摘 要
随着信息技术的迅猛发展，网络攻击呈现出跨平台、复杂化和隐蔽性的特点，给网络安全带来了严峻挑战。为此，本研究聚焦于跨平台网络攻击检测与溯源技术，旨在构建一种高效、精准的检测与溯源体系。研究基于多源异构数据融合技术，整合来自不同平台的日志信息、流量数据等，利用机器学习算法对数据进行深度分析，挖掘潜在攻击行为模式。同时，引入图神经网络模型，通过构建攻击行为图谱，实现对攻击路径的精确还原。实验结果表明，该方法能够有效识别多种类型的跨平台攻击，检测准确率较传统方法提升约20%，且在复杂网络环境下仍保持较高的鲁棒性。此外，研究提出了一种基于区块链的证据保全机制，确保溯源过程中的数据不可篡改，增强了攻击溯源的可信度。本研究不仅为跨平台网络攻击检测提供了新的思路和技术手段，还为构建更加安全可靠的网络环境奠定了理论基础，具有重要的学术价值和实际应用前景。
关 键 词：跨平台网络攻击，多源异构数据融合，图神经网络
ABSTRACT
With the rapid development of information technology, network attacks are cross-platform, complex and hidden, which brings severe challenges to network security. To this end, this research focuses on the cross-platform network attack detection and traceability technology, aiming to build an efficient and accurate detection and traceability system. Based on the multi-source heterogeneous data fusion technology, the paper integrates log information and traffic data from different platforms, uses machine learning algorithm to conduct in-depth analysis of data, and excavates potential attack behavior patterns. At the same time, the graph neural network model is introduced to realize the accurate reduction of the attack path by building the attack behavior map. The experimental results show that this method can effectively identify multiple types of cross-platform attacks, and the detection accuracy is about 20% higher than the traditional method, and still maintains high robustness in the complex network environment. In addition, the research proposes a blockchain-based evidence preservation mechanism to ensure that the data in the traceability process cannot be tampered with and enhance the credibility of the attack traceability. This study not only provides new ideas and technical means for cross-platform network attack detection, but also lays a theoretical foundation for building a more secure and reliable network environment, which has important academic value and practical application prospects.
KEY WORDS：Cross-platform network attack, multi-source heterogeneous data fusion, graph neural network
目 录
第1章 绪论 1
1.1 研究背景与意义 1
1.2 国内外研究现状 1
1.3 研究方法与技术路线 2
第2章 跨平台攻击特征分析 3
2.1 攻击模式识别 3
2.2 多平台行为关联 4
2.3 异常流量检测方法 5
第3章 检测技术体系构建 6
3.1 数据采集与预处理 6
3.2 检测算法优化 6
3.3 实时监测系统设计 7
第4章 溯源技术研究与实现 9
4.1 溯源模型建立 9
4.2 证据链完整性保障 10
4.3 溯源结果可视化呈现 10
结 论 12
参考文献 13
致 谢 14
随着信息技术的迅猛发展，网络攻击呈现出跨平台、复杂化和隐蔽性的特点，给网络安全带来了严峻挑战。为此，本研究聚焦于跨平台网络攻击检测与溯源技术，旨在构建一种高效、精准的检测与溯源体系。研究基于多源异构数据融合技术，整合来自不同平台的日志信息、流量数据等，利用机器学习算法对数据进行深度分析，挖掘潜在攻击行为模式。同时，引入图神经网络模型，通过构建攻击行为图谱，实现对攻击路径的精确还原。实验结果表明，该方法能够有效识别多种类型的跨平台攻击，检测准确率较传统方法提升约20%，且在复杂网络环境下仍保持较高的鲁棒性。此外，研究提出了一种基于区块链的证据保全机制，确保溯源过程中的数据不可篡改，增强了攻击溯源的可信度。本研究不仅为跨平台网络攻击检测提供了新的思路和技术手段，还为构建更加安全可靠的网络环境奠定了理论基础，具有重要的学术价值和实际应用前景。
关 键 词：跨平台网络攻击，多源异构数据融合，图神经网络
ABSTRACT
With the rapid development of information technology, network attacks are cross-platform, complex and hidden, which brings severe challenges to network security. To this end, this research focuses on the cross-platform network attack detection and traceability technology, aiming to build an efficient and accurate detection and traceability system. Based on the multi-source heterogeneous data fusion technology, the paper integrates log information and traffic data from different platforms, uses machine learning algorithm to conduct in-depth analysis of data, and excavates potential attack behavior patterns. At the same time, the graph neural network model is introduced to realize the accurate reduction of the attack path by building the attack behavior map. The experimental results show that this method can effectively identify multiple types of cross-platform attacks, and the detection accuracy is about 20% higher than the traditional method, and still maintains high robustness in the complex network environment. In addition, the research proposes a blockchain-based evidence preservation mechanism to ensure that the data in the traceability process cannot be tampered with and enhance the credibility of the attack traceability. This study not only provides new ideas and technical means for cross-platform network attack detection, but also lays a theoretical foundation for building a more secure and reliable network environment, which has important academic value and practical application prospects.
KEY WORDS：Cross-platform network attack, multi-source heterogeneous data fusion, graph neural network
目 录
第1章 绪论 1
1.1 研究背景与意义 1
1.2 国内外研究现状 1
1.3 研究方法与技术路线 2
第2章 跨平台攻击特征分析 3
2.1 攻击模式识别 3
2.2 多平台行为关联 4
2.3 异常流量检测方法 5
第3章 检测技术体系构建 6
3.1 数据采集与预处理 6
3.2 检测算法优化 6
3.3 实时监测系统设计 7
第4章 溯源技术研究与实现 9
4.1 溯源模型建立 9
4.2 证据链完整性保障 10
4.3 溯源结果可视化呈现 10
结 论 12
参考文献 13
致 谢 14
