Chapter 22 in “Introduction to Computer Security”

Intrusion DetectionIntrusion Detection

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Chapter 22: Intrusion DetectionChapter 22: Intrusion Detection

Principles

Basics

Models of Intrusion Detection

Architecture of an IDS

Organization

Incident Response

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Lecture 1

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IntrusionIntrusion

An intrusion is a deliberate unauthorized attempt, successful or not, to break into, access, manipulate, or misuse some valuable property and where the misuse may result into or render the property unreliable or unusable.

TypesAttempted break-ins

Masquerade attacks

Penetrations of the security control system

Leakage

Denial of service

Malicious use

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The System Intrusion ProcessThe System Intrusion Process

ReconnaissanceGather information about the target system and the details of its working and weak points.

Vulnerability assessment is part of intrusion process.

Physical IntrusionEnter an organization network masquerading as legitimate users, including administrative privileges, remote access privileges.

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The System Intrusion ProcessThe System Intrusion Process

Denial of ServiceDoS attacks are where the intruder attempts to crash a service or the machine, overload network links, overload the CPU, or fill up the disk. Ping-of-Death, sends an invalid fragment, which starts before the end of packet, but extends past the end of the packet.SYN flood, sends a huge number of TCP SYN packets to let victim wait. Land/Latierra, sends a forged SYN packet with identical source/destination address/port so that the system goes into an infinite loop trying to complete the TCP connection.

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The Dangers of System IntrusionsThe Dangers of System Intrusions

Loss of personal data that may be stored on a computer. The victim may not notice the loss of digital information.

Compromised privacy. A lot of individual data is kept on individuals by organizations, i.e., bank, mortgage company.

Legal Liability. Hack may use your computer to break into other systems in two or three level hacking.

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22.1 Principles of Intrusion Detection22.1 Principles of Intrusion Detection

Characteristics of systems not under attack1. User, process actions conform to statistically predictable pattern

2. User, process actions do not include sequences of actions that subvert the security policy

3. Process actions correspond to a set of specifications describing what the processes are allowed to do

Systems under attack do not meet at least one of these

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ExampleExample

Goal: insert a back door into a systemIntruder will modify system configuration file or programRequires privilege; attacker enters system as an unprivileged user and must acquire privilege

Nonprivileged user may not normally acquire privilege (violates #1)Attacker may break in using sequence of commands that violate security policy (violates #2)Attacker may cause program to act in ways that violate program’s specification (violates #3)

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22.2 Basic Intrusion Detection22.2 Basic Intrusion Detection

Attack tool is automated script designed to violate a security policyExample: rootkit, (http://en.wikipedia.org/wiki/Rootkit)

Includes password snifferDesigned to hide itself using Trojaned versions of various programs (ps, ls, find, netstat, etc.)Adds back doors (login, telnetd, etc.)Has tools to clean up log entries (zapper, etc.)

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DetectionDetection

Rootkit configuration files cause ls, du, etc. to hide information

ls lists all files in a directoryExcept those hidden by configuration file

dirdump (local program to list directory entries) lists them too

Run both and compare countsIf they differ, ls is doctored

Other approaches possible

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Key PointKey Point

Rootkit does not alter kernel or file structures to conceal files, processes, and network connections

It alters the programs or system calls that interpret those structures

Find some entry point for interpretation that rootkit did not alter

The inconsistency is an anomaly (violates #1)

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Denning’s ModelDenning’s Model

Hypothesis: exploiting vulnerabilities requires abnormal use of normal commands or instructions

Includes deviation from usual actions

Includes execution of actions leading to break-ins

Includes actions inconsistent with specifications of privileged programs

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Goals of IDSGoals of IDS

1. Detect wide variety of intrusionsPreviously known and unknown attacksSuggests need to learn/adapt to new attacks or changes in behavior

2. Detect intrusions in timely fashionMay need to be be real-time, especially when system responds to intrusion

Problem: analyzing commands may impact response time of system

May suffice to report intrusion occurred a few minutes or hours ago

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Goals of IDSGoals of IDS

3. Present analysis in simple, easy-to-understand formatIdeally a binary indicatorUsually more complex, allowing analyst to examine suspected attackUser interface critical, especially when monitoring many systems

4. Be accurateMinimize false positives, false negativesMinimize time spent verifying attacks, looking for them

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22.3 Models of Intrusion Detection22.3 Models of Intrusion Detection

An intrusion detection system (IDS) is a system used to detect unauthorized intrusions into computer systems and networks. Anomaly detection (behavior-based detection)

What is usual, is knownWhat is unusual, is badChallenges? Solutions?

