delete.py

import pandas as pd
data = "KDDTest.csv"

df = pd.read_csv(data, header=None)

columns = ['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'outcome', 'difficulty']
df.columns = columns

# A list ot attack names that belong to each general attack type
dos_attacks=["snmpgetattack","back","land","neptune","smurf","teardrop","pod","apache2","udpstorm","processtable","mailbomb"]
r2l_attacks=["snmpguess","worm","httptunnel","named","xlock","xsnoop","sendmail","ftp_write","guess_passwd","imap","multihop","phf","spy","warezclient","warezmaster"]
u2r_attacks=["sqlattack","buffer_overflow","loadmodule","perl","rootkit","xterm","ps"]
probe_attacks=["ipsweep","nmap","portsweep","satan","saint","mscan"]

# Helper function to label samples to 5 classes
def label_attack (row):
    if row["outcome"] in dos_attacks: return "dos"
    if row["outcome"] in r2l_attacks: return "r2l"
    if row["outcome"] in u2r_attacks: return "u2r"
    if row["outcome"] in probe_attacks: return "probe"
    return "normal"

df["Class"]=df.apply(label_attack,axis=1)

# The old outcome field is dropped since it was replaced with the Class field, the difficulty field will be dropped as well.
df=df.drop("outcome",axis=1)
df=df.drop("difficulty",axis=1)

df1 = df[['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment', 'logged_in', 'count', 'srv_count', 'Class']]
print(df1.head())

df1.to_csv('file1.csv')

'''
res = df1.loc[df['Class']=='dos']
x = res.values
for i in range(5):
    print(x[np.random.randint(1000)])
'''