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Milos Manic, Ph.D. - VCU College of Engineering. Engineering East Hall, Room E2254, Richmond, VA, US

Milos Manic, Ph.D. Milos Manic, Ph.D.

Professor | VCU College of Engineering

Engineering East Hall, Room E2254, Richmond, VA, UNITED STATES

Dr. Milos Manic conducts research in the areas of machine learning, software defined networks and human-machine interfaces.

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Modern Heuristics Research Group

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Industry Expertise (3)

Computer Software Education/Learning Research

Areas of Expertise (5)

Computational Intelligence Techniques (Machine Learning) with Applications in Energy Cybersecurity and Human Machine Interfaces Software Defined Networks Fuzzy Neural Data Mining Techniques Energy Security Human-machine Interfaces

Accomplishments (3)

IEEE IES 2012 J. David Irwin Early Career Award (professional)

for “Outstanding research contribution in computational intelligence and its applications in energy related problems, network security and infrastructure protection, and robotics”.

Best Young Faculty Award (professional)

University of Idaho, 2008 – 2009 Academic Year

Fellow of the Outstanding Foreign Scholar Program (professional)

The Brain Korea 21 Chungbuk Information Technology Center at Chungbuk National University, 2008

Education (2)

University of Idaho: Ph.D., Computer Science 2003

University of Nis: M.Sc., Computer Science 1996

Media Appearances (4)

VCU virtual reality lab opens to awe of crowd

Richmond Times-Dispatch  print

2017-09-17

The fact that virtual reality appeals to senses helps humanize software, said Milos Manic, a computer science professor at VCU.

“People react better to visualization. Something they can feel, something they can see,” he said. “We can learn from the data and apply it to different areas.”

Students in VCU Engineering's VR lab.

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Bull’s-eye on small business Experts say hackers are preying on unprotected targets

Virginia Business  print

2017-06-30

"We are not thinking enough [about threats] until something forces us to. Why? Because it costs. End of story … There’s something called fault tolerance. It’s a fixed algorithm: If this happens, I’ll do this. The problem is the approach is reactive. You’re talking about something you know has happened, which has no value tomorrow, because tomorrow is going to be a new day. So here we come to resilience, which is the idea of intelligent response or getting up after you fell down. It cannot be treated with the same algorithm because it’s going to be a different fall. That brings another facet … in this game, which is some kind of machine-learning. Trying to figure out how proactively what can go wrong, and if something goes wrong, trying to stay one, two, three, five steps ahead of the bad guys. So machine learning, artificial intelligence, call it any which way you like, I’ve been teaching and preaching about it for the last 15 years," Manic said.

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Milos Manic, Ph.D., Strengthens Computer Science Department with over $300K Funding

Virginia Commonwealth University  

2015-04-21

Since joining Virginia Commonwealth University School of Engineering in August 2014, Milos Manic, Ph.D., has secured $310K to fund his Modern Heuristics Research Group. Only nine months into his time as Professor in the Department of Computer Science, Manic has big plans for his research that will lead to the establishment of a Virtual Reality Laboratory at VCU.

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To Relieve Holiday Stress, Techies Trot Out Artificial Intelligence

Style Weekly  print

2017-12-19

By now the journey to 2018 can feel more like a crawl than a mad dash.

There's pressure to entertain family, reconnect with old friends and take that special someone on a memorable date. But a Richmond startup says artificial intelligence can solve the indecision over where to go and what to do. While some experts caution against placing exaggerated faith in artificial intelligence, early adopters are hoping for a more perfect holiday experience.
Funny things happen when people look to have their desires affirmed, says Milos Manic, a Virginia Commonwealth University professor who specializes in machine-human interactions.

If artificial intelligence is promoted as an authority, people tend to place more trust in its recommendations, even if the same suggestions are made by a friendly stranger.

"There are many definitions, and this field is changing continually," he says of artificial intelligence. "But one common denominator is the ability [for machines] to learn."

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Selected Articles (5)

Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings IEEE Industrial Electronics Magazine

2016

Building automation systems (BAS), or building control systems (BCS), typically consist of building energy management systems (BEMSs), physical security and access control, fire/life safety, and other systems (elevators, public announcements, and closed-circuit television). BEMSs control heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings; more specifically, they control HVAC's primary components such as air handling units (AHUs), chillers, and heating elements. BEMSs are essential components of modern buildings, tasked with seemingly contradicting requirements?minimizing energy consumption while maintaining occupants? comfort [1]. In the United States, about 40% of total energy consumption and 70% of electricity consumption are spent on buildings every year. These numbers are comparable to global statistics that about 30% of total energy consumption and 60% of electricity consumption are spent on buildings. Buildings are an integral part of global cyber-physical systems (smart cities) and evolve and interact with their surroundings. As buildings undergo years of exploitation, their thermal characteristics deteriorate, indoor spaces (especially in commercial buildings) get rearranged, and usage patterns change. In time, their inner (and outer) microclimates adjust to changes in surrounding buildings, overshadowing patterns, and city climates, not to mention building retrofitting. Thus, even in cases of ideally designed BEMS/HVAC systems, because of ever-changing and uncertain indoor and outdoor environments, their performance frequently falls short of expectations. Unfortunately, the complexity of BEMSs, large amounts of constantly changing data, and evolving interrelations among sensor feeds make identifying these suboptimal behaviors difficult. Therefore, traditional data-mining algorithms and data-analysis tools are often inadequate.This article provides an overview of issues related to modern BEMSs with a multitude of (often conflicting) requirements. Because of massive and often incomplete data sets, control, sensing, and the evolving nature of these complex systems, computational intelligence (CI) techniques present a natural solution to optimal energy efficiency, energy security, and occupant comfort in buildings. The article further presents an overall architecture where CI can be used in BEMSs and concludes with a case study of the practical applications of using CI techniques in the BEMS domain.

