MOBILE DEVICE FORENSICS USING NLP

MOBILE DEVICE FORENSICS USING NLP
Presented By:
Ankita Jadhao
Roll no. CSE15S2002
Supervised By:
Dr. A. J. Agrawal
Department of Computer Science & Engineering
Shri Ramdeobaba College of Engineering and Management
Nagpur
8/24/2015 1
Department of Computer Science and
Engineering

Contents
• Introduction
• Motivation
• Review of Literature
• Issues
• Methodology
• Existing System
• Advantages
• Bibliography
8/24/2015 Department of Computer Science and Engineering 2

Introduction
• Text mining also is known as Text Data Mining (TDM) and
Knowledge Discovery in Textual Database (KDT).
• Text Mining Tasks: 1. Exploratory Data Analysis
2. Information Extraction
3.Text Classification
Fig.1 Overview of Process

Introduction
Where Text Mining Used
• Biomedical applications
- To the identification of biological such as protein and gene names as well as
chemical compounds and drugs.
• Software applications
- The mining and analysis processes, and by different firms to improve their results.
- Software for tracking and monitoring terrorist activities.
• Online media applications
- Provide readers with greater search experiences which in turn increases site
“stickiness” and revenue.
• Marketing applications
- In analytical customer relationship management.

Introduction
• Sentiment analysis
-Analysis of movie reviews for estimating how favorable a review is for a movie
-Text has been used to detect emotions in the related area of affective computing.
• Security Application
-Monitoring and analysis of online plain text sources such as Internet news, blogs,
etc. for national security purposes.
- Criminal activity

Introduction
•Use of mobile phones to store and transmit personal and corporate
information
•Law enforcement, criminals and mobile phone devices
•There are limited corpora available
• A simple methodology is proposed for feature extraction
•What is corpora?
A text corpus is a large and structured set of texts.

Motivation
• Growth of mobile devices is rapid.
• The average cell phone user sends over 15,000 texts annually
• The average 18-24 year old sends almost 40,000 text messages
every year
• Most tools and methodologies merely acquire all supported data
and dump the output to a spreadsheet or HTML report
• Search hits must be manually examined and noted in a report.
• Problem 1.Simple keyword searches
2. Limited corpora
8/24/2015 7Department of Computer Science and Engineering

Literature Review
• Corpora: A corpus linguistics study of SMS text messaging[3]
-Tagg developed a text message corpus in British English, but an American English
corpus focusing on forensic application is desirable.
-Even for neutral text messages (non-drug-related), as the language is
significantly different which will skew results.
• Integrating Machine Learning into the Forensic Process
Approaches:-
1.The digital forensic process can be summarized as preservation, isolation,
correlation, and logging [4]
2. Begins with acquisition, then analysis, then concludes with presentation[5]
3. preservation, extraction, and then interpretation[1]

Literature Review
• Natural Language Processing: Dela Rosa and Ellen
- Detect linguistic patterns is an invaluable tool when applied to text messaging
data
- NLTK machine learning algorithms can be applied to a training set and assessed
on a test set to create and train an experimental model
- Applying k nearest neighbor (kNN) and support vector machines (SVM)
machine-learning algorithms to micro-text classification

Issues in Mobile Forensics
• Corpora is not available
• Accuracy Problem
• Feature Extraction
• Micro-Text Problem

Overview
•Mobile Device Forensic Extraction
•Text Message Corpus
•Feature Extraction
•Supervised Machine Learning

Mobile Device Forensic Extraction
• Text messages were extracted from mobile.
• Administrative access to the device was gained by utilizing the
redsn0w software to “jailbreak” the device.
• The text message database was accessed on the device by
navigating to the default location
• An MD5 hash value was computed for the text message
database file to mathematically verify that the file had not been
altered during the execution of the methodology

Corpora
1. First, collect the corpus data
2. Save the text in plain text format
3. Provide an identification of the text at the beginning of it.
4. Carry out any pre-processing of the text
5. The corpus was saved in extensible markup language XML
format.
Fig 3 Common Structures for Text Corpora

