Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

Anomalous DNS Query Detection Using Machine Learning Approaches

For organizations that use the Internet, their employees will visit thousands of websites every day. However, there is a chance that the destination website is not safe to visit. Such websites may be fraudulent, phishing, or even data-stealing related. On the other hand, determining if the target website link is suspicious or not could help to prevent potential harm. Using a filtered list is the most straightforward way. The problem is, as the database for malicious websites is growing, hackers’ minds are also developing, which requiring a more profound way to deal with such a problem. This project aims to find any anomalous website visit attempt by using machine learning algorithms to solve the problem. As eSentire is a cybersecurity company dedicated to bringing solutions to companies who are having cybersecurity concerns, this project will serve as a reference for eSentire to solve related problems with more options.

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Faculty Supervisor:

Hassan Khan

Student:

Partner:

eSentire

Discipline:

Computer science

Sector:

Information and Communications Technology; Artificial Intelligence; Technology

University:

University of Guelph

Program:

Accelerate

Land-use change analysis for the Annapolis Valley Sand Barrens, a globally rare ecosystem at risk

This project will support the development and implementation of a collaborative conservation strategy for the Annapolis Valley Sand Barrens, a globally rare and endangered ecosystem found in Nova Scotia. It has been estimated that only 3% of the Annapolis Valley Sand Barrens remains today, lost primarily due to competing land-uses such as urban development, agriculture and quarrying. More specifically, the project will help to identify and prioritize areas for various land protection and stewardship activities, quantify the extent of impacts from various competing land-uses, and to evaluate baseline conditions for long-term effectiveness monitoring indicators. This work will contribute to provincial and federal biodiversity conservation commitments made through the Accord for the Protection of Species at Risk and re-affirmed through the Pan-Canadian Approach to the Transformation of Species at Risk Conservation in Canada.

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Faculty Supervisor:

David Colville;Ian Spooner

Student:

Partner:

Clean Annapolis River Project

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

Acadia University; Nova Scotia Community College

Program:

Accelerate

Automation & Orchestration for Improved Security Communication

Speed is incredibly important when addressing issues with computer security. The longer the time between the attack’s start and resolution, the more assets that attackers can steal from a company. There are various security platforms that can alert a company to a cyber-attack. This research project aims to combine knowledge from all these platforms together at faster speeds than a human would be able to do. The cooperation between security platforms will allow ISA Cybersecurity Inc. to detect and respond to cyber-attacks faster than previously possible. This will benefit them in protecting business, and by proxy Canadian citizens, data from cyber criminals.

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Faculty Supervisor:

Charlie Obimbo

Student:

Partner:

ISA Cybersecurity

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Optimization of key-to-key collision repair process system

The intent of this project is to reduce the cycle time in the vehicle repair process across the Carstar network. The project also includes developing process models that will help in guiding Carstar to reduce cycle time in major process steps for all stores of varying size and disparate locations. During the process of investigation and analyzing the data, the waste (inventory limitations, conflicts, redundant process steps and blockage) will be identified and eliminated. Also, the cost analysis will be documented for each part of the repair process to help show the value of improving the process and the impact on severity, touch time etc. Establishing new process models with improved cycle time will benefit company to remain competitive in repairing vehicles with best quality, at lowest price and at the same time helping the Carstar be profitable.

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Faculty Supervisor:

Robert Fleisig

Student:

Partner:

Carstar Automotive Canada

Discipline:

Engineering

Sector:

Other services (except public administration)

University:

McMaster University

Program:

Accelerate

Building and Evaluating a Consolidated SIEM (Security Information and Event Management) Threat Identification

Businesses are collecting more and more data, but they do not have the manpower to properly analyse it. This project will implement a proof of concept for a system that uses machine learning to improve the detection of cyber threats. The machine learning algorithm will receive information from many different data sources, detect where there is suspicious activity, and alert a cyber analyst. By adding a machine learning algorithm to the arsenal of cyber analysts, the analysts will be able to cut down on the time it takes to react to the threats. The project will produce reports and documents analyzing the effectiveness of the machine learning algorithm.

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Faculty Supervisor:

Rozita Dara

Student:

Partner:

Farm Credit Canada

Discipline:

Computer science

Sector:

Finance and Insurance

University:

University of Guelph

Program:

Accelerate

Creating a comparison and alert methodology for managing the CCTX feed

Most collaborations and government departments share their threat data feed in Data Exchange. Inescapably, nowadays with increasing threat data, it is a challenge to extract a large amount of threat data and unify the format more quickly. And as more and more companies join in sharing, the redundancy of this duplicate data will increase dramatically. This project proposes machine learning algorithms for automatic format conversion to extract threat information from the traffic data, and convert them into STIX format and detect whether these structured feeds already exist in CCTX. And a dashboard is developed for security analysts to compare the frequency in feeds.

