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IoT honeynet for military deception and indications and warnings

机译:物联网蜜网用于军事欺骗以及指示和警告

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Honeyman, named for the American Revolutionary War spy and source of disinformation, is an IoT distributed deception platform (DDP). aka "honeynet", based approach to military deception and indications and warning (I&W) generation. While DDP approaches have evolved from single honeypots to complex network architectures and have resolved previous challenges associated with revealing a DDP's signature or "fingerprint" including virtual device information, and therefore have become applicable for IoT uses, these approaches are still bounded in their application to cybersecurity purposes only. For example, data positioned as cyber-bait is meant only to draw in a cyber attacker but not to influence a strategic level of decision-making such as military or national security decisions. Additionally, monitoring within the DDP gathers data to model attackers' cyber behavior and patterns for explicit purpose of identifying new offensive cyber techniques and thwarting new attacks. Honeyman combines a proxy military logistics and readiness reporting IoT comprised of a mixture of virtual and physical devices with non-cyber information operations for military deception and to stimulate nation-state adversary behavior within the DDP. A machine learning (ML)-based traffic analysis model leverages observations within the honeynet to forecast an adversary's physical military activity thereby providing critical I&W. Further research is needed to optimize the combination of physical and virtual IoT devices for best deception performance, to evolve the tradecraft of dynamic cyber-bait, and to refine appropriate ML-based I&W models.
机译:霍尼曼(Honeyman)是IoT分布式欺骗平台(DDP),以美国独立战争间谍和虚假消息来源而得名。亦称“蜜网”,是基于军事欺骗,指示和警告(I&W)生成的方法。尽管DDP方法已从单一蜜罐演变为复杂的网络体系结构,并解决了以前与揭示DDP的签名或“指纹”(包括虚拟设备信息)相关的挑战,因此已适用于IoT用途,但这些方法仍在其应用范围内受到限制。仅出于网络安全目的。例如,定位为网络诱饵的数据仅意味着吸引网络攻击者,而不会影响诸如军事或国家安全决策等战略决策水平。此外,DDP内的监视会收集数据以对攻击者的网络行为和模式进行建模,以明确识别新的攻击性网络技术并阻止新的攻击。霍尼曼结合了代理军事后勤和准备状态报告物联网,该物联网由虚拟和物理设备以及非网络信息操作组成,用于军事欺骗,并刺激DDP内的民族国家对手行为。基于机器学习(ML)的流量分析模型利用蜜网中的观察来预测对手的身体军事活动,从而提供关键的I&W。需要进一步研究以优化物理和虚拟IoT设备的组合,以实现最佳欺骗性能,发展动态网络诱饵的技巧,并完善基于ML的I&W模型。

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