"imap", "ipsweep", "land", "loadmodule", "multihop",
"neptune", "nmap", "normal", "perl", "phf", "pod",
"portsweep", "rootkit", "satan", "smurf", "spy",
"teardrop", "warezclient", "warezmaster")
colormaps = [
"b", "g", "r", "c", "m", "k", "w", "0.20", "0.75", "#eeefff",
"#000fff", "#235234", "#345454", "#5766723", "#263543", "#078787",
"#567576", "#745655", "#958673", "#262434", "#dd2453", "#eee253",
"#fff332"
]
import time
start = time.time()
df, headers, gmms = preprocessing.get_preprocessed_data()
df = df[0:100]
df_train = copy.deepcopy(df)
df_train.drop('attack', 1, inplace=True)
df_train.drop('difficulty', 1, inplace=True)
print "reductioning..."
proj = reduction.gmm_reduction(df_train, headers, gmms)
A = affinity.get_affinity_matrix(proj,
metric_method=distance.cosdist,
knn=5)
D = affinity.get_degree_matrix(A)
print A
import sugarbee.affinity as affinity
import sugarbee.solver as solver
import scipy.sparse as sparse
import scipy.sparse.csgraph as csgraph
#def assign_undirected_weight(W, i, j, v):
# W[i,j] = W[j,i] = v
if __name__ == '__main__':
import time
start = time.time()
datasize = 1000
print "preprocessing data..."
df, headers = preprocessing.get_preprocessed_data(datasize)
df_train = copy.deepcopy(df)
df_train.drop('attack', 1, inplace=True)
df_train.drop('difficulty', 1, inplace=True)
print "normal"
print len(df[df["attack"] == 11])
print "abnormal"
print len(df[df["attack"] != 11])
print "data reduction..."
proj = reduction.reduction(df_train, n_components=1)
print "graph generation..."
A = affinity.get_affinity_matrix(proj,
metric_method=distance.gaussian,
knn=200)
import sugarbee.affinity as affinity
import sugarbee.solver as solver
import scipy.sparse as sparse
import scipy.sparse.csgraph as csgraph
#def assign_undirected_weight(W, i, j, v):
# W[i,j] = W[j,i] = v
if __name__ == '__main__':
import time
start = time.time()
datasize = 1000
print "preprocessing data..."
df, headers = preprocessing.get_preprocessed_data(datasize)
df_train = copy.deepcopy(df)
df_train.drop('attack',1,inplace=True)
df_train.drop('difficulty',1,inplace=True)
print "normal"
print len(df[df["attack"] == 11])
print "abnormal"
print len(df[df["attack"] != 11])
print "data reduction..."
proj = reduction.reduction(df_train, n_components=1)
print "graph generation..."
A = affinity.get_affinity_matrix(proj, metric_method=distance.gaussian,knn=200)
# A = affinity.get_affinity_matrix(proj, metric_method=distance.dist, metric_param='euclidean', knn=8)
# A = affinity.get_affinity_matrix(proj, metric_method=distance.dist, metric_param='manhattan', knn=8)
if __name__ == '__main__':
attack_names = ("back","buffer_overflow","ftp_write","guess_passwd","imap",
"ipsweep","land","loadmodule","multihop","neptune",
"nmap","normal","perl","phf","pod",
"portsweep","rootkit","satan","smurf","spy",
"teardrop","warezclient","warezmaster")
colormaps = ["b","g","r","c","m","k","w","0.20","0.75","#eeefff",
"#000fff","#235234","#345454","#5766723","#263543","#078787","#567576","#745655","#958673","#262434",
"#dd2453","#eee253","#fff332"]
import time
start = time.time()
df, headers, gmms = preprocessing.get_preprocessed_data()
df = df[0:100]
df_train = copy.deepcopy(df)
df_train.drop('attack',1,inplace=True)
df_train.drop('difficulty',1,inplace=True)
print "reductioning..."
proj = reduction.gmm_reduction(df_train, headers, gmms)
A = affinity.get_affinity_matrix(proj, metric_method=distance.cosdist, knn=5)
D = affinity.get_degree_matrix(A)
print A
elapsed = (time.time() - start)
