Dr. Jonathan Mugan Profile

Dr. Jonathan Mugan

at 21CT

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Proceedings Article | 22 May 2014 Paper

The application of top-down abstraction learning using prediction as a supervisory signal to cyber security

Jonathan Mugan, Aram Khalili

Proceedings Volume 9119, 91190D (2014) https://doi.org/10.1117/12.2053327

KEYWORDS: Computing systems, Defense and security, Defense systems, Computer programming, Reliability, Systems modeling, Binary data, Dielectrophoresis, Complex systems, Machine learning

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Current computer systems are dumb automatons, and their blind execution of instructions makes them open to attack. Their inability to reason means that they don't consider the larger, constantly changing context outside their immediate inputs. Their nearsightedness is particularly dangerous because, in our complex systems, it is difficult to prevent all exploitable situations. Additionally, the lack of autonomous oversight of our systems means they are unable to fight through attacks. Keeping adversaries completely out of systems may be an unreasonable expectation, and our systems need to adapt to attacks and other disruptions to achieve their objectives. What is needed is an autonomous controller within the computer system that can sense the state of the system and reason about that state. In this paper, we present Self-Awareness Through Predictive Abstraction Modeling (SATPAM). SATPAM uses prediction to learn abstractions that allow it to recognize the right events at the right level of detail. These abstractions allow SATPAM to break the world into small, relatively independent, pieces that allow employment of existing reasoning methods. SATPAM goes beyond classification-based machine learning and statistical anomaly detection to be able to reason about the system, and SATPAM's knowledge representation and reasoning is more like that of a human. For example, humans intuitively know that the color of a car is not relevant to any mechanical problem, and SATPAM provides a plausible method whereby a machine can acquire such reasoning patterns. In this paper, we present the initial experimental results using SATPAM.

Proceedings Article | 28 May 2013 Paper

A developmental approach to learning causal models for cyber security

Jonathan Mugan

Proceedings Volume 8751, 87510A (2013) https://doi.org/10.1117/12.2014418

KEYWORDS: Algorithm development, Computing systems, Machine learning, Systems modeling, Defense systems, Information security, Defense and security, Computer intrusion detection, Environmental sensing, Switches

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To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,^{1, 2} and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.³ Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

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