vulnerable to this type of attack.

  • To protect against this type of threat, avoid use of broadcast network technologies and enforce the use of data encryption.

  • IP firewalling is very useful in preventing or reducing unauthorized access, network layer denial of service, and IP spoofing attacks.

  • It not very useful in avoiding exploitation of weaknesses in network services or programs and eavesdropping.


Securing a network

Securing a Network

  • Need measures to secure a network and prevent breaches

  • Apply patches; User a layered network defense strategy

  • NSA (National Security Agency) ahs developed DiD Defense in Depth) and has three models of protection

    • People, Technology, Operations

    • People: Employees are trained well

    • Technology: Strong network architecture and testing tools

    • Operations: applying security patches, anti-virus software, etc.


Network security mechanisms

Network Security Mechanisms

  • Network security starts from authenticating any user, most likely a username and a password.

  • Once authenticated, a stateful firewall enforces access policies such as what services are allowed to be accessed by the network users

  • Though effective to prevent unauthorized access, this component fails to check potentially harmful contents such as computer worms being transmitted over the network.

  • An intrusion prevention system (IPS) helps detect and prevent such malware. IPS also monitors for suspicious network traffic for contents, volume and anomalies to protect the network from attacks such as denial of service.

  • Communication between two hosts using the network could be encrypted to maintain privacy.

  • Individual events occurring on the network could be tracked for audit purposes and for a later high level analysis.


Network security mechanisms1

Network Security Mechanisms

  • Honeypots, essentially decoy network-accessible resources, could be deployed in a network as surveillance and early-warning tools.

  • Techniques used by the attackers that attempt to compromise these decoy resources are studied during and after an attack to keep an eye on new exploitation techniques.

  • Such analysis could be used to further tighten security of the actual network being protected by the honeypot

  • Some tools: Firewall, Antivirus software and Internet Security Software. For authentication, use strong passwords and change it on a bi-weekly/monthly basis. When using a wireless connection, use a robust password. Network analyzer to monitor and analyze the network.


Network forensics1

Network Forensics

  • What is Network Forensics?

    • http://searchsecurity.techtarget.com/sDefinition/0,,sid14_gci859579,00.html

  • Network Forensics Analysis

  • Relationship to Honeynets/Honeypots

  • Policies for Networks Forensics

  • Example Prototype System

  • Some Popular Networks Forensics Analysis Tools (NFAT)


What is network forensics

What is Network Forensics

  • Network forensics is the process of capturing information that moves over a network and trying to make sense of it in some kind of forensics capacity.

    • Network forensics is the capture, recording, and analysis of network events in order to discover the source of security attacks or other problem incidents.

  • A network forensics appliance is a device that automates this process.

  • Wireless forensics is the process of capturing information that moves over a wireless network and trying to make sense of it in some kind of forensics capacity.


What is network forensics1

What is Network Forensics?

  • Network forensics systems can be one of two kinds:

    • "Catch-it-as-you-can" systems, in which all packets passing through a certain traffic point are captured and written to storage with analysis being done subsequently in batch mode. This approach requires large amounts of storage, usually involving a RAID system.

    • "Stop, look and listen" systems, in which each packet is analyzed in a rudimentary way in memory and only certain information saved for future analysis. This approach requires less storage but may require a faster processor to keep up with incoming traffic.


What is network forensics2

What is Network Forensics

  • Network Forensics is the process of collecting and analyzing raw network data and then tracking network traffic to determine how an attack took place

  • When intruders break into a network they leave a trail. Need to spot variations in network traffic; detect anomalies

  • Network forensics can usually help to determine whether network has been attacked or there is a user error

  • Examiners must establish standards procedures to carry out forensics


Network analysis

Network Analysis

  • Find analysis techniques developed for one type of network and apply it to another type of network

  • Types of networks

    • Computer and Communication Networks

    • Telecommunication Network

    • Transportation networks

      • Highways, Railroad, Air Traffic

    • Human networks

      • Terror networks, Relationship networks


Network forensics analysis tools nfat relationships between ids firewalls and nfat

Network Forensics Analysis Tools (NFAT): Relationships between IDS, Firewalls and NFAT

  • IDS attempts to detect activity that violates an organization’s security policy by implementing a set of rules describing preconfigures patterns of interest

  • Firewall allows or disallows traffic to or from specific networks, machine addresses and port numbers

  • NFAT synergizes with IDSs and Firewalls.

