peds ii - 100720021 an overview of intrusion detection & countermeasure systems – research...

PEDS II - 10072002 1

An Overview of Intrusion An Overview of Intrusion Detection & Countermeasure Detection & Countermeasure

Systems – Research DirectionsSystems – Research DirectionsPart IIPart II

Fernando C. Colon OsorioFernando C. Colon Osorio

Computer Science DepartmentComputer Science Department

Worcester, MA 01609Worcester, MA 01609

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OutlineOutline• Previous Talk – what did we cover last?Previous Talk – what did we cover last?

– Definitions– A Model of an Intrusion– Basic Approaches – Critical research Problems

• PEDS – Part IIPEDS – Part II– S.A.F.E Architecture– S.A.F.E. approach to critical research problems– Other Related topics

• ConclusionsConclusions

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Intrusion Detection System – Intrusion Detection System – DefinitionDefinition

Formal Definition [10], [11]Formal Definition [10], [11]

““Intrusion Detection (ID) is the problem of identifying individuals Intrusion Detection (ID) is the problem of identifying individuals who are who are using,using, or attemptingor attempting to to use a computer system use a computer system without authorization (i.e., crackers) and those who have without authorization (i.e., crackers) and those who have legitimate access to the system but are abusing their privileges legitimate access to the system but are abusing their privileges (i.e., the insider threat”). (i.e., the insider threat”).

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Intrusion TimelineIntrusion TimelineSystem is Secure/DependableSystem is Secure/Dependable System is System is Secure/DependableSecure/Dependable

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IntrusionIntrusion

AttemptAttempt

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IntrusionIntrusion

Detected byDetected by

IDS and/orIDS and/or

IDCSIDCS

IntrusionIntrusion

CountermeasuresCountermeasures

LaunchedLaunched

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MTBASIMTBASI

Attacks BeginAttacks Begin

MTTIDMTTID

System is System is Secure/Dependable Secure/Dependable

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IntrusionIntrusion

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MTTCIMTTCI

MTBSIMTBSI

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11stst

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IDCSIDCS

IntrusionIntrusion


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Attack Is SuccessfulAttack Is Successful

MTTIDMTTID


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MthMth

IntrusionIntrusion

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MTTCIMTTCI

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11stst

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IDCSIDCS

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DiagnosisDiagnosis

RegionRegion

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11stst

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Repair/ Repair/ Re-Re-IntegrationIntegration

RegionRegion

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11stst

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IDCSIDCS

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System System OperationalOperational

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Anomaly vs. Misuse IDS Anomaly vs. Misuse IDS systemssystems

In past years, multiple Intrusion Detection systems In past years, multiple Intrusion Detection systems have been proposed an implemented. All of the have been proposed an implemented. All of the proposed systems are based on one or the other of proposed systems are based on one or the other of two basic approaches.two basic approaches.

• anomaly detection anomaly detection • misuse detection. misuse detection.

Note: Kumar [13] presents a fairly complete Note: Kumar [13] presents a fairly complete categorization of the most important systems categorization of the most important systems proposed or build thus far. proposed or build thus far.

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Intrusion TimelineIntrusion TimelineSystem is System is Secure/DependableSecure/Dependable

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Realm of MisuseRealm of Misuse

Detection Detection TechniquesTechniques

Realm of AnomalousRealm of Anomalous

Detection Detection TechniquesTechniques

MTTIDMTTID


MMthth

MthMth

IntrusionIntrusion

AttemptAttempt

(Success)(Success)

MTTCIMTTCI

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System is System is Secure/DependableSecure/Dependable

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Figure 1 – Generic Intrusion Detection Model [Denning]Figure 1 – Generic Intrusion Detection Model [Denning]

EnvironmentEnvironment

ClockClock

Activity ProfileActivity Profile

Event GeneratorEvent Generator

S = { sS = { s11, s, s22, …, s, …, sn n }}

Rule SetRule Set

Generate New ProfileGenerate New Profile

DynamicallyDynamically

GenerateGenerate

AnomalyAnomaly

RecordsRecords

UpdateUpdate

ProfileProfile

Audit Trails/

Network Packets/

Application Trails

Assert New Rules

Modify Existing Rules

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Problems with Current ApproachesProblems with Current Approaches

• Amongst the most important consideration and limitations present in the Amongst the most important consideration and limitations present in the design of all such systems are the following set of problems.design of all such systems are the following set of problems.

