Sweety Chauhan 12 October 2005 Proceedings of the 2001 ACM symposium on Applied computing Brajendra Panda, Rajesh Yalamanchili, March 2001 Security and

Sweety Chauhan

12 October 2005

Proceedings of the 2001 ACM symposium on Applied computing Brajendra Panda, Rajesh Yalamanchili, March 2001

Security and Error Correction/Detection in 802.1x

and GSM

Transaction Fusion in the Wake of Information

Warfare

Sweety Chauhan

October 19, 2005

CMSC 691I

Brajendra Panda, Rajesh Yalamanchili Computer Science Department, University of North Dakota

Proceedings of the 2001 ACM symposium on Applied Computing March 2001

Information Warfare

CMSC 691I 2Information Warfare

Overview

New and Significant

Defensive Information Warfare

Previous Work

Transaction Fusion Model

Recovery Algorithm

Conclusion

Future work


New and Significant

The existing recovery techniques first undo all malicious and affected transactions and then redo all affected transactions

New a model to fuse groups of malicious and affected transactions

Significant by combining transactions, several data items that were required to be

accessed multiple times in each individual transaction are now accessed only once in a fused transaction.

Acceleration of recovery process


What is Information Warfare?

Any electronic attack intended to disrupt a computer system is termed as “Information Warfare”

Defensive information warfare - safeguarding systems from malicious attacks

Databases form the prime target for many of the malicious activities carried out by intruders


Defensive Information Warfare

A functional

paradigm of

defensive

information

warfare

1. Protect

Techniques designed to guard hardware, software and user dataagainst threats from both outsiders as well as from malicious insiders

2. Detect

1. critical to the survival of information systems2. can be achieved by gaining an accurate understanding of the “state” of the system at any given point in time e.g. Intrusion Detection Systems

3. React

1. assessing damage2. finding malicious hidden programs3. locating and closing any back doors left by an attacker for future reentry4. recovering data


Database Information Attack

In the protect phase, an attempt is made to prevent an attack on the database.

Absolute prevention can never be achieved and hence there is a detect phase. intrusion detection mechanisms to identify attacks

Once an attack is detected, react phase makes every effort to completely wipe out the effect of malicious transaction and restore the database to a consistent state, the state database would have

reached if there was no malicious transaction must be performed in the shortest time possible


Previous Work

The damage inflicted on a database can spread manifold within a short time when the transactions are interdependant and there is lot of activity in the

system

Traditional recovery techniques, which deal with media and system failures, are not efficient in dealing with malicious attacks rollback the database to the point of attack all transactions except for the malicious transaction(s) are then redone to

bring the database to a consistent state

Highly time-consuming process which is UNACCEPTABLE!!!


Traditional Recovery Techniques

Transaction dependency approach malicious transaction(s) and the transactions that depend on the malicious

transaction(s) are considered for recovery purposes.

Data dependency approach data items that are affected by the malicious transaction(s) and their

dependant data items are restored to their correct values.

log has to be scanned from the point of attack to the end

=> time consuming process


Transaction Fusion Model

Assumptions: The schedule produced by the scheduler is strictly serializable Blind writes are not allowed The entire schedule is logged and the log cannot be modified or purged by

any user

Transaction dependency approach fuses a group of transactions into a single transaction

Fast and efficient recovery accelerating the redo and undo phases


Types of Transaction

Malicious Transaction

Authentic Transaction

Set Notations malicious transactions (M), authentic transactions (A), affected transactions (F), and unaffected transactions (U)

A schedule is a collection of transactions that are interleaved and there exists a partial ordering among the operations of these transactions


Definitions (I)

A transaction Tj is said to be dependant upon another transaction Ti, if there exists a data item x such that Ti is the last committed transaction to

update x before Tj reads x.

dependency relationship is denoted by Ti → Tj

For any two schedules of transactions Si and Sj, combined schedule

where Si and Sj do not have any transaction in common

the combined effect of executing schedule Si followed by Sj

Sj is a sub-schedule of Si (Sj ⊂s Si ) if Sj contains all the transactions that are present in Si

the order among these transactions is same

(Si Sj)