    • Preserves long term record of network traffic

    • Allows quick analysis of trouble spots identified by IDSs and Firewalls

  • NFATs must do the following:

    • Capture network traffic

    • Analyze network traffic according to user needs

    • Allow system users discover useful and interesting things about the analyzed traffic


Nfat tasks

NFAT Tasks

  • Traffic Capture

    • What is the policy?

    • What is the traffic of interest?

    • Intermal/Externasl?

    • Collect packets: tcpdump

  • Traffic Analysis

    • Sessionizing captured traffic (organize)

    • Protocol Parsing and analysis

      • Check for strings, use expert systems for analysis

  • Interacting with NFAT

    • Appropriate user interfaces, reports, examine large quantities of information and make it manageable


Network forensics networkminer

Network Forensics: NetworkMiner

  • NetworkMiner is a Network Forensic Analysis Tool (NFAT) for Windows.

  • NetworkMiner can be used as a passive network sniffer/packet capturing tool in order to detect operating systems, sessions, hostnames, open ports etc. without putting any traffic on the network.

  • The purpose of NetworkMiner is to collect data (such as forensic evidence) about hosts on the network rather than to collect data regarding the traffic on the network.

  • The main view is host centric (information grouped per host) rather than packet centric (information showed as a list of packets/frames).


Honeynets honeypots

Honeynets/Honeypots

  • Network Forensics and honeynet systems have the same features of collecting information about computer misuses

  • Honeynet system can lure attackers and gain information about new types of intrusions

  • Network forensics systems analyze and reconstruct he attack behaviors

  • These two systems integrated together build a active self learning and response system to profile the intrusion behavior features and investigate the original source of the attack.


Honeynet project

Honeynet project

  • Honeynet project was established to make information about network attacks and solutions widely available

  • Objectives: Awareness, information, tools

  • Attacks: distributed Denial of Service, Zero day attacks

  • Honeypot is a computer set up to lure attackers

  • Honeywalls are computers set up to monitor what is happening to the honeypots in the network


Policies computer attack taxonomy

Policies: Computer Attack Taxonomy

  • Probing

    • Attackers reconnaissance

    • Attackers create a profile of an organization's structure, network capabilities and content, security posture

    • Attacker finds the targets and devices plans to circumvent the security mechanism

  • Penetration

    • Exploit System Configuration errors and vulnerabilities

    • Install Trojans, record passwords, delete files, etc.

  • Cover tracks

    • Configure event logging to a previous state

    • Clear event logs and hide files


Policies to enhance forensics

Policies to enhance forensics

  • Retaining information

  • Planning the response

  • Training

  • Accelerating the investigation

  • Preventing anonymous activities

  • Protect the evidence


Example prototype system iowa state university

Example Prototype System: Iowa State University

  • Network Forensics Analysis mechanisms should meet the following:

    • Short response times; User friendly interfaces

  • Questions addresses

    • How likely is a specific host relevant to the attack? What is the role the host played in the attack? How strong are two hosts connected to the attack?

  • Features of the prototype

    • Preprocessing mechanism to reduce redundancy in intrusion alerts

    • Graph model for presenting and interacting with th3 evidence

    • Hierarchical reasoning framework for automated inference of attack group identification


Example prototype system modules

Example Prototype System: Modules

  • Evidence collection module

  • Evidence preprocessing module

  • Attack knowledge base

  • Assets knowledge base

  • Evidence graph generation module

  • Attack reasoning module

  • Analyst interface module

  • Reference

  • http://delivery.acm.org/10.1145/1420000/1410238/a4-wang.pdf?key1=1410238&key2=9838895521&coll=GUIDE&dl=GUIDE&CFID=57276464&CFTOKEN=77054716

  • https://www.dfrws.org/2005/proceedings/wang_evidencegraphs.pdf


Network tools

Network Tools

  • Network Forensics tools help in the monitoring of the network

  • Example: the records that Ps tools generate can prove that an employee ran a program without permission

