将人工智能集成到安全信息和事件管理中,实现实时网络防御
Salman Khan1, Naqqash Dilshad2, Nijad Ahmad3
1New Emerging Technologies and 5G Network and Beyond Research Chair, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
Scientific reports
|October 14, 2025
概括
这项研究介绍了一个人工智能系统,用于在Twitter上实时进行网络威胁情报 (CTI) 分析. 这种新的方法可以通过先进的机器学习技术提升对网络攻击的早期检测.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 传统的网络威胁检测系统与日益复杂的网络攻击作斗争.
- 手动分析开源情报 (OSINT) 数据,特别是来自社交媒体的数据,耗时且容易出现错误.
- 需要自动化,实时的网络威胁情报 (CTI) 解决方案.
研究的目的:
- 开发和评估一个人工智能驱动的系统,用于实时检测和分析Twitter上的网络威胁信息.
- 通过利用先进的自然语言处理和机器学习技术来提高CTI的效率和准确性.
- 为了解决手动OSINT分析对网络安全的局限性.
主要方法:
- 一种混合特征提取技术,结合了来自变压器的双向编码器表示 (BERT) 和代扩展卷积神经网络 (ID-CNN) 的混合特征提取技术.
- 二元树生长 (BTG) 算法用于特征选择,以改进数据相关性.
- 使用双向时间卷积网络 (BiTCN) 进行序列数据分析进行分类.
主要成果:
- 拟议的ID-BERT-BiTCN模型在数据集1上达到99.01%的高准确率,在数据集2上达到97.63%.
- 与其他机器学习 (ML) 和现有模型相比,该系统表现出卓越的性能.
- 10次交叉验证证实了该模型的稳定性和有效性.
结论:
- 这种由人工智能驱动的系统在实时网络威胁情报方面取得了重大进展.
- 整合BERT,ID-CNN,BTG和BiTCN有效地提高了从社交媒体上检测威胁的效果.
- 这种方法具有巨大的潜力,可以通过及时和准确的威胁识别来改善网络安全防御.
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