💖💖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
基于大数据的网络安全威胁数据可视化分析系统介绍
从技术角度来看，本课题的研究有助于推动大数据技术在网络安全领域的应用，通过整合Hadoop分布式存储和Spark计算引擎的技术优势，构建一个高效处理大规模安全威胁数据的平台，这对于提升网络安全数据分析的效率和准确性具有积极作用。从实用价值来说，系统通过可视化的方式展现威胁数据分布和变化趋势，能够帮助安全管理人员快速了解当前的安全状况，及时发现异常情况并采取相应的应对措施。对于企业用户而言，该系统可以作为一种辅助决策工具，通过对历史威胁数据的分析和挖掘，为制定安全防护策略提供参考依据。从学术研究的角度看，本课题将大数据技术与网络安全相结合，探索了新的数据处理和分析方法，为相关领域的研究提供了实践经验。对于学生的个人发展来说，通过完成这样一个综合性的项目，能够全面掌握大数据技术栈的应用，提升系统设计和开发能力，为未来从事相关工作打下基础。
基于大数据的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
基于大数据的网络安全威胁数据可视化分析系统代码展示
from pyspark.sql import SparkSession
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("ThreatAnalysis").getOrCreate()
def analyze_global_threat_trends(start_date, end_date, region_filter=None):
threat_data = spark.read.parquet("hdfs://security_data/threat_logs/")
filtered_data = threat_data.filter((threat_data.timestamp >= start_date) & (threat_data.timestamp <= end_date))
if region_filter:
filtered_data = filtered_data.filter(filtered_data.region.isin(region_filter))
threat_by_region = filtered_data.groupBy("region").count().orderBy("count", ascending=False)
threat_by_type = filtered_data.groupBy("threat_type", "severity_level").count().orderBy("count", ascending=False)
daily_trends = filtered_data.groupBy(filtered_data.timestamp.cast("date").alias("date")).count().orderBy("date")
top_attacker_ips = filtered_data.filter(filtered_data.threat_type == "malicious_access").groupBy("source_ip").count().orderBy("count", ascending=False).limit(10)
vulnerable_ports = filtered_data.filter(filtered_data.threat_type == "port_scan").groupBy("target_port").count().orderBy("count", ascending=False).limit(10)
geo_distribution = filtered_data.groupBy("country", "region").pivot("threat_type").count().fillna(0)
severity_stats = filtered_data.groupBy("severity_level").agg({"count": "sum", "threat_type": "count"})
result = {
"threat_by_region": threat_by_region.toPandas().to_dict('records'),
"threat_by_type": threat_by_type.toPandas().to_dict('records'),
"daily_trends": daily_trends.toPandas().to_dict('records'),
"top_attackers": top_attacker_ips.toPandas().to_dict('records'),
"vulnerable_ports": vulnerable_ports.toPandas().to_dict('records'),
"geo_distribution": geo_distribution.toPandas().to_dict('records'),
"severity_statistics": severity_stats.toPandas().to_dict('records')
}
return result
2. 攻击影响深度分析功能
def analyze_attack_impact_depth(target_system, attack_time_range):
system_logs = spark.read.parquet("hdfs://security_data/system_access_logs/")
attack_events = spark.read.parquet("hdfs://security_data/attack_events/")
system_data = system_logs.filter(system_logs.system_id == target_system)
attack_data = attack_events.filter((attack_events.target_system == target_system) &
(attack_events.timestamp.between(attack_time_range[0], attack_time_range[1])))
affected_users = system_data.join(attack_data, system_logs.session_id == attack_events.session_id, "inner")
user_impact_analysis = affected_users.groupBy("user_id").agg({"access_count": "sum", "data_volume": "sum"})
service_disruption = system_data.filter(system_logs.timestamp.between(attack_time_range[0], attack_time_range[1]))\
.groupBy("service_name").agg({"response_time": "avg", "error_rate": "sum"})
data_breach_analysis = attack_data.filter(attack_events.data_accessed == True)\
.groupBy("data_type", "sensitivity_level").agg({"record_count": "sum"})
attack_propagation = attack_data.groupBy("attack_stage", "affected_components").count()
recovery_metrics = system_data.filter(system_logs.timestamp > attack_time_range[1])\
.groupBy("service_name").agg({"normalization_time": "min", "performance_recovery": "avg"})
impact_score = attack_data.agg({"business_impact": "sum", "recovery_cost": "sum"})