• Problem # 1: Feature selection and pattern categorization. Problem # 1: Feature selection and pattern categorization.

– Simply stated, in Denning’s Model, Figure 1, it is assumed that the event generator can effectively select, a priori, the set of features or measures to monitor which will render an optimal set for Intrusion Detection.

• Problem # 2: the problem of adaptation. Problem # 2: the problem of adaptation.

– Systems have been build and deployed that deal very effectively with threats or intrusions previously reported or categorized.

– When previously unseen threats appear, the systems perform poorly.

• In the 1999 DARPA - Off-Line Intrusion Detection Evaluation [14], it was reported that the systems under test failed to detect an attack in 17.2 %

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Problems, contn..Problems, contn..• Problem # 3: Fault Tolerance Problem # 3: Fault Tolerance

– Resistance to subversion: Systems do fail due to accidental or malicious activities.

• system being designed must be able to recover from the traditional forms of failures such as crashes, software failures, and so forth.

• System must be able to protect itself from deliberate attempts to compromise it.

• Problem # 4: Performance Problem # 4: Performance

– System must impose minimal overhead on the system is protecting while running.

– System must be capable to sustain its performance characteristics under increasing loads and changes in the pattern of usage.

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Problems, contn..Problems, contn..

• Problem # 5: Intrusion Detection System Evaluation & Problem # 5: Intrusion Detection System Evaluation & CharacterizationCharacterization

– Workloads

– Definition of “Goodness” – i.e., how reliable in detecting intrusions is the system?

• MTBIS• MTTR after an intrusion?• Others?

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SSecure ecure AArchitecture & rchitecture & FFail-safe ail-safe EEnginengine(S.A.F.E.)(S.A.F.E.)

S.A.F.E Intrusion Detection & Countermeasure system was S.A.F.E Intrusion Detection & Countermeasure system was conceived with the specific goal of attacking the above conceived with the specific goal of attacking the above problems, and some others.problems, and some others.

S.A.F.E is a distributed rather than a network or host based systemS.A.F.E is a distributed rather than a network or host based system

• Its structure is similar to the structure proposed in AAFID [ see Balasubrayan and Garcia-Fernandez, 1998] in the sense that it depends on a set of autonomous objects (agents in AAFID nomenclature) that can reside anywhere in a network.

• However, S.A.F.E. differs significantly from AAFID, or other distributed Intrusion Detection systems (see, EMERALD system), in that all control is distributed, and implemented using a system wide distributed system component, called the Trust Manager (TTM),

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S.A.F.E Intrusion Detection & Countermeasure system architecture: S.A.F.E Intrusion Detection & Countermeasure system architecture:

– Real time– Distributed

• Most existing IDS are either host or network based. Single point of failure, lack of scalability, poor performance

• S.A.F.E architecturally/logically partitions the functions of an IDS system into a set of objects that reside anywhere in the system or the network.

– Hierarchical

• a set of reliable services is provided reliably and securely to the upper layers of the architecture

– Fault-Resilient

• Internal failures• External attacks,

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S.A.F.E Architectural Components:S.A.F.E Architectural Components:

• Probes := data collection objects. Probes are started/stopped by the Event Generator Object (EGO).

• EGO:= independent running entity that provides some of the basic S.A.F.E. services.

– Starts/Stop probes– Filters data from Probes. It provides data collection and a data abstraction layer (for common

architecture)– Implements lower/level Intrusion detection functions– Provides a Class of Event Service

• Secure Host Manager (SHM):= independent running entity that provides two basic services– Intrusion detection for the host– Implements “learning” functions– In addition, the SHM:

» Provides the Trust Manager with a “Last Gasp” message – “I am Potentially Compromised” before launching its countermeasure functions.