  • Can also monitor machines/processes that may be harmful

  • Problem is the attacker can get administrator rights and start using the tools

  • Chapter 11 discusses tools for Windows and Linux


Some popular tools

Some Popular Tools

  • Raytheon’s SilentRunner

    • Gives administrators help as they attempt to protect their company’s assets

    • Collector, Analyzer and Visualize Modules

  • Sandstorm Enterprise’s NetIntercept

    • Hardware appliance focused on capturing network traffic

  • Niksun’s NetDetector

    • Its an appliance like NetIntercept

    • Has an alerting mechanism

    • Integrates with Cicso IDS for a complete forensic analysis


Network forensics open source tools

Network Forensics: Open Source Tools

  • Open source tools

    • Wireshark

    • Kismet

    • Snort

    • OSSEC

    • NetworkMiner is an open source Network Forensics Tool available at SourceForge.

    • Xplico is an Internet/IP Traffic Decoder (NFAT). Protocols supported: HTTP, SIP, FTP, IMAP, POP, SMTP, TCP, UDP, IPv4, IPv6


Network forensics commercial tools

Network Forensics: Commercial Tools

  • Deep Analysis Tools (data mining based tools)

    • E-Detective

    • ManTech International Corporation

    • Network Instruments

    • NIKSUN's NetDetector

    • PacketMotion

    • Sandstorm's NetIntercept

    • Mera Systems NetBeholder

    • InfoWatch Traffic Monitor


Network forensics commercial tools1

Network Forensics: Commercial Tools

  • Flow-Based Systems

    • Arbor Networks

    • GraniteEdge Networks

    • Lancope http://www.lancope.com/

    • Mazu Networks http://www.mazunetworks.com/

  • Hybrid Systems

    • These systems combine flow analysis, deep analysis, and security event monitoring and reporting.

    • Q1 Labs http://www.q1labs.com/


Performing live acquisitions

Performing Live Acquisitions

  • Insert bootable forensics CD in the suspect system

  • Keep a log of all the actions

  • Send collected information to a network drive

  • Copy the physical memory

  • Determine if root kit is present; access system’s firmware, - -

  • Get forensics hash value of all files


Performing live acquisitions windows

Performing Live Acquisitions: Windows

  • Setup NetCat listener to send the forensics data

  • Load Helix CD in the CD-ROM drive

  • Click appropriate buttons – System Information; Glad arrow etc

  • Click Acquire Live Image if Widows System

  • Connect to NetCat listener to send the collected data (e.g., enter IP address of NetCat listener)

  • Click Incidence Response Tools

  • Click on appropriate tools to collect data


Standard procedures

Standard procedures

  • Standard installation image, hash schemes (e.g., MD5, SHA-1)

  • Fix vulnerabilities if intrusion is detected

  • Retrieve volatile data (RAM, processes)

  • Acquire compromised drive and make forensics image of it

  • Compare forensics image and standard image and determine if anything has changed


Network logs

Network Logs

  • Network logs record traffic in and out of network

  • Network servers, routers, firewalls record activities and events that move through them

  • One ways is to run Tcpdump

  • When viewing network log, port information can give clues about suspicious activity

  • Use network analysis tool


Packet sniffers

Packet Sniffers

  • Devices or software to monitor (sniff) traffic

  • TCP/IP sniffers operate at the Packet level; in OSI operates at the Layer 2 or 3 level (e.g. Data link or Network layers)

  • Some sniffers perform packet captures, some perform analysis and some perform both

  • Tools exist for examining (i) packets with certain flags set (ii) email headers (iii) IRC chats


Summary

Summary

  • Network Forensics is the process of collecting and analyzing raw network data and then tracking network traffic to determine how an attack took place

  • Layered defense strategies to the network architecture

  • Live acquisitions are needed to retrieve volatile items

  • Standard procedure are needed to establish how to proceed after a network attack occurs

  • By monitoring network traffic can establish normal operations; then determine if there is an anomaly

  • Network tools used to monitor networks; but intruders can get admin rights to attack from the inside

  • Tools are available for monitoring network traffic for both Windows and Linux systems

  • Honeynet project enables people to learn latest intrusion techniques


Links

Links

  • https://www.dfrws.org/2005/proceedings/wang_evidencegraphs.pdf

  • http://www.cs.fsu.edu/~yasinsac/Papers/MY01.pdf

  • http://www.sandstorm.net/support/netintercept/downloads/ni-ieee.pdf

  • http://www.giac.org/certified_professionals/practicals/gsec/2478.php

  • http://www.infragard.net/library/congress_05/computer_forensics/network_primer.pdf