vulnerability_chain = attack_data.filter(attack_events.exploitation_method != "direct")\
.groupBy("vulnerability_chain").count()
timeline_analysis = attack_data.groupBy(attack_events.timestamp.cast("hour").alias("hour"))\
.agg({"attack_intensity": "max", "affected_systems": "count"})
impact_assessment = {
"affected_users": user_impact_analysis.toPandas().to_dict('records'),
"service_disruption": service_disruption.toPandas().to_dict('records'),
"data_breach_analysis": data_breach_analysis.toPandas().to_dict('records'),
"attack_propagation": attack_propagation.toPandas().to_dict('records'),
"recovery_metrics": recovery_metrics.toPandas().to_dict('records'),
"impact_score": impact_score.collect()[0].asDict(),
"vulnerability_chain": vulnerability_chain.toPandas().to_dict('records'),
"timeline_analysis": timeline_analysis.toPandas().to_dict('records')
}
return impact_assessment
3. 漏洞防御效能分析功能
def analyze_vulnerability_defense_effectiveness(defense_mechanisms, evaluation_period):
vulnerability_data = spark.read.parquet("hdfs://security_data/vulnerability_scans/")
defense_logs = spark.read.parquet("hdfs://security_data/defense_mechanisms/")
attack_attempts = spark.read.parquet("hdfs://security_data/attack_attempts/")
vuln_within_period = vulnerability_data.filter(vulnerability_data.discovery_date.between(evaluation_period[0], evaluation_period[1]))
defense_performance = defense_logs.filter(defense_logs.mechanism_id.isin(defense_mechanisms) &
defense_logs.timestamp.between(evaluation_period[0], evaluation_period[1]))
attacks_within_period = attack_attempts.filter(attack_attempts.timestamp.between(evaluation_period[0], evaluation_period[1]))
mechanism_effectiveness = defense_performance.groupBy("mechanism_id", "attack_type")\
.agg({"blocked_attacks": "sum", "total_attempts": "sum", "response_time": "avg"})
vulnerability_coverage = vuln_within_period.join(defense_performance,
vulnerability_data.vulnerability_id == defense_performance.vulnerability_id, "left_outer")\
.groupBy("vulnerability_type", "severity_level")\
.agg({"protected_systems": "count", "total_systems": "count"})
attack_pattern_analysis = attacks_within_period.groupBy("attack_vector", "target_system")\
.agg({"success_rate": "avg", "attempt_count": "count"})
defense_failure_analysis = defense_performance.filter(defense_performance.block_status == "failed")\
.groupBy("mechanism_id", "failure_reason").count()
time_to_patch_analysis = vuln_within_period.filter(vulnerability_data.patch_date.isNotNull())\
.agg({"patch_deployment_time": "avg", "vulnerability_exposure": "sum"})
false_positive_analysis = defense_performance.filter(defense_performance.block_status == "false_positive")\
.groupBy("mechanism_id").agg({"false_positive_rate": "avg"})
overall_defense_score = defense_performance.groupBy("mechanism_id")\
.agg({"effectiveness_score": "avg", "cost_efficiency": "avg"})
defense_recommendations = mechanism_effectiveness.filter(mechanism_effectiveness.effectiveness_score < 0.7)\
.select("mechanism_id", "improvement_suggestions")
effectiveness_report = {
"mechanism_effectiveness": mechanism_effectiveness.toPandas().to_dict('records'),
"vulnerability_coverage": vulnerability_coverage.toPandas().to_dict('records'),
"attack_patterns": attack_pattern_analysis.toPandas().to_dict('records'),
"defense_failures": defense_failure_analysis.toPandas().to_dict('records'),
"patch_performance": time_to_patch_analysis.collect()[0].asDict(),
"false_positive_rates": false_positive_analysis.toPandas().to_dict('records'),
"overall_scores": overall_defense_score.toPandas().to_dict('records'),
"recommendations": defense_recommendations.toPandas().to_dict('records')
}
return effectiveness_report
基于大数据的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
