MapReduce 实践题：Web 访问日志分析与异常检测

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作业描述

MapReduce 实践题：Web 访问日志分析与异常检测

题目背景

你被要求设计和实现一个基于 MapReduce 的大规模 Web 访问日志分析与异常检测系统。该系统的目标是从每日数百万条访问日志中提取有用的信息，并检测出潜在的异常访问行为。访问日志文件格式如下：

127.0.0.1 - - [10/Oct/2021:13:55:36 -0700] "GET /index.html HTTP/1.1" 200 1043
192.168.0.1 - - [10/Oct/2021:13:56:12 -0700] "POST /login HTTP/1.1" 200 2326
...

数据集说明

• IP 地址：例如，127.0.0.1。
• 时间戳：例如，[10/Oct/2021:13:55:36 -0700]。
• 请求方法：例如，"GET" 或 "POST"。
• 请求 URL：例如，"/index.html"。
• HTTP 响应码：例如，200、404 或 500。
• 响应大小：例如，1043。

任务要求

1. 数据预处理：
   • 解析每条日志记录，提取以下字段：IP 地址、请求时间、请求方法、请求 URL、HTTP 响应码、响应大小。
   • 将解析后的数据格式化为结构化格式（例如，JSON）。
2. 访问统计：
   • 统计每个 IP 地址在一天中的访问次数。
   • 统计每个请求 URL 在一天中的访问次数。
3. 异常检测：
   • 检测异常高的访问频率：对于每个 IP 地址，计算访问次数的平均值和标准差，标记访问次数超过均值加三倍标准差的 IP 地址。
   • 检测潜在的恶意请求：检测 HTTP 响应码为 4xx 和 5xx 的请求，统计每个 IP 地址的异常请求次数，并标记异常请求次数占总请求次数比例超过 20% 的 IP 地址。
4. 结果输出：
   • 输出访问统计结果：每个 IP 地址的访问次数，每个请求 URL 的访问次数。
   • 输出异常检测结果：异常高访问频率的 IP 地址及其访问次数，潜在的恶意请求 IP 地址及其异常请求次数和总请求次数的比例。

输入数据示例

127.0.0.1 - - [10/Oct/2021:13:55:36 -0700] "GET /index.html HTTP/1.1" 200 1043
192.168.0.1 - - [10/Oct/2021:13:56:12 -0700] "POST /login HTTP/1.1" 200 2326
...

输出数据示例

访问统计结果：

IP访问次数:
127.0.0.1  150
192.168.0.1  200URL访问次数:
/index.html  300
/login  400

异常检测结果：

异常高访问频率 IP:
192.168.0.1  1200潜在恶意请求 IP:
127.0.0.1  50  25.0%

实现步骤

1. 数据预处理 Mapper：
   • 解析日志记录，提取必要字段并输出结构化数据。
2. 访问统计 Mapper 和 Reducer：
   • Mapper：统计每个 IP 地址和每个 URL 的访问次数。
   • Reducer：汇总每个 IP 地址和每个 URL 的访问次数。
3. 异常检测 Mapper 和 Reducer：
   • Mapper：计算每个 IP 地址的访问次数，检测 HTTP 响应码为 4xx 和 5xx 的请求。
   • Reducer：计算每个 IP 地址访问次数的均值和标准差，标记异常高访问频率的 IP 地址；统计每个 IP 地址的异常请求次数并计算异常请求比例，标记潜在的恶意请求 IP 地址。

解题思路

1. 数据预处理 Mapper：解析日志记录，提取必要字段并输出结构化数据。
2. 访问统计 Mapper 和 Reducer：统计每个 IP 地址和每个 URL 的访问次数。
3. 异常检测 Mapper 和 Reducer：计算每个 IP 地址的访问次数，检测 HTTP 响应码为 4xx 和 5xx 的请求。
4. 主方法：设置三个 MapReduce 作业：数据预处理、访问统计和异常检测。

1. 数据预处理

PreprocessMapper

package org.example.mapreduce.t1;import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;/*** @author 撕得失败的标签* @version 1.0* @description: 数据预处理* @date 2024/6/22 22:35*/public class PreprocessMapper extends Mapper<LongWritable, Text, Text, Text> {/*** @description:* 1. 解析每条日志记录，提取以下字段：IP 地址、请求时间、请求方法、请求 URL、HTTP 响应码、响应大小。* 2. 将解析后的数据格式化为结构化格式（例如，JSON）。* @author 撕得失败的标签* @date 2024/6/23 11:09*/@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {String line = value.toString();String[] strings = line.split(" ");if (strings.length == 10) {// 提取匹配到的字段String ipAddress = strings[0];String timestamp = strings[3] + " " + strings[4];String requestMethod = strings[5];String requestUrl = strings[6];String httpStatusCode = strings[8];String responseSize = strings[9];context.write(new Text(ipAddress), new Text(timestamp + "," + requestMethod + "," + requestUrl + "," + httpStatusCode + "," + responseSize));}}
}

