💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
基于大数据的全球网络安全威胁数据可视化分析系统介绍
《基于大数据的全球网络安全威胁数据可视化分析系统》是一款专门针对全球范围内网络安全威胁进行深度挖掘和可视化呈现的综合性平台。该系统采用Hadoop分布式存储架构配合Spark大数据计算引擎，能够高效处理海量威胁情报数据，通过Spark SQL和Pandas进行多维度数据清洗与特征提取。后端采用Spring Boot框架构建RESTful接口服务，前端运用Vue框架结合Echarts图表库实现动态可视化效果，支持时空维度分析、攻击特征分析、影响后果分析以及防御响应分析四大核心分析模块。系统基于MySQL数据库存储结构化威胁数据，利用HDFS分布式文件系统管理原始日志文件，通过NumPy进行数值计算和统计分析，最终将复杂的威胁数据转化为直观的可视化图表，帮助安全分析人员快速识别攻击模式、评估威胁等级、追踪攻击来源并制定相应的防御策略。整个系统从数据采集、存储、计算到展示形成完整的技术闭环，为网络安全威胁情报分析提供了一套完整的大数据解决方案。
基于大数据的全球网络安全威胁数据可视化分析系统演示视频
基于大数据的全球网络安全威胁数据可视化分析系统演示图片
基于大数据的全球网络安全威胁数据可视化分析系统代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.functions;
import org.springframework.stereotype.Service;
import java.util.*;
@Service
public class ThreatAnalysisService {
private SparkSession spark = SparkSession.builder().appName("ThreatAnalysis").master("local[*]").config("spark.sql.warehouse.dir", "/user/hive/warehouse").getOrCreate();
public Map<String, Object> analyzeTimeSpaceDimension(String startDate, String endDate) {
Dataset<Row> threatData = spark.read().format("jdbc").option("url", "jdbc:mysql://localhost:3306/security_db").option("dbtable", "threat_logs").option("user", "root").option("password", "123456").load();
Dataset<Row> filteredData = threatData.filter(functions.col("attack_time").between(startDate, endDate));
Dataset<Row> geoAggregation = filteredData.groupBy("source_country", "attack_type").agg(functions.count("*").alias("attack_count"), functions.avg("threat_level").alias("avg_threat_level"));
Dataset<Row> timeAggregation = filteredData.withColumn("attack_hour", functions.hour(functions.col("attack_time"))).groupBy("attack_hour").agg(functions.count("*").alias("hourly_count"));
List<Row> geoResults = geoAggregation.collectAsList();
List<Row> timeResults = timeAggregation.collectAsList();
Map<String, Object> resultMap = new HashMap<>();
List<Map<String, Object>> geoData = new ArrayList<>();
for (Row row : geoResults) {
Map<String, Object> item = new HashMap<>();
item.put("country", row.getString(0));
item.put("attackType", row.getString(1));
item.put("attackCount", row.getLong(2));
item.put("avgThreatLevel", row.getDouble(3));
geoData.add(item);
}
List<Map<String, Object>> timeData = new ArrayList<>();
for (Row row : timeResults) {
Map<String, Object> item = new HashMap<>();
item.put("hour", row.getInt(0));
item.put("count", row.getLong(1));
timeData.add(item);
}
resultMap.put("geoDistribution", geoData);
resultMap.put("timeDistribution", timeData);
Dataset<Row> hotspotAnalysis = filteredData.groupBy("source_ip", "source_country").agg(functions.count("*").alias("ip_attack_count")).orderBy(functions.desc("ip_attack_count")).limit(20);
List<Row> hotspots = hotspotAnalysis.collectAsList();
List<Map<String, Object>> hotspotData = new ArrayList<>();
for (Row row : hotspots) {
Map<String, Object> item = new HashMap<>();
item.put("sourceIp", row.getString(0));
item.put("country", row.getString(1));
item.put("attackCount", row.getLong(2));
hotspotData.add(item);
}
resultMap.put("attackHotspots", hotspotData);
return resultMap;
}
public Map<String, Object> analyzeAttackFeatures(String attackType) {
Dataset<Row> threatData = spark.read().format("jdbc").option("url", "jdbc:mysql://localhost:3306/security_db").option("dbtable", "threat_logs").option("user", "root").option("password", "123456").load();
Dataset<Row> typeFiltered = threatData.filter(functions.col("attack_type").equalTo(attackType));
Dataset<Row> portAnalysis = typeFiltered.groupBy("target_port").agg(functions.count("*").alias("port_count")).orderBy(functions.desc("port_count")).limit(10);
Dataset<Row> protocolAnalysis = typeFiltered.groupBy("protocol").agg(functions.count("*").alias("protocol_count"), functions.avg("packet_size").alias("avg_packet_size"));
Dataset<Row> payloadAnalysis = typeFiltered.groupBy("payload_pattern").agg(functions.count("*").alias("pattern_count")).orderBy(functions.desc("pattern_count")).limit(15);
List<Row> portResults = portAnalysis.collectAsList();
List<Row> protocolResults = protocolAnalysis.collectAsList();
List<Row> payloadResults = payloadAnalysis.collectAsList();
Map<String, Object> resultMap = new HashMap<>();
List<Map<String, Object>> portData = new ArrayList<>();
for (Row row : portResults) {
Map<String, Object> item = new HashMap<>();
item.put("port", row.getInt(0));
item.put("count", row.getLong(1));