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Faculty Supervisor:

Ali Dehghantanha

Student:

Partner:

Canadian Cyber Threat Exchange

Discipline:

Computer science

Sector:

Other services (except public administration)

University:

University of Guelph

Program:

Accelerate

Malicious/phishing Website Detection

Malicious websites in general, and phishing websites in particular, attempt to mimic legitimate websites to trick users into trusting them. The goal of the project is to develop algorithms for detecting these malicious websites in two contexts:
• detecting if a site visited by a user is a malicious site
• detecting malicious sites that mimic legitimate sites

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Faculty Supervisor:

Xiaodong Lin

Student:

Partner:

Arctic Wolf Networks

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Real-time Automated Security Report Generation

In today’s world, organizations protect themselves and their customer’s data through the implementation of complex cybersecurity solutions composed of many different nodes, each generating constant streams of data. Building reports from this data through the calculation of various metrics can provide much needed visibility into the state of the environment. However, building such reports can be a tedious and time-consuming process. Automated report generation can provide fast, clear views into the current or changing environment in varying levels of detail to allow for quicker incident detection and response as well as decision making.
This research project is a continuation of the Reporting Automation Platform (RAP) project that was started last year and will involve expanding the automated reporting platform to increase its functionality to generate more detailed reports. This project will also study the effects of integrating additional data streams into the platform.

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Faculty Supervisor:

Charlie Obimbo

Student:

Partner:

ISA Cybersecurity

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Guelph

Program:

Accelerate

Detection of malicious documents by extracting and interpreting macros in Microsoft Office files

Macros can greatly enhance the capabilities and convenience provided in documents. They also invite adversaries to include malicious code in lure documents, often used as initial access into a user’s environment. This project will extract and analyze macros and determine their indent and potential for malicious code execution. Reducing time to response through malicious code detection will allow analysts to spend their time on more meaningful work. Through applied machine learning and neural networks, we can detect and determine the impact to the customer’s bottom line.

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Faculty Supervisor:

Ali Dehghantanha

Student:

Partner:

eSentire

Discipline:

Computer science

Sector:

Technology; Information and Communications Technology; Other

University:

University of Guelph

Program:

Accelerate

Le cirque social québécois : son rôle, ses pratiques, ses pédagogies

Ce projet Mitacs permet de compléter une plus grande recherche sur le cirque social, son développement et ses enjeux en temps de pandémie, et d’assurer la diffusion cette dernière aux publics à la fois universitaires en publiant un ouvrage de référence sur le sujet, et communautaires et institutionnels grâce à deux documents plus accessibles. Il s’agit de mener un travail essentiel d’analyses approfondies et de synthèses, de rédaction d’articles, ainsi que d’édition de l’ouvrage. Ce projet permet de documenter une pratique peu étudiée et dont une partie des forces vives et de développement se trouvent au Canada. En ce qui concerne les documents de vulgarisation, ils répondent à une demande spéciale du partenaire Cirque Hors Piste, pour pouvoir rendre compte de ses activités auprès de ses membres et partenaires. Ces documents doivent aider, in fine, à la reconnaissance du travail de l’organisme partenaire, et des pratiques de cirque social en général.

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Faculty Supervisor:

Louis Patrick Leroux

Student:

Partner:

Cirque Hors Piste

Discipline:

Sociology

Sector:

Arts, entertainment and recreation

University:

Concordia University

Program:

Accelerate

Multi agent reinforcement learning with multiple time scale on financial markets

I am working on reinforcement learning for finance based on deep mathematical knowledge and the host supervisor is working on financial engineering, reinforcement learning and Markov decision process. We will study deep reinforcement learning for stock market trading and for portfolio management. The goal is to find a practical deep reinforcement agent to manage stock market trading including prediction and portfolio management.

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Faculty Supervisor:

Chi-Guhn Lee

Student:

Partner:

Kyungpook National University

Discipline:

Computer science

Sector:

Artificial Intelligence

University:

University of Toronto

Program:

Globalink Research Award

Participatory assessment of Aklak (grizzly bear) abundance and distribution in the Kivalliq Region, Nunavut

The objective of this project is to estimate grizzly bear abundance and distribution in the Kivalliq region of Nunavut by combining Inuit traditional knowledge about grizzly bears with genetic data already collected by the Government of Nunavut. Working with the communities of Arviat and Baker Lake, we will use both pre-existing interview recordings and new interview data collected by trained local interviewers so that no researcher from down south needs to visit Nunavut during the pandemic.

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Faculty Supervisor:

Douglas Clark

Student:

Partner:

Churchill Northern Studies Centre

Discipline:

Life Sciences

Sector:

Education; Professional, scientific and technical services

University:

University of Saskatchewan

Program:

Accelerate

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