2. 访问统计

AccessStatistics

package org.example.mapreduce.t1;import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.LongWritable;import java.io.IOException;/*** @author 撕得失败的标签* @version 1.0* @description: 访问统计* @date 2024/6/22 22:55*/
public class AccessStatistics {/*** @description:* 1. 统计每个 IP 地址在一天中的访问次数。* 2. 统计每个请求 URL 在一天中的访问次数。* @author 撕得失败的标签* @date 2024/6/23 11:08*/public static class Map extends Mapper<LongWritable, Text, Text, LongWritable> {@Overrideprotected void map(LongWritable key, Text value, Context context) throws InterruptedException, IOException {String line = value.toString();String[] strings = line.split(" ");// 统计一天的，以 20/Jun/2024 为例if (strings[3].contains("20/Jun/2024")) {// IPcontext.write(new Text(strings[0]), new LongWritable(1));// URLcontext.write(new Text(strings[6]), new LongWritable(1));}}}public static class Reduce extends Reducer<Text, LongWritable, Text, LongWritable> {@Overrideprotected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {long sum = 0;for (LongWritable value : values) {sum += value.get();}context.write(key, new LongWritable(sum));}}
}

3. 异常检测

AnomalyDetection

package org.example.mapreduce.t1;import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.LongWritable;import java.io.IOException;
import java.util.HashMap;/*** @author 撕得失败的标签* @version 1.0* @description: 异常检测* @date 2024/6/23 11:08*/
public class AnomalyDetection {/*** @description:* 1. 检测异常高的访问频率：对于每个 IP 地址，计算访问次数的平均值和标准差，标记访问次数超过均值加三倍标准差的 IP 地址。* 2. 检测潜在的恶意请求：检测 HTTP 响应码为 4xx 和 5xx 的请求，*    统计每个 IP 地址的异常请求次数，*    并标记异常请求次数占总请求次数比例超过 20% 的 IP 地址。* @author 撕得失败的标签* @date 2024/6/23 11:08*/public static class Map extends Mapper<LongWritable, Text, Text, LongWritable> {@Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {String[] strings = value.toString().split(" ");String ip = strings[0];context.write(new Text(ip), new LongWritable(1));String httpStatusCode = strings[8];if (httpStatusCode.startsWith("4") || httpStatusCode.startsWith("5")) {String anomaly = "+" + ip;context.write(new Text(anomaly), new LongWritable(1));}}}public static class Reduce extends Reducer<Text, LongWritable, Text, LongWritable> {private final HashMap<String, Long> ipToCount = new HashMap<String, Long>();private final HashMap<String, Long> ipToAnomalyCount = new HashMap<String, Long>();@Overrideprotected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {long sum = 0;for (LongWritable value : values) {sum += value.get();}
//            context.write(key, new LongWritable(sum));String ip = key.toString();if (ip.startsWith("+")) {ip = ip.substring(1);ipToAnomalyCount.put(ip, sum);}ipToCount.put(ip, sum);}@Overrideprotected void cleanup(Context context) throws IOException, InterruptedException {// 实现异常检测的逻辑long sum = 0;for (String k : ipToCount.keySet()) {sum += ipToCount.get(k);}double avg = (double) (sum / ipToCount.size());double std = 0;for (String k : ipToCount.keySet()) {std += Math.pow(ipToCount.get(k) - avg, 2);}// 异常高访问频率 IPfor (String k : ipToCount.keySet()) {if (ipToCount.get(k) > avg + 3 * std) {context.write(new Text(k), new LongWritable(ipToCount.get(k)));}}// 潜在恶意请求 IPfor (String k : ipToAnomalyCount.keySet()) {double anomaly = (double) ipToAnomalyCount.get(k) / ipToCount.get(k);if (anomaly > 0.2) {context.write(new Text(k + "\t" + String.format("%.1f", anomaly * 100) + "%"), new LongWritable(ipToAnomalyCount.get(k)));}}}}
}