  • http://dfrws.org/2003/presentations/Brief-Casey.pdf

  • http://delivery.acm.org/10.1145/1070000/1066749/p302-ren.pdf?key1=1066749&key2=0512850911&coll=GUIDE&dl=GUIDE&CFID=36223233&CFTOKEN=49225512

  • http://dfrws.org/


Application forensics

Application Forensics

  • Email Forensics

    • UTD work on Email worm detection - revisited

    • Mobile System Forensics

    • Note: Other Application/systems related forensics

      • Database forensics, Network forensics (already discussed)

  • Military Forensics Overview

  • Optional paper to read:

    • http://www.mindswap.org/papers/Trust.pdf


Email forensics

Email Forensics

  • Email Investigations

  • Client/Server roles

  • Email crimes and violations

  • Email servers

  • Email forensics tools


Email investigations

Email Investigations

  • Types of email investigations

    • Emails have worms and viruses – suspicious emails

    • Checking emails in a crime – homicide

  • Types of suspicious emails

    • Phishing emails i- they are in HTML format and redirect to suspicious web sites

    • Nigerian scam

    • Spoofing emails


Client server roles

Client/Server Roles

  • Client-Server architecture

  • Email servers runs the email server programs – example Microsoft Exchange Server

  • Email runs the client program – example Outlook

  • Identitication/authntictaion is used for client to access the server

  • Intranet/Internet email servers

    • Intranet – local environment

    • Internet – public: example: yahoo, hotmail etc.


Email crimes and violations

Email Crimes and Violations

  • Goal is to determine who is behind the crime such as who sent the email

  • Steps to email forensics

    • Examine email message

    • Copy email message – also forward email

    • View and examine email header: tools available for outlook and other email clients

    • Examine additional files such as address books

    • Trace the message using various Internet tools

    • Examine network logs (netflow analysis)

      • Note: UTD Netflow tools SCRUB are in SourceForge


Email servers

Email Servers

  • Need to work with the network administrator on how to retrieve messages from the server

  • Understand how the server records and handles the messages

  • How are the email logs created and stored

  • How are deleted email messages handled by the server? Are copies of the messages still kept?

  • Chapter 12 discussed email servers by UNIX, Microsoft, Novell


Email forensics tools

Email Forensics Tools

  • Several tools for Outlook Express, Eudora Exchange, Lotus notes

  • Tools for log analysis, recovering deleted emails,

  • Examples:

    • AccessData FTK

    • FINALeMAIL

    • EDBXtract

    • MailRecovery


Worm detection introduction

Worm Detection: Introduction

  • What are worms?

    • Self-replicating program; Exploits software vulnerability on a victim; Remotely infects other victims

  • Evil worms

    • Severe effect; Code Red epidemic cost $2.6 Billion

  • Goals of worm detection

    • Real-time detection

  • Issues

    • Substantial Volume of Identical Traffic, Random Probing

  • Methods for worm detection

    • Count number of sources/destinations; Count number of failed connection attempts

  • Worm Types

    • Email worms, Instant Messaging worms, Internet worms, IRC worms, File-sharing Networks worms

  • Automatic signature generation possible

    • EarlyBird System (S. Singh -UCSD); Autograph (H. Ah-Kim - CMU)


Email worm detection using data mining

Email Worm Detection using Data Mining

  • Task:

    • given some training instances of both “normal” and “viral” emails,

    • induce a hypothesis to detect “viral” emails.

  • We used:

    • Naïve Bayes

    • SVM

Outgoing Emails

The Model

Test data

Feature extraction

Classifier

Machine Learning

Training data

Cleanor Infected ?


Assumptions

Assumptions

  • Features are based on outgoing emails.

  • Different users have different “normal” behaviour.

  • Analysis should be per-user basis.