Misuse detection (signature-based detection)What is bad, is knownWhat is not bad, is goodTypical misuses: unauthorized access, unauthorized modification, denial of service

Specification-based detectionWhat is good, is knownWhat is not good, is bad

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22.3.1 Anomaly Detection22.3.1 Anomaly Detection

Anomaly based systems are “learning” systems in a sense that they work by continuously creating “norms” of activities. Anomaly detection compares observed activity against expected normal usage profiles “learned”.Assumption: all intrusive activities are necessarily anomalous. Any activity on the system is checked against “normal” profiles, is a deemed acceptable or not not based on the presence of such activity in the profile database.

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22.3.1 Anomaly Detection22.3.1 Anomaly Detection

Individual profile is a collection of common activities a user is expected to do and with little deviation from the expected form. Usage time, login time. Group profile covers a group of users with a common work pattern, resource requests and usage, and historic activities. Resource profile includes the monitoring of the user patterns of the system resources such as applications, accounts, storage media, protocols, communication ports. Other profiles. For instance, executable profile.

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22.3.1 Anomaly Detection22.3.1 Anomaly Detection

1. Threshold metrics

2. Statistical moments

3. Markov model

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Threshold MetricsThreshold Metrics

Counts number of events that occurBetween m and n events (inclusive) expected to occur

If number falls outside this range, anomalous

ExampleWindows: lock user out after k failed sequential login attempts. Range is (0, k–1).

k or more failed logins deemed anomalous

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DifficultiesDifficulties

Appropriate threshold may depend on non-obvious factors

Typing skill of users

If keyboards are US keyboards, and most users are French, typing errors very common

Dvorak vs. non-Dvorak within the UShttp://en.wikipedia.org/wiki/Dvorak_Simplified_Keyboard

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Statistical MomentsStatistical Moments

Analyzer computes standard deviation (first two moments), other measures of correlation (higher moments)

If measured values fall outside expected interval for particular moments, anomalous

sum = x1 + x2 + .. + xnSumsquqares =

A new observation xn+1 is defined to be abnormal if it fallls outside a confidence interval that is d standard deviations from the mena for some parameter d: mean + d * stdev,

22

1... nxx

))1/(( 2meannsumsquaressqrtstdev

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Markov ModelMarkov Model

Past state affects current transition

Anomalies based upon sequences of events, and not on occurrence of single event

Problem: need to train system to establish valid sequencesUse known, training data that is not anomalous

The more training data, the better the model

Training data should cover all possible normal uses of system

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Example: TIMExample: TIM

Time-based Inductive LearningSequence of events is abcdedeabcabcTIM derives following rules:

R1: abc (1.0) R2: cd (0.5) R3: ce (0.5)

R4: de (1.0) R5: ea (0.5) R6: ed (0.5)

Seen: abd; triggers alertc always follows ab in rule set

Seen: acf; no alert as multiple events can follow cMay add rule R7: cf (0.33); adjust R2, R3

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Potential Problems of Anomaly DetectionPotential Problems of Anomaly Detection

False Positive: Anomaly activities that are not intrusive are classified as intrusive.

False Negative: Intrusive activities that are not anomalous result in false negatives, that is events are not flagged intrusive, though they actually are.

Computational expensive because of the overhead of keeping track of, and possibly updating several system profile metrics.

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22.3.2 Misuse Modeling22.3.2 Misuse Modeling

Determines whether a sequence of instructions being executed is known to violate the site security policy

Descriptions of known or potential exploits grouped into rule setsIDS matches data against rule sets; on success, potential attack found

Cannot detect attacks unknown to developers of rule setsNo rules to cover them

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Example: Network Flight Recorder (NFR)Example: Network Flight Recorder (NFR)

Built to make adding new rules easilyArchitecture:

Packet sucker: read packets from networkDecision engine: uses filters to extract informationBackend: write data generated by filters to disk

Query backend allows administrators to extract raw, post-processed data from this fileQuery backend is separate from NFR process

Strength: clean design and adaptability to the need of the usersWeakness: one must know what to look for

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N-Code LanguageN-Code Language

Users can write their own filters using N-code languageExample: ignore all traffic not intended for 2 web servers:# list of my web serversmy_web_servers = [ 10.237.100.189 10.237.55.93 ] ;# we assume all HTTP traffic is on port 80filter watch tcp ( client, dport:80 ){

if (ip.dest != my_web_servers)return;

