AISecLab

Analytics and Information Security for Complex Systems Lab (AISecLab) logo

The mission of the Analytics and Information Secuity Lab (AISecLab) is to promote top-tier research on analytics and information security for complex systems, and to enrich education programs in related areas. The AISecLab aims to serve as the platform for interdisciplinary research, university-industry collaboration, cutting-edge knowledge dissemination and training.

AISecLab Members

Schucheng Yu, Lead

Shucheng Yu, Lead

Xiaojiang Du

Lei Wu

Min Song

K.P. (Suba) Subbalakshmi

Junjian Qi

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

Research Areas

Data security and privacy
Applied cryptography
Blockchain and distributed trust
Security and privacy in machine learning
Secure computation
Operational security and resiliency of complex networked systems, such as interdependent security and resiliency of power grid and other infrastructures
Applied machine learning
Wireless security and secure spectrum management
Security and privacy of Internet of Things

Current Projects

Shucheng Yu

FUNDED BY NSF (2017 - 2020)

In IoT applications, various wireless ad hoc networks could be formed by IoT devices of different hardware resources/interfaces and there might be no prior trust among them. This project aims to allow heterogeneous IoT devices to form an ad hoc network, considering devices with limited cryptographic capabilities and those with no cryptography at all. Three research tasks are defined to enable initial trust establishment with minimal user involvement, authenticated secret key extraction based on device mobility, and key-free wireless communication security via friendly jamming respectively.
More about Shucheng Yu
Shucheng Yu

FUNDED BY NSF (2014-2019, with non-cost extension)

This project utilizes physical layer security approaches to develop innovative key agreement and message authentication mechanisms for wireless body area networks (WBANs). Unlike existing approaches (e.g., biometric-based approaches), the proposed authentication system does not require additional hardware, error reconciliation process and bit synchronization, and thus is suitable for resource-constrained and capacity-limited medical sensor nodes in WBANs. The project focuses on the following tasks: (1) theoretical studies; (2) design of key agreement schemes; (3) development of message authentication system; and (4) system implementation and validation. The theoretical studies focus on the connections between the channel reciprocity, channel dependency and key generation. Based on the results of theoretical studies, practical key agreement schemes that use a set of dynamic wireless channel features among the communication partners are developed.
More about Shucheng Yu
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.
More About Min Song
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.
More about Lei Wu
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.
More about Lei Wu
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.
More about Lei Wu
Lei Wu

FUNDED 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.
More about Lei Wu
Dov Kruger

This research, as yet unfunded, demonstrates that an attacker can subtly alter a part to weaken or otherwise degrade it. We then demonstrate that we can construct a representation that is more compact than gcode and more difficult to subtly alter, as well as providing digital encryption and authentication so that the part cannot be intercepted along the way between the designer’s desk and the printer.
More About Dov Kruger
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.
Learn More About K.P. (Suba) Subbalakshmi
K.P. (Suba) Subbalakshmi and and R. Chandramoulii

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.
Learn More About K.P. (Suba) Subbalakshmi
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.
Learn More About K.P. (Suba) Subbalakshmi
Junjian Qi

FUNDED BY NSF (2020 - 2025)
The goal of this CAREER project is to initiate a new data-driven research direction for studying cascading failure and power system resilience and develop systematic, transformative theoretical foundations and algorithmic techniques for data-driven cascading failure analysis, prevention, and intervention, addressing the inherent limitations of existing approaches, refreshing the understanding of real-world cascading failure, and providing tools to analyze, prevent, and finally intervene cascading failures. Network science, statistical inference, data science, and deep learning will be seamlessly integrated with power system domain knowledge in order to obtain a unique solution to the very challenging problem.

Search form