We present initial research on physics-mathematics modeling, computational simulation, and data visualization that aim at discovering the physics of the adversarial minds of threat actors who develop exploits and malware. In this research, the physics of an adversarial mind refer to laws that govern the dynamics of human cognition as reflected in malware. The physics of adversarial minds, in turn, may enable us to discover cognitive vulnerabilities that are exploitable through defensive cyber deception to persistently detect malware across many generations, or for as long as the malware are developed by threat actors whose cognition we have analyzed and have found to be vulnerable. We aim at discovering physics of cognition that pinpoint innate and hence possibly involuntary cognitive vulnerabilities, as well as developmental cognitive vulnerabilities. We expect these cognitive vulnerabilities to be persistent and recurring in a variety of manifestations. The enabler of the discovery of cognitive vulnerabilities in cyber space is a deep and thorough understanding of how the cognition of threat actors and cyber terrain interact with each other to produce exploits and malware. We aim at discovering the interplay between computing and the cognitive processes that take place in the mind of threat actors. In cognitive science, human cognition is often characterized via mathematical models. Our goal is to extend and adapt these models to operations in cyber space, and thus integrate them with elements of computing. A fusion of cognitive phenomena and computing in a mathematical form serves as basis for this research, which is envisioned to leverage cyber mechanisms to learn and then probe the cognition of threat actors for cognitive vulnerabilities. A key approach in this research is the small-scale decomposition of the presence of human mind in malware into particles. We develop small-scale mathematical models that characterize the individual action of those particles, their physical quantities, and how those physical quantities are related to each other. We encode in these small-scale models the inner workings of cognition in ways that resemble physical matter such as gasses and fluids, while representing the physics of cognition with high fidelity. We then exploit small-scale models to develop large-scale mathematical models of malware-reflected cognition. We discover physical properties of an adversarial mind in aggregate from a probabilistic examination via statistical physics and nonlinear dynamical systems theory of the underlying logical particles. Of particular interest are macroscopic physical quantities of cognition that emerge from the collective behavior of particles. In other words, this research discovers the thermodynamics of an adversarial mind using malware as measurable observable. This research explores statistical physics and dynamical systems theory similarly to how these disciplines were used to study gasses and fluids in conventional physics. By developing high-fidelity mathematical models that establish equivalence between cognition in malware and physical matter, this research creates potential for inheriting the past success of those methods.

Mathematical Discovery of the Physics of Adversarial Minds in Malware for Customized and Targeted Deception

**Moderator:** Ralph Crosby

**Speakers:** 

[Julia Downes](https://www.underline.io/speakers/77563-julia-downes)
 
[Dr. Jeffery Nichols](https://www.underline.io/speakers/77617-jeff-a.-nichols)
 
[Robert Bridges](https://www.underline.io/speakers/77574-robert-bridges)

[Jeremy Young](https://www.underline.io/speakers/77556-jeremy-young)
 
[Julian Rrushi](https://www.underline.io/speakers/77892-julian-rrushi)


Cyber Security

technical paper

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

<iframe width=1280 height=706 frameborder="0" scrolling="no" src="https://screencast-o-matic.com/player/creleVVcvRh?v=6&ff=1&title=0&controls=1" allowfullscreen="true"></iframe>

Julian Rrushi

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Mathematical Discovery of the Physics of Adversarial Minds in Malware for Customized and Targeted Deception

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