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Chapter 9

Intruders

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Chapter 9 - Intruders

• significant issue for networked systems is hostile or unwanted access

• either via network or local• can identify classes of intruders:

– masquerader– misfeasor– clandestine user

• varying levels of competence

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Intruders

• clearly a growing publicized problem– from “Wily Hacker” in 1986/87– to clearly escalating CERT stats

• may seem benign, but still cost resources

• may use compromised system to launch other attacks

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Levels of hackers

• Sophisticated, with a thorough knowledge of the technology, supplying the cracking programs;

• “foot solders” using the supplied programs and willing to spend time cracking a system.

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The Stages of a Network Intrusion

1. Scan the network to:• locate which IP addresses are in use, • what operating system is in use, • what TCP or UDP ports are “open” (being listened to by Servers).

2. Run “Exploit” scripts against open ports3. Get access to Shell program which is “suid” (has

“root” privileges).4. Download from Hacker Web site special versions of

systems files that will let Cracker have free access in the future without his cpu time or disk storage space being noticed by auditing programs.

5. Use IRC (Internet Relay Chat) to invite friends to the feast.

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Intrusion Techniques

• aim to increase privileges on system• basic attack methodology

– target acquisition and information gathering – initial access – privilege escalation – covering tracks

• key goal often is to acquire passwords• so then exercise access rights of owner

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Password Guessing• one of the most common attacks• attacker knows a login (from email/web page etc) • then attempts to guess password for it

– try default passwords shipped with systems– try all short passwords– then try by searching dictionaries of common words– intelligent searches try passwords associated with the user

(variations on names, birthday, phone, common words/interests)

– before exhaustively searching all possible passwords

• check by login attempt or against stolen password file • success depends on password chosen by user• surveys show many users choose poorly

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Password Capture

• another attack involves password capture – watching over shoulder as password is entered – using a trojan horse program to collect– monitoring an insecure network login (eg. telnet,

FTP, web, email) – extracting recorded info after successful login

(web history/cache, last number dialed etc)

• using valid login/password can impersonate user

• users need to be educated to use suitable precautions/countermeasures

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Intrusion Detection

• inevitably will have security failures• so need also to detect intrusions so can

– block if detected quickly– act as deterrent– collect info to improve security

• assume intruder will behave differently to a legitimate user– but will have imperfect distinction between

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Intrusion Detection

• The intruder can be identified and ejected from the system.

• An effective intrusion detection can prevent intrusions.

• Intrusion detection enables the collection of information about intrusion techniques that can be used to strengthen the intrusion prevention facility.

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Profiles of Behavior of Intruders and Authorized

Users

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Measures used for Intrusion Detection

• Login frequency by day and time.• Frequency of login at different locations.• Time since last login.• Password failures at login.• Execution frequency.• Execution denials.• Read, write, create, delete frequency.• Failure count for read, write, create and

delete.

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Audit records

• Fundamental tool for intrusion detection - record of ongoing activity used as input to an intrusion detection system,

• Two kinds : - native audit records used normally by an accounting software and collecting information on user activity - have to be filtered and may not be complete; - detection specific audit records containing only information required by the the intrusion detection system - have to be especially generated.

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Audit Record Analysis

• foundation of statistical approaches• analyze records to get metrics over time

– counter, gauge, interval timer, resource use

• use various tests on these to determine if current behavior is acceptable– mean & standard deviation, multivariate,

markov process, time series, operational

• key advantage is no prior knowledge used

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Statistical Anomaly Detection

• threshold detection– count occurrences of specific event over time– if exceed reasonable value assume intrusion– alone is a crude & ineffective detector

• profile based– characterize past behavior of users– detect significant deviations from this– profile usually multi-parameter, e.g. counter,

gauge, interval timer, resource utilization– Different tets can performed, e.g. mean and

standard deviation, multivariate, Markov process

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Rule-Based Intrusion Detection

• observe events on system & apply rules to decide if activity is suspicious or not

• rule-based anomaly detection– analyze historical audit records to identify

usage patterns & auto-generate rules for them

– then observe current behavior & match against rules to see if conforms

– like statistical anomaly detection does not require prior knowledge of security flaws

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Rule-Based Intrusion Detection

• rule-based penetration identification– uses expert systems technology– with rules identifying known penetration,

weakness patterns, or suspicious behavior– rules usually machine & O/S specific– rules are generated by experts who interview &

codify knowledge of security admins– quality depends on how well this is done– compare audit records or states against rules

