Transition to practice success story: Pablo Moriano - technology readiness & understanding critical security issues in large-scale networked systems

Pablo Moriano is a research scientist in the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL). He received Ph.D. and M.S. degrees in Informatics from Indiana University (IU). Previously, he received M.S. and B.S. degrees in Electrical Engineering from Pontificia Universidad Javeriana in Colombia.

Moriano’s research lies at the intersection of data science, network science, and cybersecurity. In particular, he develops data-driven and analytical methods to discover and understand critical security issues in large-scale networked systems. He relies on this approach to design and develop innovative solutions to address these. Applications of his research range across multiple disciplines, including the detection of exceptional events in social media, internet route hijacking, and insider threat behavior in version control systems. His research has been published in Computer Networks, Scientific Reports, Computers & Security, Europhysics Letters, and the Journal of Statistical Mechanics: Theory and Experiments as well as the ACM CCS International Workshop on Managing Insider Security Threats.

In the past, he interned at Cisco with the Advanced Security Group. He is a member of IEEE, ACM, and SIAM and has received funding from Cisco Research.

Trusted CI sat down with Moriano to discuss his transition to practice journey, what he has learned, and his experience with the Technology Readiness Level Assessment tool.

Trusted CI: Tell us about your background and your broader research interests.

My background is in electrical engineering.

I was born and grew up in Colombia. I attended Pontificia Universidad Javeriana to pursue a degree in electrical engineering. I remember enjoying so much math-related and physics classes, which are the foundations of electrical engineering. I did pretty well on those topics.

In my engineering classes, at the end of the semester, we had the same kinds of final projects as in the US, called capstones. The idea of these projects was to integrate the learnings from different subjects to solve a real engineering challenge. In these types of activities, you usually measure the impact a technology has on solving a real problem.

In general, I enjoyed going beyond what I learned in classes. I participated in math-related contests, which allowed me to sharpen my analytical skills. By the end of my undergraduate studies, I had a professor that always was encouraging me to try research and go to grad school. I worked under his supervision to complete my undergraduate thesis. In my undergraduate thesis, I developed real-time control algorithms for a non-linear laboratory plant that used magnetic levitation. That was a starting point to be involved with research and pursuing opportunities in that direction later during grad school.

Currently at Oak Ridge National Laboratory (ORNL), I am a researcher in the computer science and mathematics division. I develop data-driven and analytical models for understanding and identifying anomalies in large-scale networked systems such as cyber-physical systems, communication systems, and socio-technological systems like social media.

This is broad, but common to these systems, also known as complex systems, is that they are made of a large number of elements and that these elements interact in non-linear ways, often producing collective behavior. This collective behavior cannot be explained by analyzing the aggregated behavior of the individual parts. For example, on the internet, a large number of independent and autonomous networks, also known as Autonomous Systems (ASes), such as internet service providers, corporations, and universities are constantly interacting between each other to share reachability of information with respect to where to find destination IP addresses. To do so, ASes communicate using a protocol called Border Gateway Protocol (BGP). The details of the protocol and the interactions between Ases are complex and subject to engineering and economic constraints. However, their aggregated behavior allows users around the globe to navigate the web—and use many other services—by allowing them to find the resources they need every time they search online.

In these networked systems such as the internet, their emergent behavior may sometimes be anomalous or substantially different. This idea in the cybersecurity space is really important because it may be an indication of a problem or in the worst case scenario an indication of an upcoming attack. A similar approach as described in the case of the internet may be used to study other real-world networked systems.

Trusted CI: Tell us about your experience using the Technology Readiness Level (TRL) assessment.

When I was finishing my studies at IU, I had the chance to participate in a Trusted CI workshop in Chicago. At that time Florence [Hudson] was leading that effort.

In addition to getting to interact with other researchers, the intention of the workshop was to provide an opportunity to share the latest research efforts in the cybersecurity space. The emphasis was also to showcase previous academic research that was subsequently translated to practice, delivering a solution to a practical need. That event was very fruitful and allowed me to interact with other peers, have a fresh perspective into transition to practice, and grow my network.