4. 主方法

Main

package org.example.mapreduce.t1;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import java.io.IOException;
import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.util.LinkedList;
import java.util.List;/*** @author 撕得失败的标签* @version 1.0* @description: 主方法* @date 2024/6/22 22:34*/
public class Main {public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {// 创建配置信息Configuration conf = new Configuration();conf.set("fs.default.name","hdfs://hadoop102:9000");// 1. 数据预处理 PreprocessMapperJob preprocessJob = Job.getInstance(conf, "preprocess job");preprocessJob.setJarByClass(Main.class);preprocessJob.setMapperClass(PreprocessMapper.class);preprocessJob.setOutputKeyClass(Text.class);preprocessJob.setOutputValueClass(Text.class);FileInputFormat.addInputPath(preprocessJob, new Path("/m1"));FileSystem fs = FileSystem.get(conf);Path outPath = new Path("/t1/preprocess");if(fs.exists(outPath)) {fs.delete(outPath, true);}FileOutputFormat.setOutputPath(preprocessJob, outPath);preprocessJob.waitForCompletion(true);// 2. 访问统计 AccessStatisticsJob accessStatisticsJob = Job.getInstance(conf, "access statistics job");accessStatisticsJob.setJarByClass(Main.class);accessStatisticsJob.setMapperClass(AccessStatistics.Map.class);accessStatisticsJob.setReducerClass(AccessStatistics.Reduce.class);accessStatisticsJob.setOutputKeyClass(Text.class);accessStatisticsJob.setOutputValueClass(LongWritable.class);FileInputFormat.addInputPath(accessStatisticsJob, new Path("/m1"));FileSystem fs1 = FileSystem.get(conf);Path outPath1 = new Path("/t1/statistics");if(fs1.exists(outPath1)) {fs1.delete(outPath1, true);}FileOutputFormat.setOutputPath(accessStatisticsJob, outPath1);accessStatisticsJob.waitForCompletion(true);// 3. 异常检测 AnomalyDetectionJob anomalyDetectionJob = Job.getInstance(conf, "anomaly detection job");anomalyDetectionJob.setJarByClass(Main.class);anomalyDetectionJob.setMapperClass(AnomalyDetection.Map.class);anomalyDetectionJob.setReducerClass(AnomalyDetection.Reduce.class);anomalyDetectionJob.setOutputKeyClass(Text.class);anomalyDetectionJob.setOutputValueClass(LongWritable.class);FileInputFormat.addInputPath(anomalyDetectionJob, new Path("/m1"));FileSystem fs2 = FileSystem.get(conf);Path outPath2 = new Path("/t1/anomaly");if(fs2.exists(outPath2)) {fs2.delete(outPath2, true);}FileOutputFormat.setOutputPath(anomalyDetectionJob, outPath2);anomalyDetectionJob.waitForCompletion(true);// 4. 输出结果 Output// 访问统计结果：FileSystem fs3 = FileSystem.get(conf);Path outPath3 = new Path("/t1/statistics/part-r-00000");BufferedReader br = new BufferedReader(new InputStreamReader(fs3.open(outPath3)));List<String> ip = new LinkedList<String>();List<String> url = new LinkedList<String>();String line;while ((line = br.readLine()) != null) {if (line.startsWith("/")) {url.add(line);} else {ip.add(line);}}// IP访问次数:System.out.println("\nIP访问次数:");for (String s : ip) {System.out.println(s);}// URL访问次数:System.out.println("\nURL访问次数:");for (String s : url) {System.out.println(s);}// 异常检测结果：FileSystem fs4 = FileSystem.get(conf);Path outPath4 = new Path("/t1/anomaly/part-r-00000");BufferedReader br1 = new BufferedReader(new InputStreamReader(fs4.open(outPath4)));List<String> potential = new LinkedList<String>();List<String> anomaly = new LinkedList<String>();String line1;while ((line1 = br1.readLine()) != null) {String[] strings = line1.split("\t");if (strings.length == 2) {anomaly.add(line1);} else {potential.add(line1);}}// 异常高访问频率 IP:System.out.println("\n异常高访问频率 IP:");if (anomaly.size() == 0) {System.out.println("无");} else {for (String s : anomaly) {System.out.println(s);}}// 潜在恶意请求 IP:System.out.println("\n潜在异常高访问频率 IP:");if (potential.size() == 0) {System.out.println("无");} else {for (String s : potential) {String[] strings = s.split("\t");System.out.println(strings[0] + "\t" + strings[2] + "\t" + strings[1]);}}}
}

5. 结果输出

IP访问次数:
10.0.0.1	334003
10.0.0.2	334350
10.0.0.3	333056
10.0.0.4	333947
10.0.0.5	333263
127.0.0.1	332347
127.0.0.2	333025
127.0.0.3	332450
127.0.0.4	333005
127.0.0.5	333428
192.168.0.1	334054
192.168.0.2	332883
192.168.0.3	333681
192.168.0.4	333133
192.168.0.5	333375URL访问次数:
/cart	713975
/checkout	713453
/contact	715382
/home.html	712570
/index.html	715544
/login	714255
/products	714821异常高访问频率 IP:
无潜在异常高访问频率 IP:
192.168.0.2	222498	66.8%
192.168.0.1	222165	66.5%
127.0.0.5	221778	66.5%
192.168.0.4	222096	66.7%
127.0.0.4	222156	66.7%
192.168.0.3	222227	66.6%
192.168.0.5	222070	66.6%
10.0.0.4	222243	66.6%
10.0.0.3	221966	66.6%
10.0.0.5	222347	66.7%
10.0.0.2	222664	66.6%
10.0.0.1	222493	66.6%
127.0.0.3	221464	66.6%
127.0.0.2	222197	66.7%
127.0.0.1	221702	66.7%Process finished with exit code 0

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