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全球网络安全威胁数据可视化分析系统-系统功能
《基于大数据的全球网络安全威胁数据可视化分析系统》是一个综合运用Hadoop+Spark大数据处理框架构建的网络安全数据分析平台。该系统采用Python+Django后端架构，结合Vue+ElementUI+Echarts前端技术栈，对全球范围内2015-2024年的网络安全威胁事件进行深度挖掘和可视化展示。系统通过HDFS分布式文件系统存储海量安全数据，利用Spark SQL和Pandas、NumPy等数据处理工具对攻击类型、目标行业、经济损失、受影响用户数等多维度指标进行统计分析。平台具备年度威胁趋势分析、全球事件分布分析、攻击类型占比统计、安全漏洞频率分析、经济损失评估、事件聚类分析等核心功能模块，能够生成直观的图表展示，包括折线图、柱状图、饼图、热力图等多种可视化形式，为网络安全研究人员和决策者提供数据驱动的威胁态势感知和风险评估支持，帮助理解全球网络安全威胁的演变规律和分布特征。
全球网络安全威胁数据可视化分析系统-技术选型
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
开发语言：Python+Java（两个版本都支持）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL
全球网络安全威胁数据可视化分析系统-背景意义
选题背景 随着数字化转型的深入推进，网络安全威胁呈现出复杂化、多样化的发展态势，勒索软件、网络钓鱼、数据泄露等安全事件频繁发生，给全球各国政府、企业和个人用户造成了巨大的经济损失和社会影响。传统的网络安全分析方法往往局限于单一维度的统计，难以从海量的威胁数据中提取有价值的洞察信息，缺乏对全球网络安全态势的整体性和系统性认知。特别是面对跨国界、跨行业的复杂网络攻击活动，现有的分析工具在处理大规模数据集时存在性能瓶颈，无法满足实时性和准确性的双重要求。大数据技术的快速发展为解决这一问题提供了新的技术路径，通过分布式计算和数据挖掘技术，能够对大规模网络安全数据进行高效处理和深度分析，挖掘隐藏在数据背后的威胁模式和演变趋势。 选题意义 本课题具有重要的理论价值和实践意义，为网络安全领域的数据分析提供了一种创新的技术方案。从技术角度来看，该系统探索了大数据技术在网络安全威胁分析中的应用模式，验证了Hadoop+Spark架构在处理安全数据方面的可行性和有效性，为后续相关研究奠定了技术基础。从实用角度考虑，系统能够帮助网络安全从业者更好地理解全球威胁态势，识别高风险的攻击类型和目标行业，为制定针对性的防御策略提供数据支撑。对于企业管理者而言，系统提供的经济损失分析和行业风险评估功能，可以辅助进行安全投入的决策规划。对于研究机构来说，平台的多维度分析能力有助于发现网络攻击的规律性特征，推动网络安全理论研究的深入发展。当然，作为一个毕业设计项目，本系统在数据规模和功能完善程度上还有提升空间，但它为网络安全数据分析领域提供了一个可参考的技术实现方案。
全球网络安全威胁数据可视化分析系统-演示视频
全球网络安全威胁数据可视化分析系统-演示图片
全球网络安全威胁数据可视化分析系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum as spark_sum, avg, desc, asc, when, isnan, isnull
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler, StandardScaler
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("CyberSecurityThreatAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def global_threat_trends_analysis():
df = spark.read.csv("/data/cybersecurity/Global_Cybersecurity_Threats_2015-2024.csv", header=True, inferSchema=True)
df_cleaned = df.filter(col("Year").isNotNull() & col("Country").isNotNull())
yearly_trends = df_cleaned.groupBy("Year").agg(count("*").alias("total_incidents"), spark_sum("Financial Loss (in Million $)").alias("total_loss"), spark_sum("Number of Affected Users").alias("total_affected_users")).orderBy("Year")
yearly_trends_pandas = yearly_trends.toPandas()
yearly_trends_pandas["total_loss"] = yearly_trends_pandas["total_loss"].round(2)
yearly_trends_pandas["avg_loss_per_incident"] = (yearly_trends_pandas["total_loss"] / yearly_trends_pandas["total_incidents"]).round(2)
country_distribution = df_cleaned.groupBy("Country").agg(count("*").alias("incident_count"), spark_sum("Financial Loss (in Million $)").alias("country_total_loss")).orderBy(desc("incident_count"))
country_top20 = country_distribution.limit(20).toPandas()
country_top20["country_total_loss"] = country_top20["country_total_loss"].round(2)
attack_type_trends = df_cleaned.groupBy("Year", "Attack Type").agg(count("*").alias("attack_count")).orderBy("Year", desc("attack_count"))
attack_pivot = attack_type_trends.toPandas().pivot(index="Year", columns="Attack Type", values="attack_count").fillna(0)