# now process the packet; we just write out packet inforecord system.time, ip.src, ip.dest to www._list;

}www_list = recorder(“log”)

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22.3.3 Specification Modeling22.3.3 Specification Modeling

Determines whether execution of sequence of instructions violates specificationOnly need to check programs that alter protection state of system

System traces, or sequences of events t1, … ti, ti+1, …, are basis of this

Event ti occurs at time C(ti)Events in a system trace are totally ordered

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ExamplesExamples

Subject S composed of processes p, q, r, with traces Tp, Tq, Tr has Ts = TpTq Tr

Filtering function: apply to system traceOn process, program, host, user as 4-tuple

< ANY, emacs, ANY, bishop >lists events with program “emacs”, user “bishop”

< ANY, ANY, nobhill, ANY >list events on host “nobhill”

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Example: Apply to Example: Apply to rdistrdist

Ko, Levitt, Ruschitzka defined PE-grammar to describe accepted behavior of program

rdist creates temp file, copies contents into it, changes protection mask, owner of it, copies it into place

Attack: during copy, delete temp file and place symbolic link with same name as temp file

rdist changes mode, ownership to that of program

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Comparison and ContrastComparison and Contrast

Misuse detection: if all policy rules known, easy to construct rule sets to detect violations

Usual case is that much of policy is unspecified, so rule sets describe attacks, and are not complete

Anomaly detection: detects unusual events, but these are not necessarily security problemsSpecification-based vs. misuse: spec assumes if specifications followed, policy not violated; misuse assumes if policy as embodied in rule sets followed, policy not violated

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22.4 IDS Architecture22.4 IDS Architecture

Basically, a sophisticated audit systemAgent like logger; it gathers data for analysis

Director like analyzer; it analyzes data obtained from the agents according to its internal rules

Notifier obtains results from director, and takes some actionMay simply notify security officer

May reconfigure agents, director to alter collection, analysis methods

May activate response mechanism

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22.4.1 Agents22.4.1 Agents

Obtains information and sends to director

May put information into another formPreprocessing of records to extract relevant parts

May delete unneeded information

Director may request agent send other information

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ExampleExample

IDS uses failed login attempts in its analysisAgent scans login log every 5 minutes, sends director for each new login attempt:

Time of failed loginAccount name and entered password

Director requests all records of login (failed or not) for particular user

Suspecting a brute-force cracking attempt

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Host-Based AgentHost-Based Agent

Obtain information from logsMay use many logs as sources

May be security-related or not

May be virtual logs if agent is part of the kernel

Agent generates its informationScans information needed by IDS, turns it into equivalent of log record

Typically, check policy; may be very complex

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Network-Based AgentsNetwork-Based Agents

Detects network-oriented attacksDenial of service attack introduced by flooding a network

Monitor traffic for a large number of hostsExamine the contents of the traffic itselfAgent must have same view of traffic as destination

TTL tricks, fragmentation may obscure this

End-to-end encryption defeats content monitoringNot traffic analysis, though

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Network IssuesNetwork Issues

Network architecture dictates agent placementEthernet or broadcast medium: one agent per subnet

Point-to-point medium: one agent per connection, or agent at distribution/routing point

Focus is usually on intruders entering networkIf few entry points, place network agents behind them

Does not help if inside attacks to be monitored

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Aggregation of InformationAggregation of Information

Agents produce information at multiple layers of abstraction

Application-monitoring agents provide one view (usually one line) of an eventSystem-monitoring agents provide a different view (usually many lines) of an eventNetwork-monitoring agents provide yet another view (involving many network packets) of an event

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22.4.2 Director22.4.2 Director

Reduces information from agentsEliminates unnecessary, redundant records

Analyzes remaining information to determine if attack under way

Analysis engine can use a number of techniques, discussed before, to do this

Usually run on separate systemDoes not impact performance of monitored systems

Rules, profiles not available to ordinary users

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ExampleExample

Jane logs in to perform system maintenance during the dayShe logs in at night to write reportsOne night she begins recompiling the kernelAgent #1 reports logins and logoutsAgent #2 reports commands executed

Neither agent spots discrepancyDirector correlates log, spots it at once

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Adaptive DirectorsAdaptive Directors

Modify profiles, rule sets to adapt their analysis to changes in system

Usually use machine learning or planning to determine how to do this

Example: use neural nets to analyze logsNetwork adapted to users’ behavior over timeUsed learning techniques to improve classification of events as anomalous

