Kitsune.py

from FeatureExtractor import *
from KitNET.KitNET import KitNET
import numpy as np

# MIT License
#
# Copyright (c) 2018 Yisroel mirsky
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

class Kitsune:
    def __init__(self,file_path,limit,max_autoencoder_size=10,FM_grace_period=None,AD_grace_period=10000,learning_rate=0.1,hidden_ratio=0.75,num_features=420):
        #init packet feature extractor (AfterImage)
        self.FE = FE(file_path,limit,num_features)
        #init Kitnet
        self.AnomDetector = KitNET(self.FE.get_num_features(),max_autoencoder_size,FM_grace_period,AD_grace_period,learning_rate,hidden_ratio)

    def proc_next_packet(self):
        # create feature vector
        x = self.FE.get_next_vector()

        if len(x) == 0:
            return -1 #Error or no packets left

        # process KitNET
        return self.AnomDetector.process(x)  # will train during the grace periods, then execute on all the rest.

    def get_feature_list(self, csv=False, single=False, kind=1):
        vectorList = self.FE.get_all_vectors(csv, single, kind=kind)
        return vectorList

    def feed_batch(self, data):
        resultList = []
        count = 0
        for instance in data:
            if count % 1000 == 0:
                print("processing packet ", count, " / ", len(data))
            resultList.append(self.AnomDetector.process(instance))
            count += 1
        return np.array(resultList)

    def giveMeTheKit(self):
        return self.AnomDetector