attack_pivot = attack_pivot.round(0).astype(int)
yearly_trends_pandas.to_csv("/output/yearly_threat_trends_analysis.csv", index=False)
country_top20.to_csv("/output/country_threat_distribution_analysis.csv", index=False)
attack_pivot.to_csv("/output/attack_type_yearly_trends_analysis.csv")
return yearly_trends_pandas, country_top20, attack_pivot
def attack_characteristics_analysis():
df = spark.read.csv("/data/cybersecurity/Global_Cybersecurity_Threats_2015-2024.csv", header=True, inferSchema=True)
df_cleaned = df.filter(col("Attack Type").isNotNull() & col("Security Vulnerability Type").isNotNull())
attack_type_stats = df_cleaned.groupBy("Attack Type").agg(count("*").alias("frequency"), avg("Financial Loss (in Million $)").alias("avg_financial_loss"), avg("Number of Affected Users").alias("avg_affected_users"), avg("Incident Resolution Time (in Hours)").alias("avg_resolution_time")).orderBy(desc("frequency"))
attack_type_pandas = attack_type_stats.toPandas()
attack_type_pandas["frequency_percentage"] = (attack_type_pandas["frequency"] / attack_type_pandas["frequency"].sum() * 100).round(2)
attack_type_pandas[["avg_financial_loss", "avg_affected_users", "avg_resolution_time"]] = attack_type_pandas[["avg_financial_loss", "avg_affected_users", "avg_resolution_time"]].round(2)
vulnerability_stats = df_cleaned.groupBy("Security Vulnerability Type").agg(count("*").alias("exploit_frequency"), avg("Financial Loss (in Million $)").alias("avg_loss_per_vuln")).orderBy(desc("exploit_frequency"))
vulnerability_pandas = vulnerability_stats.toPandas()
vulnerability_pandas["exploit_percentage"] = (vulnerability_pandas["exploit_frequency"] / vulnerability_pandas["exploit_frequency"].sum() * 100).round(2)
vulnerability_pandas["avg_loss_per_vuln"] = vulnerability_pandas["avg_loss_per_vuln"].round(2)
attack_source_stats = df_cleaned.groupBy("Attack Source").agg(count("*").alias("source_frequency")).orderBy(desc("source_frequency"))
attack_source_pandas = attack_source_stats.toPandas()
attack_source_pandas["source_percentage"] = (attack_source_pandas["source_frequency"] / attack_source_pandas["source_frequency"].sum() * 100).round(2)
correlation_matrix = df_cleaned.select("Attack Type", "Security Vulnerability Type").groupBy("Attack Type", "Security Vulnerability Type").agg(count("*").alias("combination_count")).orderBy(desc("combination_count"))
correlation_pandas = correlation_matrix.toPandas()
attack_type_pandas.to_csv("/output/attack_type_characteristics_analysis.csv", index=False)
vulnerability_pandas.to_csv("/output/vulnerability_exploitation_analysis.csv", index=False)
attack_source_pandas.to_csv("/output/attack_source_distribution_analysis.csv", index=False)
correlation_pandas.to_csv("/output/attack_vulnerability_correlation_analysis.csv", index=False)
return attack_type_pandas, vulnerability_pandas, attack_source_pandas, correlation_pandas
def impact_consequence_clustering_analysis():
df = spark.read.csv("/data/cybersecurity/Global_Cybersecurity_Threats_2015-2024.csv", header=True, inferSchema=True)