portData.add(item);
}
List<Map<String, Object>> protocolData = new ArrayList<>();
for (Row row : protocolResults) {
Map<String, Object> item = new HashMap<>();
item.put("protocol", row.getString(0));
item.put("count", row.getLong(1));
item.put("avgPacketSize", row.getDouble(2));
protocolData.add(item);
}
List<Map<String, Object>> payloadData = new ArrayList<>();
for (Row row : payloadResults) {
Map<String, Object> item = new HashMap<>();
item.put("pattern", row.getString(0));
item.put("count", row.getLong(1));
payloadData.add(item);
}
resultMap.put("portDistribution", portData);
resultMap.put("protocolDistribution", protocolData);
resultMap.put("payloadPatterns", payloadData);
Dataset<Row> attackDuration = typeFiltered.agg(functions.avg("attack_duration").alias("avg_duration"), functions.max("attack_duration").alias("max_duration"), functions.min("attack_duration").alias("min_duration"));
Row durationRow = attackDuration.first();
Map<String, Object> durationStats = new HashMap<>();
durationStats.put("avgDuration", durationRow.getDouble(0));
durationStats.put("maxDuration", durationRow.getLong(1));
durationStats.put("minDuration", durationRow.getLong(2));
resultMap.put("durationStatistics", durationStats);
return resultMap;
}
public Map<String, Object> analyzeImpactConsequence(String targetSystem) {
Dataset<Row> threatData = spark.read().format("jdbc").option("url", "jdbc:mysql://localhost:3306/security_db").option("dbtable", "threat_logs").option("user", "root").option("password", "123456").load();
Dataset<Row> systemFiltered = threatData.filter(functions.col("target_system").equalTo(targetSystem));
Dataset<Row> severityAnalysis = systemFiltered.groupBy("severity_level").agg(functions.count("*").alias("severity_count"), functions.sum("data_loss_size").alias("total_data_loss"));
Dataset<Row> serviceImpact = systemFiltered.groupBy("affected_service").agg(functions.count("*").alias("impact_count"), functions.avg("downtime_minutes").alias("avg_downtime"));
Dataset<Row> damageType = systemFiltered.groupBy("damage_type").agg(functions.count("*").alias("damage_count"));
List<Row> severityResults = severityAnalysis.collectAsList();
List<Row> serviceResults = serviceImpact.collectAsList();
List<Row> damageResults = damageType.collectAsList();
Map<String, Object> resultMap = new HashMap<>();
List<Map<String, Object>> severityData = new ArrayList<>();
for (Row row : severityResults) {
Map<String, Object> item = new HashMap<>();
item.put("severityLevel", row.getString(0));
item.put("count", row.getLong(1));
item.put("totalDataLoss", row.getLong(2));
severityData.add(item);
}
List<Map<String, Object>> serviceData = new ArrayList<>();
for (Row row : serviceResults) {
Map<String, Object> item = new HashMap<>();
item.put("service", row.getString(0));
item.put("impactCount", row.getLong(1));
item.put("avgDowntime", row.getDouble(2));
serviceData.add(item);
}
List<Map<String, Object>> damageData = new ArrayList<>();
for (Row row : damageResults) {
Map<String, Object> item = new HashMap<>();
item.put("damageType", row.getString(0));
item.put("count", row.getLong(1));
damageData.add(item);
}
resultMap.put("severityDistribution", severityData);
resultMap.put("serviceImpact", serviceData);
resultMap.put("damageTypeDistribution", damageData);
Dataset<Row> financialImpact = systemFiltered.agg(functions.sum("estimated_loss").alias("total_loss"), functions.avg("estimated_loss").alias("avg_loss"));
Row financialRow = financialImpact.first();
Map<String, Object> financialStats = new HashMap<>();
financialStats.put("totalLoss", financialRow.getDouble(0));
financialStats.put("avgLoss", financialRow.getDouble(1));
resultMap.put("financialImpact", financialStats);
Dataset<Row> recoveryTime = systemFiltered.groupBy("recovery_status").agg(functions.avg("recovery_hours").alias("avg_recovery_hours"), functions.count("*").alias("status_count"));
List<Row> recoveryResults = recoveryTime.collectAsList();
List<Map<String, Object>> recoveryData = new ArrayList<>();
for (Row row : recoveryResults) {
Map<String, Object> item = new HashMap<>();
item.put("status", row.getString(0));
item.put("avgRecoveryHours", row.getDouble(1));
item.put("count", row.getLong(2));
recoveryData.add(item);
}
resultMap.put("recoveryAnalysis", recoveryData);
return resultMap;
}
}
基于大数据的全球网络安全威胁数据可视化分析系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
