import numpy as np from scipy.cluster.hierarchy import linkage, fcluster, to_tree # A helper class for KitNET which performs a correlation-based incremental clustering of the dimensions in X # n: the number of dimensions in the dataset # For more information and citation, please see our NDSS'18 paper: Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection class corClust: def __init__(self,n): #parameter: self.n = n #varaibles self.c = np.zeros(n) #linear num of features self.c_r = np.zeros(n) #linear sum of feature residules self.c_rs = np.zeros(n) #linear sum of feature residules self.C = np.zeros((n,n)) #partial correlation matrix self.N = 0 #number of updates performed # x: a numpy vector of length n def update(self,x): self.N += 1 self.c += x c_rt = x - self.c/self.N self.c_r += c_rt self.c_rs += c_rt**2 self.C += np.outer(c_rt,c_rt) # creates the current correlation distance matrix between the features def corrDist(self): c_rs_sqrt = np.sqrt(self.c_rs) C_rs_sqrt = np.outer(c_rs_sqrt,c_rs_sqrt) C_rs_sqrt[C_rs_sqrt==0] = 1e-100 #this protects against dive by zero erros (occurs when a feature is a constant) D = 1-self.C/C_rs_sqrt #the correlation distance matrix D[D<0] = 0 #small negatives may appear due to the incremental fashion in which we update the mean. Therefore, we 'fix' them return D # clusters the features together, having no more than maxClust features per cluster def cluster(self,maxClust): D = self.corrDist() Z = linkage(D[np.triu_indices(self.n, 1)]) # create a linkage matrix based on the distance matrix if maxClust < 1: maxClust = 1 if maxClust > self.n: maxClust = self.n map = self.__breakClust__(to_tree(Z),maxClust) return map # a recursive helper function which breaks down the dendrogram branches until all clusters have no more than maxClust elements def __breakClust__(self,dendro,maxClust): if dendro.count <= maxClust: #base case: we found a minimal cluster, so mark it return [dendro.pre_order()] #return the origional ids of the features in this cluster return self.__breakClust__(dendro.get_left(),maxClust) + self.__breakClust__(dendro.get_right(),maxClust) # Copyright (c) 2017 Yisroel Mirsky # # MIT License # # 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.