在API中采用深度学习模型来管理威胁,要求透明度义务实践,以实现整体弹性
Nihala Basheer1, Shareeful Islam1,2, Mohammed K S Alwaheidi3
1School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
本研究介绍了一种人工智能驱动的架构,用于检测应用程序编程接口 (API) 调用中的威胁,增强系统安全性. 它在识别大型API数据集中的漏洞时达到88%的准确性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 软件工程 软件工程 软件工程
背景情况:
- 应用程序编程接口 (API) 对于系统对系统的通信至关重要,使得高效的服务提供成为可能.
- 应用程序应用程序引入了攻击者可以利用的安全漏洞,需要强大的威胁识别和缓解策略.
- 分析威胁的API呼叫流量是具有挑战性的,因为大量的数据和不断变化的攻击向量.
研究的目的:
- 通过API调用开发一个集成的架构,以便在系统对系统通信中有效检测威胁.
- 通过识别和减轻与API相关的漏洞来增强组织安全态度.
- 在人工智能生命周期中引入透明度,以更好地理解和信任威胁检测模型.
主要方法:
- 开发了一个集成的架构,结合了人工神经网络 (ANN) 和多层感知器 (MLP) 深度学习模型.
- 使用开放情报目录 (CWE,CAPEC) 和NIST SP 800-53控制器进行了威胁分析.
- 通过数据/方法实践和夏普利添加式解释 (SHAP) 分析,确保了透明度.
主要成果:
- 拟议的方法在Windows PE恶意软件API数据集上实现了88%的平均检测准确度.
- 像FindResourceExA和NtClose这样的关键功能被确定为潜在弱点和威胁的指标.
- 该研究提供了一份准确的控制行动清单,用于管理已识别的API呼叫威胁.
结论:
- 开发的深度学习架构有效地检测大型API调用数据集中的威胁,提高了系统的弹性.
- 人工智能模型的透明度提高了所有用户群体的可理解性和信任性.
- 识别与威胁相关的特定API特征可以实现有针对性的安全控制.
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