DoS事件日志对Conditions基于速率检测属性值修正
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@@ -193,7 +193,7 @@ public class DosDetection extends RichMapFunction<DosSketchLog, DosEventLog> {
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switch (type) {
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case STATIC_CONDITION_TYPE:
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return new StrBuilder()
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.append(tag).append(" > ")
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.append("Rate > ")
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.append(base).append(" ")
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.append(tag).append("/s")
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.toString();
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@@ -1,6 +1,8 @@
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package com.zdjizhi.utils;
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import com.zdjizhi.common.CommonConfig;
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import org.apache.flink.streaming.api.CheckpointingMode;
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import org.apache.flink.streaming.api.environment.CheckpointConfig;
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import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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@@ -12,6 +14,33 @@ public class FlinkEnvironmentUtils {
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static {
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streamExeEnv.setParallelism(CommonConfig.STREAM_EXECUTION_ENVIRONMENT_PARALLELISM);
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/*
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// 每 1000ms 开始一次 checkpoint
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streamExeEnv.enableCheckpointing(CommonConfig.FLINK_WINDOW_MAX_TIME * 1000);
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// 设置模式为精确一次 (这是默认值)
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streamExeEnv.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
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// 确认 checkpoints 之间的时间会进行 500 ms
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streamExeEnv.getCheckpointConfig().setMinPauseBetweenCheckpoints(500);
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// Checkpoint 必须在一分钟内完成,否则就会被抛弃
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streamExeEnv.getCheckpointConfig().setCheckpointTimeout(60000);
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// 允许两个连续的 checkpoint 错误
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streamExeEnv.getCheckpointConfig().setTolerableCheckpointFailureNumber(2);
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// 同一时间只允许一个 checkpoint 进行
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streamExeEnv.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
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// 使用 externalized checkpoints,这样 checkpoint 在作业取消后仍就会被保留
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streamExeEnv.getCheckpointConfig().enableExternalizedCheckpoints(
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CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
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// 开启实验性的 unaligned checkpoints
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streamExeEnv.getCheckpointConfig().enableUnalignedCheckpoints();
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*/
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}
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}
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@@ -22,12 +22,14 @@ public class KafkaUtils {
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}
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public static FlinkKafkaProducer<String> getKafkaSink(String topic){
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return new FlinkKafkaProducer<String>(
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FlinkKafkaProducer<String> kafkaProducer = new FlinkKafkaProducer<>(
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topic,
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new SimpleStringSchema(),
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getKafkaSinkProperty(),
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Optional.empty()
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);
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kafkaProducer.setLogFailuresOnly(true);
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return kafkaProducer;
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}
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}
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@@ -15,7 +15,7 @@ kafka.input.topic.name=DOS-SKETCH-RECORD
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kafka.input.bootstrap.servers=192.168.44.11:9094,192.168.44.14:9094,192.168.44.15:9094
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#读取kafka group id
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kafka.input.group.id=2112080949
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kafka.input.group.id=dos-detection-job-220516-1
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#kafka.input.group.id=dos-detection-job-210813-1
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#发送kafka metrics并行度大小
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@@ -37,8 +37,8 @@ kafka.output.bootstrap.servers=192.168.44.12:9094
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#kafka.output.bootstrap.servers=192.168.44.11:9092,192.168.44.14:9092,192.168.44.15:9092
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#zookeeper地址
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hbase.zookeeper.quorum=192.168.44.12:2181
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#hbase.zookeeper.quorum=192.168.44.11:2181,192.168.44.14:2181,192.168.44.15:2181
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#hbase.zookeeper.quorum=192.168.44.12:2181
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hbase.zookeeper.quorum=192.168.44.11:2181,192.168.44.14:2181,192.168.44.15:2181
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#hbase客户端处理时间
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hbase.client.operation.timeout=30000
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@@ -74,12 +74,12 @@ destination.ip.partition.num=10000
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data.center.id.num=15
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#IP mmdb库路径
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ip.mmdb.path=D:\\data\\dat\\
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ip.mmdb.path=D:\\data\\dat\\bak\\
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#ip.mmdb.path=/home/bigdata/topology/dat/
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#ip.mmdb.path=/home/bigdata/wlh/topology/dos-detection/dat/
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#静态敏感阈值,速率小于此值不报警
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static.sensitivity.threshold=500
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static.sensitivity.threshold=10
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#基线敏感阈值
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baseline.sensitivity.threshold=0.2
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@@ -92,8 +92,8 @@ baseline.sessions.severe.threshold=5
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baseline.sessions.critical.threshold=8
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#bifang服务访问地址
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#bifang.server.uri=http://192.168.44.72:80
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bifang.server.uri=http://192.168.44.3:80
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bifang.server.uri=http://192.168.44.72:80
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#bifang.server.uri=http://192.168.44.3:80
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#访问bifang只读权限token,bifang内置,无需修改
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bifang.server.token=ed04b942-7df4-4e3d-b9a9-a881ca98a867
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