Team Name: DAYS

Team Members: Shubhi Jain, Sharmin Pathan, Yash Shrivastava, Dharamendra Kumar

Malware Classification

Classification Strategy: Random Forest classifier

Technologies Used:

• Python 2.7
• Apache Spark 2.0
• Resilient Distributed Datasets (RDDs)
• Dataframes
• Django API
• PyParsing API

Preprocessing of Byte File:

• The hash files on S3 storage were read from "https://s3.amazonaws.com/eds-uga-csci8360/data/project2/metadata/"
• The next step was to read the label file
• Create a pair resulting from hash file and label file
• Read the contents of all files specified in the training set
• Cleaning of the .bytes files with the following parser (i) removal of pointers (ii) removal of special characters (??)
• Added content to corresponding hash file and label file
• Cleaned data is saved into a parquet file.
• The parquet file is accessed everytime the data is to be read.

Preprocessing of Opcodes from .asm files:

• Two librabries, namely Django and pyparsing have been used for the preporcessing.
• Django: Django is a python library to facilitate rapid development and pragmatic design. It has been used for the removal of special symbols from the opcodes.
• pyparsing: It's a python library for constructing and executing simple grammar. This has been used for opcode extraction.

Flow:

• N-grams are generated from the preprocessed byte file and opcodes.
• Convert the N-grams of the byte file and opcodes to vectors of token counts.
• These vectors are brought together by the Vector Assembler.
• The data is then fed to Random Forest Classifier.
• The prediction is computed.

Tuning the accuracy:

• 1,2,3,4 grams of byte file and opcodes were generated in order to test which provides a better accuracy.
• Tuning of the parameters of Random Forest classifier, namely maxDepth, no. of trees, maxBin.

Challenges Faced:

• Memory issues in cluster (Out-of-memory issue)
• Reading parquet file in cluster
• Tuning of Random Forest Classifier in order to increase accuracy