基于机器学习SVM实现的网络入侵检测系统python源码+详细注释(机器学习作业).zip

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.feature_selection import RFECV from sklearn.model_selection import StratifiedKFold from sklearn.svm import SVC col_names = [ "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", "label"] protocol_type_list = ['tcp', 'udp', 'icmp'] service_list = ['aol', 'auth', 'bgp', 'courier', 'csnet_ns', 'ctf', 'daytime', 'discard', 'domain', 'domain_u', 'echo', 'eco_i', 'ecr_i', 'efs', 'exec', 'finger', 'ftp', 'ftp_data', 'gopher', 'harvest', 'hostnames','http', 'http_2784', 'http_443', 'http_8001', 'imap4', 'IRC', 'iso_tsap', 'klogin', 'kshell', 'ldap', 'link', 'login', 'mtp', 'name', 'netbios_dgm', 'netbios_ns', 'netbios_ssn', 'netstat', 'nnsp', 'nntp', 'ntp_u', 'other', 'pm_dump', 'pop_2', 'pop_3', 'printer', 'private', 'red_i', 'remote_job', 'rje', 'shell', 'smtp', 'sql_net', 'ssh', 'sunrpc', 'supdup', 'systat', 'telnet', 'tftp_u', 'tim_i', 'time', 'urh_i', 'urp_i', 'uucp', 'uucp_path', 'vmnet', 'whois', 'X11', 'Z39_50'] flag_list = ['OTH', 'REJ', 'RSTO', 'RSTOS0', 'RSTR', 'S0', 'S1', 'S2', 'S3', 'SF', 'SH'] label_list = ['normal.', 'buffer_overflow.', 'loadmodule.', 'perl.', 'neptune.', 'smurf.', 'guess_passwd.', 'pod.', 'teardrop.', 'portsweep.', 'ipsweep.', 'land.', 'ftp_write.', 'back.', 'imap.', 'satan.', 'phf.', 'nmap.', 'multihop.', 'warezmaster.', 'warezclient.', 'spy.', 'rootkit.'] # 获取文件路径 def get_file_path(data_loc_path, file_name, file_type): return '{}/{}.{}'.format(data_loc_path, file_name, file_type) # 将读入的数据存取为csv文件 def read_file_to_csv(data_loc_path, from_file_name, to_file_name): from_path = data_loc_path + '/{}'.format(from_file_name) to_path = data_loc_path + '/{}'.format(to_file_name) print("# 开始读取文件") data = pd.read_csv(from_path, header=None, names=col_names) print(data[:2]) print("# 成功读取文件，开始转换为CSV") data.to_csv(to_path, columns=None, index=False) print("# 转换完成") # 查找字符串在数组中的下标 def find_index(string, array): for i in range(len(array)): if string == array[i]: return i # 数据标准化计算 def compute_regression_value(val, std, mean): if std == 0 or mean == 0: return 0 return (val - mean) / std # 数据归一化计算 def compute_normalization_value(val, min_val, max_val): if min_val == max_val or val == min_val: return 0 return (val - min_val) / (max_val - min_val) # 数据处理 def process_data(file_path): # 读取数据 data_frame = pd.read_csv(file_path) data_num = data_frame.shape[0] # 将dataFrame转换为list print("# 准备开始数据无量纲化") data_list = data_frame.values.tolist() ''' 1.数据无量纲化： 将数据类型统一以便后续操作 ''' print("# 准备完成, 开始数据无量纲化") # 遍历数组将字符改为数值 for idx in range(len(data_list)): row = data_list[idx] # 将数据中字符转化为数字 row[1] = find_index(row[1], protocol_type_list) row[2] = find_index(row[2], service_list) row[3] = find_index(row[3], flag_list) row[41] = find_index(row[41], label_list) print("## 已完成{}%".format((idx / data_num) * 100)) print("# 数据无量纲化完成, 正在处理数据...") # 转化为DataFrame data_frame = pd.DataFrame(data_list, columns=col_names) print("# 数据处理完成") print(data_list[:3]) ''' 2.数据标准化 x' = [x-mean(x)] / std(x) ** 此处理需要将最后一列排除 ** ''' print("# 准备开始数据标准化") # 计算每一列的标准差 std = data_frame.std(axis='rows').tolist() # 计算每一列的均值 mean = data_frame.mean(axis='rows').tolist() # 将dataFrame转换为list data_list = data_frame.values.tolist() print("# 准备完成, 开始数据标准化") # 遍历数组并计算 for idx in range(len(data_list)): row = data_list[idx] for col_idx in range(len(row)-1): row[col_idx] = abs(compute_regression_value(row[col_idx], std[col_idx], mean[col_idx])) print("## 已完成{}%".format((idx / data_num) * 100)) print("# 数据标准化完成, 正在处理数据...") data_frame = pd.DataFrame(data_list, columns=col_names) print("# 处理完成") ''' 3.数据归一化 x' = (x-x_min) / (x_max-x_min) ** 此处理需要将最后一列排除 ** ''' # 计算每一列的最小值和最大值 print("# 准备开始数据归一化") min_val = data_frame.min(axis='rows').tolist() max_val = data_frame.max(axis='rows').tolist() data_list = data_frame.values.tolist() print("# 准备完成, 开始数据归一化") for idx in range(len(data_list)): row = data_list[idx] for col_idx in range(len(row)-1): row[col_idx] = compute_normalization_value(row[col_idx], min_val[col_idx], max_val[col_idx]) print("## 已完成{}%".format((idx / data_num) * 100)) print("# 数据归一化完成, 正在处理数据...") data_frame = pd.DataFrame(data_list, columns=col_names) print("# 处理完成") return data_frame # 特征选择(递归消除法,返回值:处理后的数据、新的数据标签) def recursive_feature_elimination(data_frame): print("# 准备开始特征选择, 正在准备数据") # 获取X和y的特征名 x_col_names = col_names x_col_names.remove("label") y_col_name = "label" # 把data分割成X和y X = data_frame[x_col_names] y = data_frame[y_col_name] model = SVC(kernel="linear") print("# 准备完成, 开始特征选择") rfe = RFECV(estimator=model, step=1, cv=StratifiedKFold(2)) rfe = rfe.fit(X, y) feature_new = X.columns.values[rfe.support_] X_new = X[feature_new] print("特征选择完成") # 将两部分合并返回 return pd.concat([X_new, y], axis=1), feature_new