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Grant: Idea to Innovation

University of Windsor

$125,000.00

NSERC I2I Phase Ia: An Intelligent Framework for Social Engineering Cyber Security Training

FY 2021-22

Summary
Human beings are the weakest link in cybersecurity, and are, in fact, more vulnerable than computers. In recent years, countless major security incidents have succeeded because of human errors and the lack of appropriate security training and awareness. In this regard, although there is a wide range of cybersecurity products in the market to protect computers and networks, almost none or a few immature products exist for developing and training users in cybersecurity awareness. At present, organizations are still relying on hands-on workshops, online courses, and traditional training methods for cybersecurity awareness, which are ineffective and unscalable. In this regard, we have developed and patented a new multilayer technology for cybersecurity awareness training to help organizations understand and mitigate potential security risks associated with social engineering threats. Our technology utilizes the principles of artificial intelligence, gamification theory and cybersecurity strategies for enabling organizations to implement a social engineering firewall. The system provides an estimation of potential social engineering threats for a given organization by measuring the digital footprint of the organization assets on public sources, including the Internet, social networks and media, among others. Based on the estimated social engineering threats, using deep reinforcement learning (RL) algorithms our technology constructs and executes an interactive anti-social engineering training program that combines both passive and active training. Our technology then creates and recommends a social engineering firewall security strategy that is used within the organization network to reduce and mitigate the risk of any potential social engineering threats. The main goal of this project is to develop a scalable prototype that gives a proof of concept that the reinforcement learning approach will craft and execute users-specific social engineering attacks. The RL prototype aims at interacting with the users by performing social engineering attacks via different types of media, including social/professional/research networks and/or emails.
Program
Idea to Innovation
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
567660-2021
Name nom
Rueda, Luis
Department département
Computer Science, School of
Organization id
37
Institution établissement
University of Windsor
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2021
Competition year année de concours
2021
Award amount
125000
Program id
I2IPJ
Program name en
Idea to Innovation
Program name fr
De l'idée à l'innovation
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2710
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information systems design
Research subject fr
Conception de systèmes d'information
Installment
inst:1
Partie
1
Nb partie
1
Application title
NSERC I2I Phase Ia: An Intelligent Framework for Social Engineering Cyber Security Training
Keyword
NSERC
Application summary
Human beings are the weakest link in cybersecurity, and are, in fact, more vulnerable than computers. In recent years, countless major security incidents have succeeded because of human errors and the lack of appropriate security training and awareness. In this regard, although there is a wide range of cybersecurity products in the market to protect computers and networks, almost none or a few immature products exist for developing and training users in cybersecurity awareness. At present, organizations are still relying on hands-on workshops, online courses, and traditional training methods for cybersecurity awareness, which are ineffective and unscalable. In this regard, we have developed and patented a new multilayer technology for cybersecurity awareness training to help organizations understand and mitigate potential security risks associated with social engineering threats. Our technology utilizes the principles of artificial intelligence, gamification theory and cybersecurity strategies for enabling organizations to implement a social engineering firewall. The system provides an estimation of potential social engineering threats for a given organization by measuring the digital footprint of the organization assets on public sources, including the Internet, social networks and media, among others. Based on the estimated social engineering threats, using deep reinforcement learning (RL) algorithms our technology constructs and executes an interactive anti-social engineering training program that combines both passive and active training. Our technology then creates and recommends a social engineering firewall security strategy that is used within the organization network to reduce and mitigate the risk of any potential social engineering threats. The main goal of this project is to develop a scalable prototype that gives a proof of concept that the reinforcement learning approach will craft and execute users-specific social engineering attacks. The RL prototype aims at interacting with the users by performing social engineering attacks via different types of media, including social/professional/research networks and/or emails.