  • Two groups of features

    • Per email (#of attachments, HTML in body, text/binary attachments)

    • Per window (mean words in body, variable words in subject)

  • Total of 24 features identified

  • Goal: Identify “normal” and “viral” emails based on these features


Feature sets

Feature sets

  • Per email features

    • Binary valued Features

      • Presence of HTML; script tags/attributes; embedded images; hyperlinks;

      • Presence of binary, text attachments; MIME types of file attachments

    • Continuous-valued Features

      • Number of attachments; Number of words/characters in the subject and body

  • Per window features

    • Number of emails sent; Number of unique email recipients; Number of unique sender addresses; Average number of words/characters per subject, body; average word length:; Variance in number of words/characters per subject, body; Variance in word length

    • Ratio of emails with attachments


Data mining approach

Data Mining Approach

Classifier

Clean/ Infected

Test instance

Clean/ Infected

infected?

SVM

Naïve Bayes

Test instance

Clean?

Clean


Data set

Data set

  • Collected from UC Berkeley.

    • Contains instances for both normal and viral emails.

  • Six worm types:

    • bagle.f, bubbleboy, mydoom.m,

    • mydoom.u, netsky.d, sobig.f

  • Originally Six sets of data:

    • training instances: normal (400) + five worms (5x200)

    • testing instances: normal (1200) + the sixth worm (200)

  • Problem: Not balanced, no cross validation reported

  • Solution: re-arrange the data and apply cross-validation


Our implementation and analysis

Our Implementation and Analysis

  • Implementation

    • Naïve Bayes: Assume “Normal” distribution of numeric and real data; smoothing applied

    • SVM: with the parameter settings: one-class SVM with the radial basis function using “gamma” = 0.015 and “nu” = 0.1.

  • Analysis

    • NB alone performs better than other techniques

    • SVM alone also performs better if parameters are set correctly

    • mydoom.m and VBS.Bubbleboy data set are not sufficient (very low detection accuracy in all classifiers)

    • The feature-based approach seems to be useful only when we have

      • identified the relevant features

      • gathered enough training data

      • Implement classifiers with best parameter settings


Mobile device system forensics

Mobile Device/System Forensics

  • Mobile device forensics overview

  • Acquisition procedures

  • Summary


Mobile device forensics overview

Mobile Device Forensics Overview

  • What is stored in cell phones

    • Incoming/outgoing/missed calls

    • Text messages

    • Short messages

    • Instant messaging logs

    • Web pages

    • Pictures

    • Calendars

    • Address books

    • Music files

    • Voice records


Mobile phones

Mobile Phones

  • Multiple generations

    • Analog, Digital personal communications, Third generations (increased bandwidth and other features)

  • Digital networks

    • CDMA, GSM, TDMA, - - -

  • Proprietary OSs

  • SIM Cards (Subscriber Identity Module)

    • Identifies the subscriber to the network

    • Stores personal information, addresses books, etc.

  • PDAs (Personal digital assistant)

    • Combines mobile phone and laptop technologies


Acquisition procedures

Acquisition procedures

  • Mobile devices have volatile memory, so need to retrieve RAM before losing power

  • Isolate device from incoming signals

    • Store the device in a special bag

    • Need to carry out forensics in a special lab (e.g., SAIAL)

  • Examine the following

    • Internal memory, SIM card, other external memory cards, System server, also may need information from service provider to determine location of the person who made the call


Mobile forensics tools

Mobile Forensics Tools

  • Reads SIM Card files

  • Analyze file content (text messages etc.)

  • Recovers deleted messages

  • Manages PIN codes

  • Generates reports

  • Archives files with MD5, SHA-1 hash values

  • Exports data to files

  • Supports international character sets


Information warfare

Information Warfare

  • Information Warfare

    • Defensive Strategies for Government and Industry

    • Military Tactics

    • Terrorism and Information Warfare

    • Tactics of Private Corporations

    • Future IW strategies

    • Surveillance Tools

    • The Victims of Information Warfare

  • Military Forensics

  • Relevant Papers


What is information warfare

What is Information Warfare?

  • Information warfare is the use and management of information in pursuit of a competitive advantage over an opponent. Information warfare may involve collection of tactical information, assurance that one's own information is valid, spreading of propaganda or disinformation to demoralize the enemy and the public, undermining the quality of opposing force information and denial of information collection opportunities to opposing forces.