Later, I was invited to participate in the [Trusted CI] cohort. The intention of the cohort is to bring together researchers interested in solving real-world problems in cybersecurity and help them do so. During the process, you get mentorship through the process of transition to practice. In addition, the experience allows you to foster interactions with external stakeholders to receive feedback and support during the process.

The cohort, under the leadership of Ryan [Kiser] has been developing different useful tools like the TRL assessment and canvas proposition.

The TRL assessment idea is not new. In fact, it came from NASA in the 70s. However, it has not been widely used as a resource for transition to practice by cybersecurity researchers. In particular, the TRL assessment provides a tool—similar to a decision tree—to help classify the level of maturity of a technology. Originally, it was conceived using a nine-level scale (from one to nine) with nine being the most mature technology. The TRL assessment is super helpful, for example, to identify the next steps in the transition to practice journey. The fundamental assumption of the tool is that by recognizing where you are at the moment, you will have a clearer picture on how to proceed next.

For instance, when searching for funding opportunities, having a clear picture of where you are (with respect to the maturation of the technology) will allow you to better target specific sources of funding, enabling next steps in the transition to practice journey. In my experience at ORNL, it is an important decision element when deciding which funding steps to pursue in the overall R&D pipeline across several federal agencies.

Trusted CI: Talk about your experience with the funding you were pursuing.

Here at ORNL, there are different opportunities for funding, including specific ones for transitioning to practice your research. One of the fundamental advantages of working in a national laboratory is that it is an environment that bridges academia and industry. In that sense, the work we do is mission-driven and has real-world impact—often with some component of transition to practice as a measure of impact. That means that both research and development are tied together and highly appreciated.

I already applied to an internal funding opportunity for transition to practice. The main purpose of the solicitation was to look for technologies at a minimum of TRL 5 (requiring a working high-fidelity prototype which is beyond basic research) to support the necessary steps for technology maturation. The final goal was to help convert the prototype into an actual usable system that may open the door to commercialization opportunities.

By the time I applied, my technology was not at TRL 5 and of course that was the basis of the feedback that I received. I, however, enjoyed and learned during the process and realized that there are other solicitations that may be more adequate to help me to increase the TRL of my technology (from proof-of-concept to prototype). Throughout the process, I had the chance to talk with practitioners out there and learn about the practical challenges they faced with current deployed systems. I also learned about other federal agencies such as DOE, DHS, and DARPA (and people there) looking for proposals with the focus on transition to practice. That was encouraging.

Trusted CI: Tell us more about your technology.

It's a technology that aims to detect and inform network operators in near real-time about routing incidents (of different severity) by leveraging update messages transmitted in BGP. The fundamental characteristic of the intended system is that it is somehow automatic (leveraging AI/ML methods), detects incidents as soon as possible (allowing quick turnaround), and is able to detect subtle attacks in which only a small fraction of IP prefixes are affected (usually the ones performed through man-in-the-middle).

Trusted CI: Describe where you’d say you are in your transition to practice.

Through the Trusted CI cohort, I had the opportunity to use that TRL tool to evaluate the current state of my technology. By using the tool and the decision criteria behind it, I am pretty confident that the technology at this stage is on what is called Level 3 or proof-of-concept.

The next step will be to mature the technology to build a high-fidelity working prototype that can be used to detect routing incidents using real-time data.

This particular BGP project came from my dissertation research. I recently published a paper about it. However, beyond this project, I see that tools like the TRL assessment are essential to guide my next steps. For that reason, this experience easily translates to other ongoing research projects that go through the whole R&D pipeline.

Trusted CI: Where do you see your research heading down the road?

I'm pursuing the idea of maturing the BGP technology. The problem of BGP incident detection has been in the community for many years. BGP anomaly detection is a difficult space with little room for improvement. For that reason, you need to be very precise about the added value the technology is offering. I also started new projects in the cybersecurity space where I see a clear path between research and development. Currently, these are in earlier stages but may benefit from early consideration through the use of tools like the TRL assessment and the Trusted CI cohort experience.