» It stops processing further requests from the Trust Manager – takes itself off-line.» Countermeasure launching.» Starts/Stops all EGO objects

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S.A.F.E System Wide Services:S.A.F.E System Wide Services:

• TTM:= the trust managerTTM:= the trust manager. The TTM is an independent entity . The TTM is an independent entity serving three major functions:serving three major functions:

– It “knows” which logical “nodes” can be trusted.

• This is accomplished through the concept of a trust relationship matrix. • Nodes trust measure tij is added, modified, and changed using a distributed software algorithm • Algorithm is based on the solution to the “Byzantine General’s Problem”.

– It delivers the “Last Gasp Message” to other nodes in the system.

– Prevents partition of the trust system.

• Atomicity Manager (ATOM)Atomicity Manager (ATOM) := independent entity providing := independent entity providing atomic secure operations across the system.atomic secure operations across the system.

– running entity that provide some of the basic S.A.F.E. services.

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SSecure ecure AArchitecture & rchitecture & FFail-safe ail-safe EEngine (S.A.F.E.)ngine (S.A.F.E.)

PP

PP

PP

EGOEGO

SHMSHM

TT

TT

MM

EGOEGO

SHMSHMTT

TT

MM

PP

PP

PP

PP

EGOEGO

TT

TT

MM

SHMSHM

SHMSHM

EGOEGO

PP

PP

PP

PP

EGOEGO

PPPP

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S.A.F.E. SHM Intrusion Detection & Learning S.A.F.E. SHM Intrusion Detection & Learning EngineEngine

( class(( class(xx), ), xx ) )

CC11((xx))

CC22((xx))

CCkk((xx))

wwi i * C* Cii ( (xx))

True/FalseTrue/False

(Intrusion Correctly(Intrusion Correctly

Identified)Identified)

GG((wwi i ))

GG((wwi i ))

GG((wwi i ))


wwi2i2* C* C22 ( (xx))

wwk k * C* Ckk ( (xx))

..

..

..

Lower Lever IntrusionLower Lever Intrusion

Detection (EGO)Detection (EGO)

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Intrusion Detection ModelsIntrusion Detection ModelsFigure 3 – Model of An Intrusion/ Attack

Node b

Node h

Node g

Node e Node d

Node a

Node f

Node c

Tab,

Tae, Tea

Tce, Tec

TebTbd

Tdg

TegTef

Thg

Tfh

Source of Attack

Node Under Attack

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A network ModelA network Model• A trust function Tij (t) for i ¹ j, exist between two nodes, it is not necessarily symmetrical. A trust function Tij (t) for i ¹ j, exist between two nodes, it is not necessarily symmetrical.

• The trust function Tij (t) changes over time. The trust function Tij (t) changes over time.

• In addition, the lack of trust between two nodes will be denoted as having a trust relationship of zero value, Tij (t) = 0. In addition, the lack of trust between two nodes will be denoted as having a trust relationship of zero value, Tij (t) = 0.

• In the above example, Node a is the source of the intruder attack, while Node h is the target of the attack. Note that, the In the above example, Node a is the source of the intruder attack, while Node h is the target of the attack. Note that, the path for the intruder ispath for the intruder is

– Path 1: a Ü e Ü g Ü h– Path 2: a Ü b Ü e Ü g Ü h– Path 3: a Ü d Ü g Ü h

• This topological constraint amongst nodes in a network has a significant advantage over other approaches. That is, it This topological constraint amongst nodes in a network has a significant advantage over other approaches. That is, it allows the designer of the IDC System to create multiple logical layers of defense against intruders, in effect, creating allows the designer of the IDC System to create multiple logical layers of defense against intruders, in effect, creating time to detect potential intrusions and dwarfed them. time to detect potential intrusions and dwarfed them.

• ExampleExample

– Let’s say that nodes b and e suspect an intrusion by using traditional audit methods. Then, nodes b and e can invoke a state change on their trust relationships with other nodes in such a way that,

Taj (t) = 0 for all j Taj (t) = 0 for all j ¹ a and t > t of intrusion; and a and t > t of intrusion; andEquation 1:Equation 1:

Tej (t) = 0 for all j ¹ e and t > t of intrusion.Tej (t) = 0 for all j ¹ e and t > t of intrusion.

