Conceptual futuristic look at information technology of internet of things with multiple tentacle-like threads leading to data points.

Analytics and Information Security for Complex Systems Laboratory

The Analytics and Information Security for Complex Systems Lab (AISecLab) promotes top-tier research on analytics and information security for wireless networks, cyber-physical systems, Internet of Things, power and energy systems, and trustworthy AI systems.

Research Areas

  • Information and network security, privacy-enhancing techniques, post-quantum cryptography (PQC)

  • Trustworthy machine learning and AI, on-device learning, and edge-based AI

  • Artificial General Intelligence (AGI) applications, including misinformation detection, communications and healthcare

  • Power systems and electricity markets, integration of renewables, and the co-optimization of critical interdependent infrastructures

  • Internet of Things, cyber-physical systems and their security & privacy issues

  • Radio spectrum management, open radio access network (O-RAN), mobile computing

  • Blockchain and distributed trust

  • Information and communication in unmanned aerial vehicle (UAV) and 5G/6G networks

AISecLab Members

Affiliated Members

Cristina Comaniciu

Stevens Institute of Technology

Yi Guo

Stevens Institute of Technology

Kevin Lu

Stevens Institute of Technology

Wenjia Li

New York Institute of Technology

Shaoshuai Mou

Purdue University

Sachin Shetty

Old Dominion University

Qiang Tang

University of Sydney

Chee-Wooj Ten

Michigan Technological University

Mehmet Can Vuran

University of Nebraska - Lincoln

Jiawei Yuan

University of Massachusetts Dartmouth

Hong-Sheng Zhou

Virginia Commonwealth University

Current Projects

Towards Understanding and Handling Problems Due to Coexistence...

Towards Understanding and Handling Problems Due to Coexistence of Multiple IoT Platforms

Xiaojiang Du

Funded by NSF CNS Core Program

In this ongoing project, we investigate the categorization and understanding of Multi-Platform Coexistence (MPC) problems in smart IoT systems. We will systematically uncover and categorize problems, due to coexistence of multiple IoT platforms. To obtain in-depth understanding of MPC problems, we will analyze causes behind each type and quantitatively measure various aspects of them. We will also study how to handle the MPC problems.

An Accelerated Computation Architecture for State Transition...

An Accelerated Computation Architecture for State Transition Representation and Applications

Xiaojiang Du

Funded by NSF SHF Program

In this ongoing project, we design and implement a novel accelerated computation architecture tailored to the analysis of state transition representations, which is a foundational abstraction used to model the behavior of dynamic systems in domains such as software verification, hardware design, smart environments, and cyber-physical systems.

The Ontology of Inter-Vehicle Networking

The Ontology of Inter-Vehicle Networking with Spatial-Temporal Correlation and Spectrum Cognition

Min Song

Funded by NSF NeTS

In this ongoing project, we investigate the fundamental challenges of inter-vehicle networking, including the theoretical foundation and constraints in practice that enable such networks to achieve their performance limits. Efficient algorithms will be designed to achieve fast neighbor discovery using reinforcement learning and case-based reasoning scheme. The results will advance the knowledge of opportunistic communications and facilitate engineering practice for much-needed applications in vehicular environments.

Communication Framework for Advanced Community Resilience Microgrids

US Ignite: Focus Area 1: An Integrated Reconfigurable Control and Self-Organizing Communication Framework for Advanced Community Resilience Microgrids

Lei Wu

Funded by NSF (2017 - 2019)

The United States is experiencing an increasing frequency of catastrophic weather events that inflict serious social and economic impacts. A critical issue associated with such catastrophes is the availability of electricity for the recovery efforts. Community resilience microgrids can connect critical loads in the community and share the distributed energy resources of multiple providers to enhance the availability of electricity supply during disruptions. This project explores coordinated dynamic control strategies for distributed energy resources and loads of multiple owners across different timescales, together with their distinct communication requirements, which could enhance the resilient and economic operation of community microgrids in both grid-connected and islanded modes.

Multi-Stage and Multi-Timescale Robust Co-Optimization Planning

Multi-Stage and Multi-Timescale Robust Co-Optimization Planning for Reliable and Sustainable Power Systems

Lei Wu
Funded by DOE/Clarkson University (2016 - 2019)

This project will develop a decision-support tool that augments existing power utility capabilities to support collaborative planning, analysis, and implementation of emerging variable and distributed power systems and help effectively mitigate risks and uncertainties in both short-term operation and long-term policy/technology changes. The developed tool will assist power market participants, utilities and regulatory agencies in analyzing economic, reliability, and sustainability issues when considering options for planning new and upgraded transmission facilities to accommodate existing and emerging generation sources.

Co-Optimization Planning for Interdependent Infrastructure Systems

Improving Energy Reliability by Co-Optimization Planning for Interdependent Electricity and Natural Gas Infrastructure Systems

Lei Wu
FUNDED BY DOE/CLARKSON UNIVERSITY (2016 - 2019)

The electricity grid and the natural gas network are two essential infrastructure systems in the U.S. energy industry, which were originally designed and managed independently. However, because of the planned retirement of many coal-fired generators, the deeper penetration of renewable energy sources, and the commercially sustainable gas price, their interactions have intensified over the last five years. Hence, in order to ensure environmentally friendly, reliable, and cost-effective electricity and gas production and delivery, it is important to jointly optimize these two systems. However, due to their scales, complexities, and requirements/regulations, such a co-optimization planning problem is very challenging in both modeling and computation aspects. To address this critical challenge, this project will build analytical decision support models and design efficient solution methods to aid the energy industry in formulating and computing practical-scale co-optimization problems.