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Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions IEEE Transactions on Industrial Informatics

2014

Building Energy Management Systems (BEMSs) are essential components of modern buildings that are responsible for minimizing energy consumption while maintaining occupant comfort. However, since indoor environment is dependent on many uncertain criteria, performance of BEMS can be suboptimal at times. Unfortunately, complexity of BEMSs, large amount of data, and interrelations between data can make identifying these suboptimal behaviors difficult. This paper proposes a novel Fuzzy Anomaly Detection and Linguistic Description (Fuzzy-ADLD)-based method for improving the understandability of BEMS behavior for improved state-awareness. The presented method is composed of two main parts: 1) detection of anomalous BEMS behavior; and 2) linguistic representation of BEMS behavior. The first part utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to build a model of normal BEMS behavior. The second part of the presented method computes the most relevant linguistic description of the identified anomalies. The presented Fuzzy-ADLD method was applied to real-world BEMS system and compared against a traditional alarm-based BEMS. Six different scenarios were tested, and the presented Fuzzy-ADLD method identified anomalous behavior either as fast as or faster (an hour or more) than the alarm based BEMS. Furthermore, the Fuzzy-ADLD method identified cases that were missed by the alarm-based system, thus demonstrating potential for increased state-awareness of abnormal building behavior.

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FN-DFE: Fuzzy-Neural Data Fusion Engine for Enhanced Resilient State-Awareness of Hybrid Energy Systems IEEE Transactions on Cybernetics

2014

Resiliency and improved state-awareness of modern critical infrastructures, such as energy production and industrial systems, is becoming increasingly important. As control systems become increasingly complex, the number of inputs and outputs increase. Therefore, in order to maintain sufficient levels of state-awareness, a robust system state monitoring must be implemented that correctly identifies system behavior even when one or more sensors are faulty. Furthermore, as intelligent cyber adversaries become more capable, incorrect values may be fed to the operators. To address these needs, this paper proposes a fuzzyneural data fusion engine (FN-DFE) for resilient state-awareness of control systems. The designed FN-DFE is composed of a three-layered system consisting of: 1) traditional threshold based alarms; 2) anomalous behavior detector using self-organizing fuzzy logic system; and 3) artificial neural network-based system modeling and prediction. The improved control system stateawareness is achieved via fusing input data from multiple sources and combining them into robust anomaly indicators. In addition, the neural network-based signal predictions are used to augment the resiliency of the system and provide coherent state-awareness despite temporary unavailability of sensory data. The proposed system was integrated and tested with a model of the Idaho National Laboratory's hybrid energy system facility known as HYTEST. Experiment results demonstrate that the proposed FNDFE provides timely plant performance monitoring and anomaly detection capabilities. It was shown that the system is capable of identifying intrusive behavior significantly earlier than conventional threshold-based alarm systems.

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Cyber-Physical System Security With Deceptive Virtual Hosts for Industrial Control Networks IEEE Transactions on Industrial Informatics

2014

A challenge facing industrial control network administrators is protecting the typically large number of connected assets for which they are responsible. These cyber devices may be tightly coupled with the physical processes they control and human induced failures risk dire real-world consequences. Dynamic virtual honeypots are effective tools for observing and attracting network intruder activity. This paper presents a design and implementation for self-configuring honeypots that passively examine control system network traffic and actively adapt to the observed environment. In contrast to prior work in the field, six tools were analyzed for suitability of network entity information gathering. Ettercap, an established network security tool not commonly used in this capacity, outperformed the other tools and was chosen for implementation. Utilizing Ettercap XML output, a novel four-step algorithm was developed for autonomous creation and update of a Honeyd configuration. This algorithm was tested on an existing small campus grid and sensor network by execution of a collaborative usage scenario. Automatically created virtual hosts were deployed in concert with an anomaly behavior (AB) system in an attack scenario. Virtual hosts were automatically configured with unique emulated network stack behaviors for 92% of the targeted devices. The AB system alerted on 100% of the monitored emulated devices.

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Autonomic Intelligent Cyber-Sensor to Support Industrial Control Network Awareness IEEE Transactions on Industrial Informatics

2013

The proliferation of digital devices in a networked industrial ecosystem, along with an exponential growth in complexity and scope, has resulted in elevated security concerns and management complexity issues. This paper describes a novel architecture utilizing concepts of autonomic computing and a simple object access protocol (SOAP)-based interface to metadata access points (IF-MAP) external communication layer to create a network security sensor. This approach simplifies integration of legacy software and supports a secure, scalable, and self-managed framework. The contribution of this paper is twofold: 1) A flexible two-level communication layer based on autonomic computing and service oriented architecture is detailed and 2) three complementary modules that dynamically reconfigure in response to a changing environment are presented. One module utilizes clustering and fuzzy logic to monitor traffic for abnormal behavior. Another module passively monitors network traffic and deploys deceptive virtual network hosts. These components of the sensor system were implemented in C++ and PERL and utilize a common internal D-Bus communication mechanism. A proof of concept prototype was deployed on a mixed-use test network showing the possible real-world applicability. In testing, 45 of the 46 network attached devices were recognized and 10 of the 12 emulated devices were created with specific operating system and port configurations. In addition, the anomaly detection algorithm achieved a 99.9% recognition rate. All output from the modules were correctly distributed using the common communication structure.

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