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Problems & Well Known Solutions Problems & Well Known Solutions Present in the IDCS fieldPresent in the IDCS field

• Problem # 1: Feature selection and pattern Problem # 1: Feature selection and pattern

categorization.categorization.

– Simply stated, in Denning’s Model, Figure 1, it is assumed that the event generator can effectively select, a priori, the set of features or measures to monitor which will render an optimal set for Intrusion Detection.

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ClockClock



S = { sS = { s11, s, s22, …, s, …, sn n }}

Rule SetRule Set



GenerateGenerate

AnomalyAnomaly

RecordsRecords

UpdateUpdate

ProfileProfile

Audit Trails/

Network Packets/

Application Trails

Assert New Rules


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Learning, Feature Selection, Inductive Learning, Learning, Feature Selection, Inductive Learning, Learning with a teacher, KDD systemsLearning with a teacher, KDD systems

Given a training set characterized by training samples, Given a training set characterized by training samples,

TT = { ( = { ( xx11, , yy11), ( ), ( xx22, , yy22), …, ( ), …, ( xxnn, , yynn) }) }

For some unknown functionFor some unknown functionƒ((x) x) = y= y

Where each Where each xxi i is an attribute vector of the formis an attribute vector of the form

XXii = { x = { xi1i1, x, xi2i2, …, x, …, xikik }, },

And each yAnd each yi i is the class label belonging to the setis the class label belonging to the set

YY = { y = { y11, y, y22, …, y, …, ymm }, then }, then

Find the mapping (function), Find the mapping (function), ƒ*,, such that such that

ƒ*((xx)) ƒ((xx) )

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Learning, Feature Selection, Inductive Learning, Learning, Feature Selection, Inductive Learning, Learning with a teacher, KDD systems, contn…Learning with a teacher, KDD systems, contn…

Three Important and critical questions:Three Important and critical questions:

Q1: Does an Q1: Does an ƒ*((xx) exists?, i.e., is there a pattern?, is there knowledge to be mined?, ) exists?, i.e., is there a pattern?, is there knowledge to be mined?, is there learning to be developed?; is there learning to be developed?;

Q2: What are the correct set of features xQ2: What are the correct set of features x ikik providing “best” inference rules? providing “best” inference rules?

Q3: If Q3: If ƒ*((xx) exists, then what is the computational complexity of the algorithms trying ) exists, then what is the computational complexity of the algorithms trying to find to find ƒ*((xx)?)?

In this context, we are interested in algorithms that can find ƒ*(x) in finite time. Or what is the computational quality, Q c( s), of the algorithm?

In this context, given two algorithm that find ƒ*(x), A1 and A2, then

Q Q cc( A( A11) > Q ) > Q cc( A( A22) if the computational running time of A) if the computational running time of A1 1 << A A2.2.

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Problems & Well Known Solutions Problems & Well Known Solutions Present in the IDCS fieldPresent in the IDCS field

• Problem # 2: the problem of adaptation. Problem # 2: the problem of adaptation.

– Systems have been build and deployed that deal very effectively with threats or intrusions previously reported or categorized.

– When previously unseen threats appear, the systems perform poorly.

• In the 1999 DARPA - Off-Line Intrusion Detection Evaluation [14], it was reported that the systems under test failed to detect an attack in 17.2 %

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ClockClock



S = { sS = { s11, s, s22, …, s, …, sn n }}

Rule SetRule Set



GenerateGenerate

AnomalyAnomaly

RecordsRecords

UpdateUpdate

ProfileProfile

Audit Trails/

Network Packets/

Application Trails

Assert New Rules


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Figure 2 – A simplified Intrusion Detection EngineFigure 2 – A simplified Intrusion Detection Engine

EnvironmentEnvironment ClockClock

Memory of IDSMemory of IDS

(Rule Set/ Activity Profile(Rule Set/ Activity Profile

Decision EngineDecision Engine

ffg g ((, S, M, P(n), T, G ), S, M, P(n), T, G )