Corpora
<?xml version="1.0" encoding="UTF-8"?>
<corpus_data>
<text_message>
<class>0</class>
<subscriber>1</subscriber>
<message_body>Text Message</message_body>
<timestamp>9/4/2012 2:40 PM</timestamp>
<type>Incoming</type>
</text_message>
• Class refers to whether or not each individual text message is drug-
related (1) or neutral (0).
• The data were modified and additional text messages were developed

Information Extraction System
Fig 1 Simple Pipeline Architecture for an Information Extraction System
We first convert the unstructured data of natural language sentences into
the structured data.
Then getting meaning from text is called Information Extraction

Information Extraction System
Example:
String: We saw the yellow dog
Fig 2 Segmentation and Labeling at both the Token and Chunk Levels

Feature Extraction
Data Representation
– “Bag of words” most commonly used: either counts or binary
– Can also use “phrases” for commonly occurring combinations
of words
There are three aspects of feature extraction:
• Feature construction;
• Feature subset generation (or search strategy);
• Evaluation criterion estimation

Approach for Feature Extraction
•Utilizing a count of known drug-related unigrams as a Feature
•NLTK was used to identify bigrams of interest
•The alternate approach
-Two-word pairs as features and to allow the algorithm to
determine which bigrams were most effective in classifying text
messages as drug-related or neutral.
Example:
1. “After school today let’s go smoke some weed at my house.”
2. “Hey pull that weed in my flower garden when you get
home.”

Approach for Feature Extraction
•While the first text message was drug-related, the second
was neutral and would therefore be a false positive.
•The hypothesis was that drugrelated terms would exist in
frequented bigrams, such as “smoke weed,” “mary jane,” “hit
acid,” “pop pilz,” etc. and that these bigrams would increase
classification accuracy

Algorithm

Supervised Machine Learning
•Input- text message corpus.
•Bigrams were selected as features.
•System was trained utilizing NLTK’s implementation of the Naïve
Bayes classifier .
•It was hypothesized that a smaller training set might increase the
accuracy.

Application’s of Mobile Forensics
• Makes SMS analysis techniques highly applicable to Twitter
“tweet” analysis.
• It is useful for corporate investigation, criminal and civil
defense.
• Useful for law enforcement investigators to analyze Social
Media Profile for evidence of criminal activity

Conclusion
•Natural language processing and machine classification
have been applied to mobile device forensic analysis in a
unique way
•Text message classification and are free to develop a
better methodology using the text message corpus.
•Develop the more efficient corpora, it has been made
available to the research community

Future Work
•We can overcome on the “micro-text” problem by using more
efficient feature extraction techniques
•Future research recommendations include determination of the
frequency of text messaging between criminal suspects
•Calculating the average time span between sent and received
messages in text message conversation threads.

References
1. Daniel R. O’Day and Ricardo A. Calix“TEXT MESSAGE CORPUS: APPLYING NATURAL
LANGUAGE PROCESSING TO MOBILE DEVICE FORENSICS”, Purdue University
Calumet, 2200 169th Street, Hammond, IN, 46323, USA
2. D. Phuc and N.T.K. Phung, “Using Naïve Bayes model and natural language
processing for classifying messages on online forum,” 2007 IEEE International
Conference on Research, Innovation and Vision for the Future, pp. 247-252, March
2007
3. A.Smith. “Americans and Text Messaging”. 2011.[Online]. http://pewinternet.org
media/Files/Reports/2011/Americans %20and%20Text%20Messaging.pdf
4. B. Carrier, “File System Forensic Analysis”. Boston,MA: Addison-Wesley, 2005, p. 8.
5. C. Altheide and H. Carvey, “Digital Forensics With Open Source Tools”, Waltham,
MA: Syngress, 2011.
6. S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python: Analyzing
Text with the Natural Language Toolkit. Sebastopol, CA: O’Reilly Media, 2009, pp.
221-255.

Thank you!
8/24/2015
Department of Computer Science and
Engineering
26

MOBILE DEVICE FORENSICS USING NLP

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