df_numeric = df.select("Financial Loss (in Million $)", "Number of Affected Users", "Incident Resolution Time (in Hours)", "Attack Type", "Target Industry").filter(col("Financial Loss (in Million $)").isNotNull() & col("Number of Affected Users").isNotNull() & col("Incident Resolution Time (in Hours)").isNotNull())
feature_cols = ["Financial Loss (in Million $)", "Number of Affected Users", "Incident Resolution Time (in Hours)"]
assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
df_vector = assembler.transform(df_numeric)
scaler = StandardScaler(inputCol="features", outputCol="scaled_features", withStd=True, withMean=True)
scaler_model = scaler.fit(df_vector)
df_scaled = scaler_model.transform(df_vector)
kmeans = KMeans(featuresCol="scaled_features", predictionCol="cluster", k=4, seed=42, maxIter=100)
kmeans_model = kmeans.fit(df_scaled)
df_clustered = kmeans_model.transform(df_scaled)
cluster_analysis = df_clustered.groupBy("cluster").agg(count("*").alias("cluster_size"), avg("Financial Loss (in Million $)").alias("avg_financial_loss"), avg("Number of Affected Users").alias("avg_affected_users"), avg("Incident Resolution Time (in Hours)").alias("avg_resolution_time")).orderBy("cluster")
cluster_pandas = cluster_analysis.toPandas()
cluster_pandas[["avg_financial_loss", "avg_affected_users", "avg_resolution_time"]] = cluster_pandas[["avg_financial_loss", "avg_affected_users", "avg_resolution_time"]].round(2)
cluster_descriptions = {0: "低损失快速恢复型", 1: "高损失广影响型", 2: "中等损失长时间恢复型", 3: "小规模精准攻击型"}
cluster_pandas["cluster_description"] = cluster_pandas["cluster"].map(cluster_descriptions)
industry_impact = df_clustered.groupBy("Target Industry").agg(avg("Financial Loss (in Million $)").alias("industry_avg_loss"), spark_sum("Number of Affected Users").alias("industry_total_users"), count("*").alias("industry_incident_count")).orderBy(desc("industry_avg_loss"))
industry_pandas = industry_impact.toPandas()
industry_pandas[["industry_avg_loss"]] = industry_pandas[["industry_avg_loss"]].round(2)
attack_economic_impact = df_clustered.groupBy("Attack Type").agg(avg("Financial Loss (in Million $)").alias("attack_avg_loss"), spark_sum("Financial Loss (in Million $)").alias("attack_total_loss"), avg("Number of Affected Users").alias("attack_avg_users")).orderBy(desc("attack_avg_loss"))
attack_impact_pandas = attack_economic_impact.toPandas()
attack_impact_pandas[["attack_avg_loss", "attack_total_loss", "attack_avg_users"]] = attack_impact_pandas[["attack_avg_loss", "attack_total_loss", "attack_avg_users"]].round(2)
cluster_pandas.to_csv("/output/incident_clustering_analysis.csv", index=False)
industry_pandas.to_csv("/output/industry_impact_analysis.csv", index=False)
attack_impact_pandas.to_csv("/output/attack_economic_impact_analysis.csv", index=False)
return cluster_pandas, industry_pandas, attack_impact_pandas
全球网络安全威胁数据可视化分析系统-结语
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💟💟如果大家有任何疑虑，欢迎在下方位置详细交流，也可以在主页联系我。