S = { sS = { s11, s, s22, …, s, …, sn n }}

1, 2, …, n }

Create New Rules/ProfilesCreate New Rules/Profiles

Modify Existing Rules/ProfilesModify Existing Rules/Profiles

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Meta - LearningMeta - Learning

Loose Definition [chang’ 93]:Loose Definition [chang’ 93]:

““Learning from learned knowledge”Learning from learned knowledge”

References: References:

[chang’ 93]:= P. Chang and S. Stolfo. “Experiments on multistrategy learning by meta-learning”, Proc. Second [chang’ 93]:= P. Chang and S. Stolfo. “Experiments on multistrategy learning by meta-learning”, Proc. Second International Workshop, Multistrategy Learning, pp. 150-165, 1993.International Workshop, Multistrategy Learning, pp. 150-165, 1993.

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Meta – Learning, contn…Meta – Learning, contn…

Let the training set be instances of correct Let the training set be instances of correct classifications and predictions, such as the classifications and predictions, such as the Training set is of the form:Training set is of the form:

TT = { class( = { class( xx), ), CC11((xx), ), CC22((xx)), …,, …, CCkk((xx) | ) | x x E }

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S.A.F.E. SHM Intrusion Detection & Learning S.A.F.E. SHM Intrusion Detection & Learning EngineEngine

( class(( class(xx), ), xx ) )

CC11((xx))

CC22((xx))

CCkk((xx))


True/FalseTrue/False

(Intrusion Correctly(Intrusion Correctly

Identified)Identified)

GG((wwi i ))

GG((wwi i ))

GG((wwi i ))


wwi2i2* C* C22 ( (xx))

wwk k * C* Ckk ( (xx))

..

..

..

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• Problem # 3: Fault Tolerance Problem # 3: Fault Tolerance

– Resistance to subversion: Systems do fail due to accidental or malicious activities.

• The Trust Manager (TTM)

• Problem # 4: Performance Problem # 4: Performance

– Distributed architecture– Light weight processes– Simple objects

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• Problem # 5: Intrusion Detection Systems Evaluation & Problem # 5: Intrusion Detection Systems Evaluation & CharacterizationCharacterization

– Workloads – testing, e.g., the 1999 DARPA evaluation uses 10 workload or traces to compare systems

• Results are empirical• Not representative of all environments• Very little data available

– Definition of “Goodness” (Modeling)

• how reliable in detecting intrusions is the system?, and• how intrusion resilient is the underlying system being protected?

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Intrusion TimelineIntrusion TimelineSystem is Secure/DependableSystem is Secure/Dependable

System is System is Secure/DependableSecure/Dependable

11stst

IntrusionIntrusion

AttemptAttempt

22ndnd

IntrusionIntrusion

AttemptAttempt

NthNth

IntrusionIntrusion

AttemptAttempt

(Success)(Success)

IntrusionIntrusion



IDCSIDCS

IntrusionIntrusion


LaunchedLaunched

ŒŒ NNthth

MTBASIMTBASI

Attacks BeginAttacks Begin

MTTIDMTTID


MMthth

MthMth

IntrusionIntrusion

AttemptAttempt

(Success)(Success)

MTTCIMTTCI

MTBSIMTBSI

MTTRMTTR

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Problem # 5: Intrusion Detection Systems Evaluation & Problem # 5: Intrusion Detection Systems Evaluation & Characterization, contn..Characterization, contn..

• Definition of “Goodness” – i.e., how reliable is the system with Definition of “Goodness” – i.e., how reliable is the system with respect to intrusions?respect to intrusions?

• MTBFi – mean time between failures/intrusion (defn: in this context a failure is a successful intrusion) – a measure of the underlying system being protected.

• MTTID – mean time to intrusion detection

• MTTCI – mean time to countermeasure issuance

• MTTR – mean time to contain & repair a successful intrusion

• Of course – Availability or A(t) = P ({system is operational and intrusion free at time t1 if it was intrusion free at time t0 }

Other metrics:

• MTBASI – mean time between 1st attack and successful intrusion

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Problem # 5: Intrusion Detection Systems Evaluation & Problem # 5: Intrusion Detection Systems Evaluation & Characterization, contn..Characterization, contn..