Reduced number of false alarms

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22.4.3 Notifier22.4.3 Notifier

Accepts information from director

Takes appropriate actionNotify system security officer

Respond to attack

Often GUIsWell-designed ones use visualization to convey information

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GrIDS GUIGrIDS GUI

A E

D

C

B

GrIDS interface showing the progress of a worm as it spreads through network

Left is early in spread

Right is later on

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Other ExamplesOther Examples

Courtney detected SATAN attacksAdded notification to system logCould be configured to send email or paging message to system administrator

IDIP protocol coordinates IDSes to respond to attack

If an IDS detects attack over a network, notifies other IDSes on co-operative firewalls; they can then reject messages from the source

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Types of Intrusion Detection SystemsTypes of Intrusion Detection Systems

Network-Based Intrusion Detection SystemsHave the whole network as the monitoring scope, and monitor the traffic on the network to detect intrusions.

Can be run as an independent standalone machine where it promiscuously watches over all network traffic,

Or just monitor itself as the target machine to watch over its own traffic. (SYN-flood or a TCP port scan)

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Advantage of NIDSAdvantage of NIDS

Ability to detect attacks that a host-based system would miss because NIDSs monitor network traffic at a transport layer.

Difficulty to remove evidence compared with HIDSs.

Real-time detection and response. Real time notification allows for a quick and appropriate response.

Ability to detect unsuccessful attacks and malicious intent.

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Disadvantages of NIDSDisadvantages of NIDS

Blind spots. Deployed at the border of an organization network, NIDS are blink to the whole inside network.

Encrypted data. NIDSs have no capabilities to decrypt encrypted data.

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Host-based Intrusion Detection Systems (HIDS)Host-based Intrusion Detection Systems (HIDS)

Misuse is not confined only to the “bad” outsiders but within organizations.

Local inspection of systems is called HIDS to detect malicious activities on a single computer.

Monitor operating system specific logs including system, event, and security logs on Windows systems and syslog in Unix environments to monitor sudden changes in these logs.

They can be put on a remote host.

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Advantages of HIDSAdvantages of HIDS

Ability to verify success or failure of an attack quickly because they log continuing events that have actually occurred, have less false positive than their cousins.

Low level monitoring. Can see low-level activities such as file accesses, changes to file permissions, attempts to install new executables or attempts to access privileged services, etc.

Almost real-time detection and response.

Ability to deal with encrypted and switched environment.

Cost effectiveness. No additional hardware is needed to install HIDS.

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Disadvantages of HIDSDisadvantages of HIDS

Myopic viewpoint. Since they are deployed at a host, they have a very limited view of the network.

Since they are close to users, they are more susceptible to illegal tempering.

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22.5 Organization of an IDS22.5 Organization of an IDS

Monitoring network traffic for intrusionsNSM system

Combining host and network monitoringDIDS

Making the agents autonomous, distributing the director among multiple systems to enhance security and reliability

AAFID system

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Lecture 2

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22.5.1 Monitoring Networks: NSM22.5.1 Monitoring Networks: NSM

Develops profile of expected usage of network, compares current usageHas 3-D matrix for data

Axes are source, destination, serviceEach connection has unique connection IDContents are number of packets sent over that connection for a period of time, and sum of dataNSM generates expected connection dataExpected data masks data in matrix, and anything left over is reported as an anomaly

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ProblemProblem

Too much data!Solution: arrange data hierarchically into groups

Construct by folding axes of matrix

Analyst could expand any group flagged as anomalous

(S1, D1, SMTP)(S1, D1, FTP)

…

(S1, D1)

(S1, D2, SMTP)(S1, D2, FTP)

…

(S1, D2)

S1

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SignaturesSignatures

Analyst can write rule to look for specific occurrences in matrix

Repeated telnet connections lasting only as long as set-up indicates failed login attempt

Analyst can write rules to match against network traffic

Used to look for excessive logins, attempt to communicate with non-existent host, single host communicating with 15 or more hosts

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OtherOther

Graphical interface independent of the NSM matrix analyzerDetected many attacks

But false positives too

Still in use in some placesSignatures have changed, of course

Also demonstrated intrusion detection on network is feasibleDid no content analysis, so would work even with encrypted connections

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22.5.2 Combining Sources: DIDS22.5.2 Combining Sources: DIDS

Neither network-based nor host-based monitoring sufficient to detect some attacks

Attacker tries to telnet into system several times using different account names: network-based IDS detects this, but not host-based monitorAttacker tries to log into system using an account without password: host-based IDS detects this, but not network-based monitor

