testwithMyapp.lua

-- Example of how to test using a pre-trained network
-- Expects a directory containing two or more directories
-- One directory contains all the malware
-- The other directory contains all the benign software

-- given a model that has already been trained
-- and a directory containing programs - classify into malware / benign

require 'nn'
require 'optim'
require 'nngraph'
require 'cunn'
require 'cutorch'

require 'readMalwareData'


cmd = torch.CmdLine()
cmd:option('-useCUDA',false,'use CUDA optimisation')
cmd:option('-dataDir','./malwareDataset/','directory with your own app')
cmd:option('-modelPath','./trainedNets/model.th7','path to model to use for testing')
opt = cmd:parse(arg)

print('loading model from disk')
savedModel = torch.load(opt.modelPath)
print('loaded model')
print(savedModel.trainedModel)

-- we need these values to correctly prepare the files when reading from disk
opt.programLen = savedModel.opt.programLen
opt.kernelLength = savedModel.opt.kernelLength
opt.maxSequenceLength = savedModel.opt.maxSequenceLength

print('reading data from disk')

	
if opt.useCUDA then
	savedModel.trainedModel:cuda()
end
savedModel.trainedModel:evaluate()

print('starting test')

model = savedModel.trainedModel

print('testing my app')
local currProgramPtr = 1
--print(opt.dataDir)
local contents = readfileFunc_tensor(opt.dataDir)
local currProgramLen = contents:size(1)

currProgramPtr = torch.Tensor(currProgramPtr)


--print(contents)

--print('currProgramLen:',currProgramLen)
--print('currProgramLen type:',type(currProgramLen))
local valBatch = torch.zeros(1,currProgramLen)
--print('valBatch is:',valBatch)
valBatch[{{1},{}}] = contents[{{currProgramPtr,currProgramPtr + currProgramLen - 1}}]

local netOutput = model:forward(valBatch)
 		
local netOutputProb = nn.Exp():forward(netOutput:double())

local v,i = torch.max(netOutputProb,2)
local pred = i[{1,1}]
print('pred:',pred)
if pred == 1 then
	print('this is a benign apk')
else
	print('this is a malware apk')
end