• Definition of “Goodness” – i.e., how reliable is the system with respect Definition of “Goodness” – i.e., how reliable is the system with respect to intrusions?to intrusions?

• MTBFi – mean time between failures/intrusion (defn: in this context a failure is a successful intrusion)

– A measure of the quality of the software system (O/S, Applications, and so forth)

• Further assume that MTBFi Reliability of the software, then,

– Techniques such as Component Base Reliability Estimation (CBRE), see Krishnamurthy and Mathur;

– Software Test Coverage and Reliability techniques, see Malaiya and Karcich- where software testing & coverage is used as

» predictors of software reliability» To estimate the remaining defects or number of residual faults; and» Mean time between failures or bugs.

Are all applicable.

• Challenge – to define an accurate model!!!

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Other related Topics – Honeypots & Other related Topics – Honeypots & HoneynetsHoneynets

Honeypot:Honeypot:

• A honeypot is a fake or false system to lure the hacker into. It provides A honeypot is a fake or false system to lure the hacker into. It provides another obstacle for the hacker.another obstacle for the hacker.

• honeypot systems are decoy servers or systems set up to gather honeypot systems are decoy servers or systems set up to gather information regarding an attacker or intruder into your system.information regarding an attacker or intruder into your system.

• honeypot traps tempt intruders into areas which appear attractive, worth honeypot traps tempt intruders into areas which appear attractive, worth investigating and easy to access, taking them away from the really investigating and easy to access, taking them away from the really sensitive areas of your systems. They do not replace other traditional sensitive areas of your systems. They do not replace other traditional Internet security systems but act as an additional safeguard with alarms.Internet security systems but act as an additional safeguard with alarms.

• A honeypot is a resource which pretends to be a real target. A honeypot is A honeypot is a resource which pretends to be a real target. A honeypot is expected to be attacked or compromised. The main goals are the expected to be attacked or compromised. The main goals are the distraction of an attacker and the gain of information about an attack and distraction of an attacker and the gain of information about an attack and the attacker.the attacker.

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honeypotshoneypots

honeypots will help you:honeypots will help you:

• notice when you are penetrated notice when you are penetrated

• learn how attacks are formed learn how attacks are formed

• identify who is attacking you identify who is attacking you

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honeypot Exampleshoneypot Examples

• honeypot Projecthoneypot Project

– http://www.landfield.com/isn/mail-archive/2000/Nov/0124.html

• Deception Tool Kit ProjectDeception Tool Kit Project

– http://www.all.net/dtk/index.html

• SpecterSpecter

– http://www.specter.com/default50.htm

http://www.landfield.com/isn/mail-archive/2000/Nov/0124.html

http://www.all.net/dtk/index.html

http://www.specter.com/default50.htm

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““Specter” – Basic IdeaSpecter” – Basic Idea

• Virtual Machine (VM) environmentVirtual Machine (VM) environment– Early Traps– Early detection

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honeypot Tools – “Specter”honeypot Tools – “Specter”

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Honeypots LimitationsHoneypots Limitations

• Hard to MaintainHard to Maintain• Human Resource Intensive – Specialize Human Resource Intensive – Specialize

KnowledgeKnowledge

– Operating Systems– Network security– Current deficiencies (holes) in both O/S

and applications

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HoneynetHoneynet

Honeynet Honeynet Honeypots Honeypots

Honeynet (Defn)Honeynet (Defn)

• A network systemA network system

• All systems are standard production All systems are standard production systems systems

• All usage is ~ Production All usage is ~ Production

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HoneynetHoneynet

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ConclusionsConclusions

• A new model based on Byzantine A new model based on Byzantine General’s problem will be General’s problem will be investigated.investigated.

• Research Area is prime for Research Area is prime for discovery.discovery.

peds ii - 100720021 an overview of intrusion detection & countermeasure systems – research...

Documents

intrusion timeline system

intrusion detection

computer system

successful mttid system

misuse ids systems

proposed systems

important systems

outline previous talk