  • http://en.wikipedia.org/wiki/Information_warfare


Defensive strategies for government and industry

Defensive Strategies for Government and Industry

  • Are US and Foreign governments prepared for Information Warfare

    • According to John Vacca, US will be most affected with 60% of the world’s computing power

    • Stealing sensitive information as well as critical, information to cripple an economy (e.g., financial information)

  • What have industry groups done

    • IT-SAC: Information Technology Information Sharing and Analysis

  • Will strategic diplomacy help with Information Warfare?

  • Educating the end user is critical according to John Vacca


Defensive strategies for government and industry1

Defensive Strategies for Government and Industry

  • What are International organizations?

    • Think Tanks and Research agencies

    • Book cites several countries from Belarus to Taiwan engaged in Economic Espionage and Information Warfare

  • Risk-based analysis

  • Military alliances

    • Coalition forces – US, UK, Canada, Australia have regular meetings on Information Warfare

  • Legal implications

  • Strong parallels between National Security and Cyber Security


Military tactics

Military Tactics

  • Supporting Technologies

    • Agents, XML, Human Computer Interaction

  • Military tactics

    • Planning, Security, Intelligence

  • Tools

    • Offensive Ruinous IW tools

      • Launching massive distributed denial of service attacks

    • Offensive Containment IW tools

      • Operations security, Military deception, Psychological operations, Electronic warfare (use electromagnetic energy), Targeting: Disable enemy's C2 (c0mmand and control) system and capability


Military tactics1

Military Tactics

  • Tools (continued)

    • Defensive Preventive IW Tools

      • Monitor networks

    • Defensive Ruinous IW tools

      • Information operations

    • Defensive Responsive Containment IW tools

      • Handle hacking, viruses.

  • Other aspects

    • Dealing with sustained terrorist IW tactics, Dealing with random terrorist IW tactics


Terrorism and information warfare

Terrorism and Information Warfare

  • Terrorists are using the web to carry out terrorism activities

  • What are the profiles of terrorists? Are they computer literate?

  • Hacker controlled tanks, planes and warships

  • Is there a Cyber underground network?

  • What are their tools?

    • Information weapons, HERF gun (high power radio energy at an electronic target), Electromagnetic pulse. Electric power disruptive technologies

  • Why are they hard to track down?

    • Need super forensics tools


Tactics of private corporations

Tactics of Private Corporations

  • Defensive tactics

    • Open course intelligence, Gather business intelligence

  • Offensive tactics

    • Packet sniffing, Trojan horse etc.

  • Prevention tactics

    • Security techniques such as encryption

  • Survival tactics

    • Forensics tools


Future iw tactics

Future IW Tactics

  • Electromagnetic bomb

    • Technology, targeting and delivery

  • Improved conventional method

    • Virus, worms, trap doors, Trojan horse

  • Global positioning systems

  • Nanotechnology developments

    • Nano bombs


Surveillance tools

Surveillance Tools

  • Data emanating from sensors:

    • Video data, surveillance data

    • Data has to be analyzed

    • Monitoring suspicious events

  • Data mining

    • Determining events/activities that are abnormal

  • Biometrics technologies

  • Privacy is a concern


Victims of information warfare

Victims of Information Warfare

  • Loss of money and funds

  • Loss of shelter, food and water

  • Spread of disease

  • Identity theft

  • Privacy violations

  • Death and destruction

  • Note: Computers can be hacked to loose money and identity; computers can be used to commit a crime resulting in death and destruction


Military forensics

Military Forensics

  • CFX-2000: Computer Forencis Experiment 2000

    • Information Directorate (AFRL) partnership with NIJ/NLECTC

    • Hypothesis: possible to determine the motives, intent, targets, sophistication, identity and location of cyber terrorists by deploying an integrated forensics analysis framework

    • Tools included commercial products and research prototypes

    • http://www.afrlhorizons.com/Briefs/June01/IF0016.html

    • http://rand.org/pubs/monograph_reports/MR1349/MR1349.appb.pdf


Digital forensics1

Digital Forensics

Dr. Bhavani Thuraisingham

The University of Texas at Dallas

Appendix

Social Network Analysis and Forensics

October 8, 2010


Social network analysis of 9 11 terrorists www orgnet com

Social Network Analysis of 9/11 Terrorists (www.orgnet.com)

Early in 2000, the CIA was informed of two terrorist suspects linked to al-Qaeda.