FUNDED BY DOE/CLARKSON UNIVERSITY (2016 - 2019)

The electricity grid and the natural gas network are two essential infrastructure systems in the U.S. energy industry, which were originally designed and managed independently. However, because of the planned retirement of many coal-fired generators, the deeper penetration of renewable energy sources, and the commercially sustainable gas price, their interactions have intensified over the last five years. Hence, in order to ensure environmentally friendly, reliable, and cost-effective electricity and gas production and delivery, it is important to jointly optimize these two systems. However, due to their scales, complexities, and requirements/regulations, such a co-optimization planning problem is very challenging in both modeling and computation aspects. To address this critical challenge, this project will build analytical decision support models and design efficient solution methods to aid the energy industry in formulating and computing practical-scale co-optimization problems.

UNDED BY NSF (2013 - 2019, WITH NON-COST EXTENSION)

The objective of this CAREER proposal is to study the impacts of short-term variability and uncertainty of renewable generation (RG) and demand response (DR) as well as hourly chronological operation details of energy storage (ES) and generators on the long-term planning via the proposed co-optimized generation, transmission, and DR planning solutions. The interaction among variability, uncertainty, and constraints from long-term planning and hourly chronological operation will be quantified for enhancing security and sustainability of power systems with significant RG, DR, and ES. This research can be used to evaluate effective load carrying capability (ELCC) of variable energy sources, and to study policies on portfolios of energy production and storage techniques. The research and educational findings would help educate engineers to meet challenges of the secure and sustainable electricity infrastructure.

Rumor Source Detection in On-Line Social Networks

Rumor Source Detection in On-Line Social Networks

K.P. (Suba) Subbalakshmi

Online social networks are extremely popular and known for being an expedient disseminator of information. This ease of information dissemination can be a double-edged sword as social networks can also be used to spread rumors, or computer malware. For instance, in 2013, a fake tweet originating from a hacked Associated Press Twitter account about bombings in the White House caused the Dow Jones Industrial Average to drop 145 points within 2 min. Clearly it is necessary to detect the sources of such misinformation for rapid damage control as well as to facilitate the design of sophisticated policies to prevent further viral spreading of misinformation through social networks in the future. Since practical online social networks are vast, it is impossible to continuously monitor the entire social network. One method to deal with this problem is to observe only a subset of designated nodes (called sensors). Although this approach offers advantages, there is a possibility that some percentage of these sensor nodes may be unavailable at certain times. We address the problem of rumor source detection under these circumstances. We use generative adversarial and autoencoder neural network architectures to overcome the problem of missing information. This algorithm is then tested on datasets that contain Twitter conversation threads associated with different newsworthy events including the Ferguson unrest, the shooting at Charlie Hebdo, the shooting in Ottawa, the hostage situation in Sydney and the crash of a Germanwings plane.

Deception & Author Identity Detection, Author Validation From Written Text

Deception Detection, Author Identity Detection, Author Validation From Written Text

K.P. (Suba) Subbalakshmi

As more of our lives become entwined with the Internet, we consume more and more information online. But how accurate is this information? How safe is it to trust strangers? There are many cases of fraud and crime committed online and via social media. In this part of our research work, we grapple with some fundamental problems in this area. Is it possible to detect whether or not someone is deceiving you online? Is the person chatting with your child on social media a sexual predator? Who exactly are you conversing with? How honest are they? These are some of the questions we set out to answer in this set of projects. Examples include scam detection from written text, author gender identification, author identity verification, detecting plagiarism, etc.

DoS Attacks and Counter Measures in Dynamic Spectrum Access Networks

Denial-of-Service Attacks and Counter Measures in Dynamic Spectrum Access Networks

K.P. (Suba) Subbalakshmi

Primary user emulation attack (PUEA) is a denial of service (DoS) attack unique to dynamic spectrum access (DSA) networks. We were the first group to develop a mathematical model for PUEA and one of the first groups to develop several strategies for detecting and mitigating PUEA. We then studied the impact of PUEA on the performance of secondary networks. Specifically, we analyze how PUEA affects call dropping and delay in secondary networks carrying real-time traffic and non-real-time traffic, respectively. We consider two types of malicious users: (i) “obstructive” malicious users whose sole intention is to evacuate secondary users, and (ii) “greedy” malicious users, who not only evacuate secondary users but also use the spectrum to transmit their own traffic. Numerical results indicate that PUEA can increase the number of dropped calls by up to two orders of magnitude and can increase the mean delay by up to a factor larger than two. We then evaluate the performance of secondary networks that deploy the protocols which we proposed previously to mitigate PUEA. Our protocols are found to reduce the number of dropped calls by up to one order of magnitude. Our protocols are also shown to provide almost the same delay performance as that of a system with no PUEA, for low malicious traffic load.