A Framework for Detecting Malformed SMS Attack

A.C. Chen 2012/07/23 @ ADL 1

A FRAMEWORK FOR DETECTING MALFORMED SMS ATTACK

M Zubair RafiqueMuhammad Khurram KhanKhaled AlghathbarMuddassar Farooq

The 8th FTRA International Conference on Secure and Trust Computing, data management, and Applications ( STA 2011)

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Outline Introduction Malformed message detection

framework Evaluation and experimental results Conclusion

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Introduction Malformed message detection

framework Evaluation and experimental results Conclusion

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SMS Deliver Process

SMS_SUBMIT

SMS_DELIVER

BSC: Base Station Controller

MSC: Mobile Switch CenterGMSC: Gateway MSCIWMSC: Interworking MSC

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Short Message Service ( SMS ) A message sent to and from a mobile

phone are first sent to an intermediate component called the Short Message Service Center (SMSC)

The SMS message exists in 2 formats SMS_SUBMIT: mobile phone to SMSC SMS_DELIVER: SMSC to mobile phone

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GSM Modem The SMS received on a mobile phone

is handled through the GSM modem Provides an interface with the GSM network

and the application processor of a smart phone Controlled through standardized AT commands

AppsTelephony Stack

Modem

AT commandsAT Result Codes

Responsible for cellular communications

Responsible for the communication between application processor and the modem

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Example: SMS_DELIVER///AT Result Code + the length of SMS

Complete SMS string in hex.

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Malformed SMS attack Cause the application processor to

reach an undefined state Significant processing delays Unauthorized access Denying legitimate users access …

AppsTelephony

Stack

Modem

However, malformed message detection in mobile phones has received little attention

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In this Paper… A malformed message detection

framework was proposed Automatically extracts novel syntactical

features to detect a malformed SMS at the access layer of mobile phones

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Introduction Malformed message detection

framework Evaluation and experimental results Conclusion

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Common Idea Anomalies are deviations from a

learnt normal model [Patrick Dssel, et al.] Learning→Normal model→Anomaly detection Supported by our pilot studies

• The distance values of malformed messages are normally greater than those of benign messages

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SMS Detection Framework

MessageAnalyzer

FeatureExtractio

nFeature

SelectionClassificatio

n

12

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Message Analyzer Message dissection

Transform incoming SMS messages into a format from which we can extract intelligent features

Extracts the complete SMS message string i.e. the second line of AT Result code

FeatureExtraction

FeatureSelection ClassificationMessage

Analyzer 13

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Extraction of String Features Mine features from an incoming SMS

message Exploit the properties of a suffix tree Use a set of attribute strings to model the content

of the incoming message Entrenching function : Extracts the

( attribute, value ) pair from the suffix tree attribute: a feature string a value: the frequency of a from the nodes of the

suffix tree Example

FeatureExtraction

FeatureSelection ClassificationMessage

Analyzer

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Raw Model Vectors For the purpose of training, we

prepared a training data set 𝛫: Set of messages used for training, ={ 𝛫 m1,

…,mk } After each mi passes through the entrenching

function, we have our raw model

FeatureExtraction

FeatureSelection ClassificationMessage

Analyzer

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Feature Selection The high dimensionality of the raw

model will result in large processing overheads

Remove redundant features having low classification potential Not at the cost of a high false alarm rate

MessageAnalyzer

FeatureExtraction ClassificationFeature

Selection

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Selection Techniques Use 3 selection mechanisms to obtain

3 distinct model set of attributes Information Gain (IG) Gain Ratio (GR) Chi Squared (CH)

MessageAnalyzer

FeatureExtraction ClassificationFeature

Selection

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Distance/Divergence For a given vector of pairs, compute

the deviation ( message score, distance ) of the vector

Use 2 well-known distance measures to obtain the score Manhattan distance (md) Itakura-Saito Divergence (isd)

MessageAnalyzer

FeatureExtraction

FeatureSelection Classification

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Classification Threshold value

The largest distance score of a message in the training model

Raise an alarm If the distance score of an incoming SMS is

greater than the threshold value

MessageAnalyzer

FeatureExtraction

FeatureSelection Classification

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ReviewTraining is only required in the beginning

20

threshold

message score

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Introduction Malformed message detection

framework Evaluation and experimental results Conclusion

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Evaluation Collect real world dataset of SMS

message ≥ 5000 benign datasets

• Developed modem terminal interface to collect more than 5000 real world benign SMS dataset

≥ 5000 malformed datasets• SMS injection framework ( Mulliner, C., et al., 2009)

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Experimental Goal To select the best feature selection technique and distance measure

3 feature selection modules• Information Gain (IG)• Gain Ratio (GR) • Chi-squared (CH)

2 distance measures• Manhattan distance (md)• Itakura-Saito Divergence (isd)

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Parameters and Definitions Used 4 parameters to define the

detection accuracy and the false alarm rate True Positive (TP), False Positive (FP), False

Negative (FN), True Negative (TN) Detection Rate

False Alarm Rate

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Results: Receiver Operating Characteristic Curves

ROC using Manhattan Distance ROC using Itakura-Saito Divergence

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Results: Overheads Training and Threshold calculation overheads in ( ms/100 SMS ) Testing overheads in ( ms/1 SMS ) using Information Gain, Gain Ratio

and Chisquared for Manhattan distance and Itakura-Saito Divergence

Average training time = 3.5s/100SMS

Average detection time of a malformed message = 10ms

Provides the best performance

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Introduction Malformed message detection

framework Evaluation and experimental results Conclusion

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Conclusion A real time malformed message

detection framework Tested on real datasets of SMS messages Successfully detects malformed messages with

a detection accuracy of more than 98% The future research will focus on

further optimizing and deploying it on real world mobile devices and smart phones

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Q & A

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Example of a Suffix Tree Extract feature strings from an

incoming message m=0110223 The set of attribute strings is thus generated

FeatureExtraction

FeatureSelection ClassificationMessage

Analyzer

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Example of Entrenching Function

Message m=0110223 Set of attribute:

{3, 0, 1, 2, 23, 223, 110223, 10223, 0223, 0110223}

Vector of pairs =(3, 1), (0, 2), (1, 2), (2, 2), (23, 1), (223, 1)…

FeatureExtraction

FeatureSelection ClassificationMessage

Analyzer

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The RIL in the context of Android's Telephony system architecture [ref]

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Modules that implement telephony functionality