Nawaf Alhazmi and Khalid Almihdhar were photographed attending a meeting of

known terrorists in Malaysia. After the meeting they returned to Los Angeles,

where they had

already set up residence in late 1999.


Social network analysis of 9 11 terrorists

Social Network Analysis of 9/11 Terrorists

  • What do you do with these suspects? Arrest or deport them immediately? No, we need to use them to discover more of the al-Qaeda network.

  • Once suspects have been discovered, we can use their daily activities to uncloak their network. Just like they used our technology against us, we can use their planning process against them. Watch them, and listen to their conversations to see...

  • who they call / email

  • who visits with them locally and in other cities

  • where their money comes from

  • The structure of their extended network begins to emerge as data is discovered via surveillance.


Social network analysis of 9 11 terrorists1

Social Network Analysis of 9/11 Terrorists

A suspect being monitored may have many contacts -- both accidental and intentional. We must always be wary of 'guilt by association'. Accidental contacts, like the mail delivery person, the grocery store clerk, and neighbor may not be viewed with investigative interest.

Intentional contacts are like the late afternoon visitor, whose car license plate is traced back to a rental company at the airport, where we discover he arrived from Toronto (got to notify the Canadians) and his name matches a cell phone number (with a Buffalo, NY area code) that our suspect calls regularly. This intentional contact is added to our map and we start tracking his interactions -- where do they lead? As data comes in, a picture of the terrorist organization slowly comes into focus.

How do investigators know whether they are on to something big? Often they don't. Yet in this case there was another strong clue that Alhazmi and Almihdhar were up to no good -- the attack on the USS Cole in October of 2000. One of the chief suspects in the Cole bombing [Khallad] was also present [along with Alhazmi and Almihdhar] at the terrorist meeting in Malaysia in January 2000.

Once we have their direct links, the next step is to find their indirect ties -- the 'connections of their connections'. Discovering the nodes and links within two steps of the suspects usually starts to reveal much about their network. Key individuals in the local network begin to stand out. In viewing the network map in Figure 2, most of us will focus on Mohammed Atta because we now know his history. The investigator uncloaking this network would not be aware of Atta's eventual importance. At this point he is just another node to be investigated.


Social network analysis of 9 11 terrorists2

Figure 2 shows the two suspects and

Social Network Analysis of 9/11 Terrorists


Social network analysis of 9 11 terrorists3

Social Network Analysis of 9/11 Terrorists


Social network analysis of 9 11 terrorists4

Social Network Analysis of 9/11 Terrorists

  • We now have enough data for two key conclusions:

  • All 19 hijackers were within 2 steps of the two original suspects uncovered in 2000!

  • Social network metrics reveal Mohammed Atta emerging as the local leader

  • With hindsight, we have now mapped enough of the 9-11 conspiracy to stop it. Again, the investigators are never sure they have uncovered enough information while they are in the process of uncloaking the covert organization. They also have to contend with superfluous data. This data was gathered after the event, so the investigators knew exactly what to look for. Before an event it is not so easy.

  • As the network structure emerges, a key dynamic that needs to be closely monitored is the activity within the network. Network activity spikes when a planned event approaches. Is there an increase of flow across known links? Are new links rapidly emerging between known nodes? Are money flows suddenly going in the opposite direction? When activity reaches a certain pattern and threshold, it is time to stop monitoring the network, and time to start removing nodes.

  • The author argues that this bottom-up approach of uncloaking a network is more effective than a top down search for the terrorist needle in the public haystack -- and it is less invasive of the general population, resulting in far fewer "false positives".


Social network analysis of steroid usage in baseball www orgnet com

Figure 2 shows the two suspects and

Social Network Analysis of Steroid Usage in Baseball (www.orgnet.com)

When the Mitchell Report on steroid use in Major League Baseball [MLB], was published, people were surprised at who and how many players were mentioned. The diagram below shows a human network created from data found in the Mitchell Report. Baseball players are shown as green nodes. Those who were found to be providers of steroids and other illegal performance enhancing substances appear as red nodes. The links reveal the flow of chemicals -- from provider to player.


Applying to network forensics

Applying to Network Forensics

  • Start with infected machines

  • Then follow the chain to other machines

  • Visualization techniques for the network of affected machines

  • Iowa State University Prototype is an example


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