DIDS uses agents on hosts being monitored, and a network monitor

DIDS director uses expert system to analyze data

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Attackers Moving in NetworkAttackers Moving in Network

Intruder breaks into system A as alice

Intruder goes from A to system B, and breaks into B’s account bob

Host-based mechanisms cannot correlate these

DIDS director could see bob logged in over alice’s connection; expert system infers they are the same user

Assigns network identification number NID to this user

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Handling Distributed DataHandling Distributed Data

Agent analyzes logs to extract entries of interestAgent uses signatures to look for attacks

Summaries sent to director

Other events forwarded directly to director

DIDS model has agents report:Events (information in log entries)Action, domain

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Actions and DomainsActions and Domains

Subjects perform actionssession_start, session_end, read, write, execute, terminate, create, delete, move, change_rights, change_user_id

Domains characterize objectstagged, authentication, audit, network, system, sys_info, user_info, utility, owned, not_ownedObjects put into highest domain to which it belongs

Tagged, authenticated file is in domain taggedUnowned network object is in domain network

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More on Agent ActionsMore on Agent Actions

Entities can be subjects in one view, objects in anotherProcess: subject when changes protection mode of object, object when process is terminated

Table determines which events sent to DIDS director

Based on actions, domains associated with event

All NIDS events sent over so director can track view of systemAction is session_start or execute; domain is network

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Layers of Expert System ModelLayers of Expert System Model

1. Log records2. Events (relevant information from log entries)3. Subject capturing all events associated with a user; NID

assigned to this subject4. Contextual information such as time, proximity to other

eventsSequence of commands to show who is using the systemSeries of failed logins follow

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Top LayersTop Layers

5. Network threats (combination of events in context)Abuse (change to protection state)Misuse (violates policy, does not change state)Suspicious act (does not violate policy, but of interest)

6. Score (represents security state of network)Derived from previous layer and from scores associated with rules

Analyst can adjust these scores as needed

A convenience for user

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22.5.3 Autonomous Agents: AAFID22.5.3 Autonomous Agents: AAFID

Distribute director among agents

Autonomous agent is process that can act independently of the system of which it is part

Autonomous agent performs one particular monitoring function

Has its own internal model

Communicates with other agents

Agents jointly decide if these constitute a reportable intrusion

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AdvantagesAdvantages

No single point of failureAll agents can act as directorIn effect, director distributed over all agents

Compromise of one agent does not affect othersAgent monitors one resource

Small and simple

Agents can migrate if neededApproach appears to be scalable to large networks

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DisadvantagesDisadvantages

Communications overhead higher, more scattered than for single director

Securing these can be very hard and expensiveAs agent monitors one resource, need many agents to monitor multiple resourcesDistributed computation involved in detecting intrusions

This computation also must be secured

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Example: AAFIDExample: AAFID

Host has set of agents and transceiverTransceiver controls agent execution, collates information, forwards it to monitor (on local or remote system)

Filters provide access to monitored resourcesUse this approach to avoid duplication of work and system dependence

Agents subscribe to filters by specifying records needed

Multiple agents may subscribe to single filter

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Transceivers and MonitorsTransceivers and Monitors

Transceivers collect data from agentsForward it to other agents or monitorsCan terminate, start agents on local system

Example: System begins to accept TCP connections, so transceiver turns on agent to monitor SMTP

Monitors accept data from transceiversCan communicate with transceivers, other monitors

Send commands to transceiver

Perform high level correlation for multiple hostsIf multiple monitors interact with transceiver, AAFID must ensure transceiver receives consistent commands

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22.6 Intrusion Response22.6 Intrusion Response

Once a intrusion is detected, how can the system be protected. Goal:

Minimize the damage of attackThwart intrusionAttempt to repair damages

PhasesIncident PreventionIntrusion Handling

Containment PhaseEradication PhaseFollow-Up phase

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22.6.1 Incident Prevention22.6.1 Incident Prevention

Identify attack before it completes, ideally

Prevent it from completing

Jails useful for thisAttacker placed in a confined environment that looks like a full, unrestricted environment

Attacker may download files, but gets bogus ones

Can imitate a slow system, or an unreliable one

Useful to figure out what attacker wants

Multilevel secure systems are excellent places to implement jails.

