Siren represents a pioneering research effort aimed at fortifying
cybersecurity through strategic integration of deception, machine learning, and
proactive threat analysis. Drawing inspiration from mythical sirens, this
project employs sophisticated methods to lure potential threats into controlled
environments. The system features a dynamic machine learning model for
real-time analysis and classification, ensuring continuous adaptability to
emerging cyber threats. The architectural framework includes a link monitoring
proxy, a purpose-built machine learning model for dynamic link analysis, and a
honeypot enriched with simulated user interactions to intensify threat
engagement. Data protection within the honeypot is fortified with probabilistic
encryption. Additionally, the incorporation of simulated user activity extends
the system's capacity to capture and learn from potential attackers even after
user disengagement. Siren introduces a paradigm shift in cybersecurity,
transforming traditional defense mechanisms into proactive systems that
actively engage and learn from potential adversaries. The research strives to
enhance user protection while yielding valuable insights for ongoing refinement
in response to the evolving landscape of cybersecurity threats.

Siren 项目通过欺骗、机器学习和主动威胁分析的战略整合来加强网络安全。该项目利用神话中的塞壬作为灵感，采用复杂的方法将潜在威胁引诱到受控环境中。系统具备实时分析和分类的动态机器学习模型，以确保对新兴网络威胁的持续适应能力。架构框架包括链接监控代理、针对动态链接分析的特定机器学习模型和通过模拟用户交互来加强威胁参与的蜜罐。蜜罐内的数据保护采用概率加密进行加固。此外，引入模拟用户活动扩展了系统对潜在攻击者的捕获和学习能力，即使在用户停止参与后仍然有效。Siren 在网络安全领域引入了范式转变，将传统的防御机制转变为主动参与和从潜在对手中学习的系统。该研究旨在提高用户的保护能力，并为不断变化的网络安全威胁的持续改进提供有价值的洞见。

Siren - 通过欺骗和自适应分析提升网络安全

Siren -- Advancing Cybersecurity through Deception and Adaptive Analysis

Deep neural networks are a promising tool for Audio Event Classification. In
contrast to other data like natural images, there are many sensible and
non-obvious representations for audio data, which could serve as input to these
models. Due to their black-box nature, the effect of different input
representations has so far mostly been investigated by measuring classification
performance. In this work, we leverage eXplainable AI (XAI), to understand the
underlying classification strategies of models trained on different input
representations. Specifically, we compare two model architectures with regard
to relevant input features used for Audio Event Detection: one directly
processes the signal as the raw waveform, and the other takes in its
time-frequency spectrogram representation. We show how relevance heatmaps
obtained via "Siren"{Layer-wise Relevance Propagation} uncover
representation-dependent decision strategies. With these insights, we can make
a well-informed decision about the best input representation in terms of
robustness and representativity and confirm that the model's classification
strategies align with human requirements.

本文研究音频事件分类中的不同输入表示所采用的相关特征。 作者使用 eXplainable AI （XAI）来比较两种模型体系结构的分类策略，并通过 Siren 的层级相关传播来获取相关热图，以揭示不同表示依赖的决策策略，从而为选择最佳的输入表示提供决策支持，并确认模型的分类策略与人的要求相符。