Definitions (II)

If Sj ⊂s Si then the effect of executing Si and undoing all the transactions belonging to Sj is represented by Si Θ Sj

Two schedules are said to be value equivalents, if they produce the same final state when they start from the same initial state


Example

Si : T1, T3, T5, T4, T2, T6

Sj : T1, T2, T6

T4 and T5 depend on T3

T6 depends on T1

T2 depends on T1 and T3

Following steps are carried out for finding S i Θ Sj

1. Dependency graph for original schedule (S i) is drawn first

2. The last transaction in Si (T6) is undone only if it is a leaf node

3. Node representing the transaction undone is deleted and the process is repeated.

Si Θ Sj: T3, T5, T4

T2

T3T1

T4 T5T6

Dependency Graph


Model Description (I)

When an attack by one or more malicious transactions is detected

Step 1: Undo affected and malicious transactions

Step 2: Affected transactions are then re-executed

Step 3: Unaffected transactions are skipped in both undo and redo processes

Compensated-for transaction - the transaction that needs to be undone

Compensating transaction - the transaction that eliminates the effect of compensated-for transaction a compensating transaction is executed for each transaction that needs to

be undone


Model Description (II)

Fuse transactions wherever possible and execute the fused transactions

Significant gain in time one commit incase of a fused transaction rather than multiple commits good chance of dependant transactions being fused into a single

transaction

Database has a state at any given instant of time. It will change from its current state to another state when one or more

transactions commit contemporary transactions- one or more transactions committing at the

same time


Recovery Procedure (I)

variables M_ID and F_ID - generate IDs for the newly formed fused malicious transaction and fused affected transaction

variable M_flag serves as a flag to identify the first malicious transaction in a group of malicious transactions

F_flag helps in identifying the first affected transaction in a group of affected transactions


Recovery Procedure (II)

The function fuse combines the operations belonging to two transactions (T1 and T2) and places them in a single fused transaction (Tf)

If the two conditions needed for fusion of writes are satisfied, then both the writes are fused together by calling fuse_writes function


Recovery Procedure (II)- Algorithm* Source: Research paper [1]


Recovery Procedure (III)

The algorithm is suitable for strict logical logging, where before and after images are not stored

In semantic logging, before and after images are stored along with the transaction semantics Undo process – simple and consists of two phases:

first phase – the set of data items updated by malicious transactions and affected transactions are determined

second phase - the unaffected transactions updating these data items are identified

Redo process - same as that of strict logical logging


Conclusion

Recovery - phase in defensive information warfare Should be carried out in the shortest time possible to minimize denial of

service

Set of malicious transactions and affected transactions => single fused transaction fused malicious and affected transactions sets are undone in undo process fused affected transactions sets are re-executed in redo process Number of transactions and total number of operations are minimized Executing new fused sets of transactions during recovery expedites the

process


Future Work

Simulation study of this model

Integration of this model in distributed databases and systems


References

Transaction fusion in the wake of information warfare, Brajendra Panda, Rajesh Yalamanchili March 2001, Proceedings of the 2001 ACM symposium on Applied computing

The IWAR range: a laboratory for undergraduate information assurance education, Joseph Schafer, Daniel J. Ragsdale, John R. Surdu, Curtis A. Carver, April 2001, Journal of Computing Sciences in Colleges , Proceedings of the sixth annual CCSC northeastern conference on The journal of computing in small colleges, Volume 16 Issue 4

Emerging technologies for homeland security: Countering terrorism through information technology, Robert Popp, Thomas Armour, Ted Senator, Kristen Numrych , March 2004 Communications of the ACM, Volume 47 Issue 3


Thanks a lot …

For Your

Presence

And

Patience


Any Questions


Homework

Presentation Slides, Homework and Research Papers are available at :

www.umbc.edu/~chauhan2/CMSC691I/

Documents

Sweety Chauhan 12 October 2005 Proceedings of the 2001 ACM symposium on Applied computing Brajendra Panda, Rajesh Yalamanchili, March 2001 Security and