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22.6.2 Intrusion Handling22.6.2 Intrusion Handling

Restoring system to satisfy site security policySix phases

Preparation for attack (before attack detected)Identification of attack

Containment of attack (confinement) Eradication of attack (stop attack)

Recovery from attack (restore system to secure state) Follow-up to attack (analysis and other actions)

Discussed in what follows

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22.6.2.1 Containment Phase22.6.2.1 Containment Phase

Goal: limit access of attacker to system resources

Two methodsPassive monitoring

Constraining access

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Passive MonitoringPassive Monitoring

Records attacker’s actions; does not interfere with attackIdea is to find out what the attacker is after and/or methods the attacker is using

Problem: attacked system is vulnerable throughoutAttacker can also attack other systems

Example: type of operating system can be derived from settings of TCP and IP packets of incoming connections

Analyst draws conclusions about source of attack

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Constraining ActionsConstraining Actions

Reduce protection domain of attacker

Problem: if defenders do not know what attacker is after, reduced protection domain may contain what the attacker is after

Stoll created document that attacker downloaded

Download took several hours, during which the phone call was traced to Germany

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DeceptionDeception

Deception Tool KitCreates false network interface

Can present any network configuration to attackers

When probed, can return wide range of vulnerabilities

Attacker wastes time attacking non-existent systems while analyst collects and analyzes attacks to determine goals and abilities of attacker

Experiments show deception is effective response to keep attackers from targeting real systems

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22.6.2.2 Eradication Phase22.6.2.2 Eradication Phase

Usual approach: deny or remove access to system, or terminate processes involved in attackUse wrappers to implement access control

Example: wrap system callsOn invocation, wrapper takes control of processWrapper can log call, deny access, do intrusion detectionExperiments focusing on intrusion detection used multiple wrappers to terminate suspicious processes

Example: network connectionsWrapper around servers log, do access control on, incoming connections and control access to Web-based databases

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FirewallsFirewalls

Mediate access to organization’s networkAlso mediate access out to the Internet

Example: Java applets filtered at firewallUse proxy server to rewrite them

Change “<applet>” to something else

Discard incoming web files with hex sequence CA FE BA BEAll Java class files begin with this

Block all files with name ending in “.class” or “.zip”Lots of false positives

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Intrusion Detection and Isolation ProtocolIntrusion Detection and Isolation Protocol

Coordinates response to attacksBoundary controller is system that can block connection from entering perimeter

Typically firewalls or routers

Neighbor is system directly connectedIDIP domain is set of systems that can send messages to one another without messages passing through boundary controller

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Intrusion Detection and Isolation ProtocolIntrusion Detection and Isolation Protocol

IDIP protocol engine monitors connection passing through members of IDIP domains

If intrusion observed, engine reports it to neighbors

Neighbors propagate information about attack

Trace connection, datagrams to boundary controllers

Boundary controllers coordinate responsesUsually, block attack, notify other controllers to block relevant communications

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Example of IDIPExample of IDIP

C, D, W, X, Y, Z boundary controllersf launches flooding attack on ANote after X suppresses traffic intended for A, W begins accepting it and A, b, a, and W can freely communicate again

C D

X

W

b

a

AeY

Z

f

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22.6.2.3 Follow-Up Phase -- Counterattacking22.6.2.3 Follow-Up Phase -- Counterattacking

Use legal proceduresCollect chain of evidence so legal authorities can establish attack was real

Check with lawyers for thisRules of evidence very specific and detailed

If you don’t follow them, expect case to be dropped

Technical attackGoal is to damage attacker seriously enough to stop current attack and deter future attacks

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ConsequencesConsequences

1. May harm innocent party• Attacker may have broken into source of attack or may be

impersonating innocent party

2. May have side effects• If counterattack is flooding, may block legitimate use of network

3. Antithetical to shared use of network• Counterattack absorbs network resources and makes threats more

immediate

4. May be legally actionable

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Example: CounterwormExample: Counterworm

Counterworm given signature of real wormCounterworm spreads rapidly, deleting all occurrences of original worm

Some issuesHow can counterworm be set up to delete only targeted worm?What if infected system is gathering worms for research?How do originators of counterworm know it will not cause problems for any system?

And are they legally liable if it does?

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IDS ToolsIDS Tools

Snort

Honeypot, www.honeyd.orgA honeypot is a system designed to look like something that an intruder can hack.

The goal is to deceive intruders and learn from them without compromising the security of the network.

IPAudit,

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Key PointsKey Points

Intrusion detection is a form of auditing

Anomaly detection looks for unexpected events

Misuse detection looks for what is known to be bad

Specification-based detection looks for what is known not to be good

Intrusion response requires careful thought and planning