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Base-Rate Fallacy

• practically an intrusion detection system needs to detect a substantial percentage of intrusions with few false alarms– if too few intrusions detected -> false security– if too many false alarms -> ignore / waste time

• this is very hard to do• existing systems seem not to have a good

record

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Distributed Intrusion Detection

• traditional focus is on single systems• but typically have networked systems• more effective defense has these

working together to detect intrusions• issues

– dealing with varying audit record formats– integrity & confidentiality of networked data– centralized or decentralized architecture

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Distributed Intrusion Detection - Architecture

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Distributed Intrusion Detection – Agent Implementation

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Template driven logic analysis

• Notable events – failed file accesses, accessing system files, changing file access control;

• Signatures – known attack patterns;

• Noteworthy sessions - anomalous behavior with respect to the number of programs executed, number of files accessed

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Honeypots

• decoy systems to lure attackers– away from accessing critical systems– to collect information of their activities– to encourage attacker to stay on system so

administrator can respond

• are filled with fabricated information• instrumented to collect detailed

information on attackers activities• may be single or multiple networked

systems

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Password Management

• front-line defense against intruders• users supply both:

– login – determines privileges of that user– password – to identify them

• passwords often stored encrypted– Unix uses multiple DES (variant with salt)– more recent systems use crypto hash

function

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Managing Passwords• need policies and good user education • ensure every account has a default password • ensure users change the default passwords to

something they can remember • protect password file from general access• set technical policies to enforce good

passwords – minimum length (>6) – require a mix of upper & lower case letters,

numbers, punctuation – block known dictionary words

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Managing Passwords

• may reactively run password guessing tools – note that good dictionaries exist for almost any

language/interest group

• may enforce periodic changing of passwords • have system monitor failed login attempts, &

lockout account if see too many in a short period

• do need to educate users and get support • balance requirements with user acceptance • be aware of social engineering attacks

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Password protection

• System maintain a file that associates a password with each authorized user.

• Password file can be protected with:– One-way encryption– Access Control

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Storing UNIX Passwords

• UNIX passwords were kept in in a publicly readable file, etc/passwords.

• Now they are kept in a “shadow” directory and only visible by “root”.

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UNIX Password Scheme

Loading a new password

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”Salt”

• The salt serves three purposes:– Prevents duplicate passwords.– Effectively increases the length of the

password.– Prevents the use of hardware

implementations of DES

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UNIX Password Scheme

Verifying a password file

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Threats to password scheme

• By using a guest account or other means to access a machine and then running a password guessing program on that machine,

• obtaining a copy of the password file, and then running the password cracking program on another machine.

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Password Selecting Strategies

• User education• Computer-generated passwords• Reactive password checking• Proactive password checking

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Proactive Password Checking

• most promising approach to improving password security

• allow users to select own password• but have system verify it is acceptable

– simple rule enforcement – compile a large dictionary of “bad” passwords and

check every new password against them - require space (was e.g 30 MB) and time

• use algorithmics (markov model or bloom filter) to detect poor choices

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Markov Model

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Transition Matrix

1. Determine the frequency matrix f, where f(i,j,k) is the number of occurrences of the trigram consisting of the ith, jth and kth character.

2. For each bigram ij, calculate f(i,j, ) as the total number of trigrams beginning with ij.

3. Compute the entries of T as follows:

),,(),,(

),,( jifkjif

kjiT

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Spafford (Bloom Filter)

where

10;1;1)( NyDjkiyXH ii

dictionarypasswordinwordofnumberD

dictionarypasswordinwordjthX i

The following procedure is then applied to the dictionary:

1. A hash table of N bits is defined, with all bits initially set to 0.

2. For each password, its k hash values are calculated, and the responding bits in the hash table are set to 1

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Spafford (Bloom Filter)• Design the hash scheme to minimize

false positive.• Probability of false positive:

)()(,/

)1ln(

,,

)1()1(

/1

//

wordssizedictionarytobitssizetablehashofratioDNR

dictionaryinwordsofnumberD

tablehashinbitsofnumberN

functionhashofnumberk

where

P

kR

lyequivalentor

eeP

k

kRkkNkD

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Performance of Bloom Filter

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Advantages of Bloom filter

• Much less space required by the hash table in comparison with the full dictionary.

• Simple calculation of the hash functions independent of the size of the dictionary,

• No need for search of the dictionary.