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Grant: Alliance Grants

Dalhousie University

$42,100.00

Context-aware security framework for detection and mitigation of Distributed Denial of Service (DDoS) attacks on Internet of Things (IoT)

FY 2024-25

Summary
Cybersecurity has become one of the top concerns for businesses, governments, and organizations worldwide due to the drastic increase in cyber-attacks in recent years. One emergent technology that has attracted cyber-attackers is Internet of Things (IoT). This technology refers to a collection of autonomous, smart, connected, and uniquely identifiable objects with embedded processors that have sensing, computing, and communicating capabilities. IoT promises enormous application benefits in the areas of mobile commerce, energy and environment, agriculture, military, security, surveillance, and critical infrastructure such as smart grids, water supply, nuclear plants, connected health, and telecommunications. However, resource constraints, lack of security protocols, and the heterogeneous nature of IoT devices have presented new attack surfaces. In particular, IoT systems are vulnerable to one major type of cyber-attack, namely, Distributed Denial of Service or DDoS, in which access to services are blocked, thus leading to catastrophic consequences, especially in critical infrastructure and mission critical applications. The primary objective of this proposal is to innovate, design, and implement a suite of algorithms for the detection and mitigation of DDoS attacks on IoT systems. The novelty of the proposal is the design and development of an end-to-end security architecture that uses a context-aware framework for the prediction, detection, and mitigation of DDoS attacks. This proposal is supported by industry partner Calian Ltd., one of Canada's leading solution providers and industry leaders in cybersecurity. Calian will work closely with the researchers and train highly qualified personnel (HQP) in the areas of IoT, cyber-security, data analytics, machine learning, and predictive algorithms. The outcome of this research will generate innovative solutions for DDoS attack mitigation and lead to knowledge transfer and knowledge mobilization with the supporting partner. In the long term, investing in this program will give Canada a driving role in cybersecurity research and training, ensuring an economic payoff and social benefit to citizens well into the future.
Program
Alliance Grants
Location
Nova Scotia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
576942-2022
Name nom
Sampalli, Srinivas S
Department département
Computer Science, Faculty of
Organization id
79
Institution établissement
Dalhousie University
Province en
Nova Scotia
Province fr
Nouvelle-Écosse
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2022
Award amount
42100
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2700
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information technology
Research subject fr
Technologie de l'information
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Context-aware security framework for detection and mitigation of Distributed Denial of Service (DDoS) attacks on Internet of Things (IoT)
Keywords
authentication algorithms; behavioural analytics; cyber-security; distributed denial of service (ddos); internet of things (iot); key generation and management; lightweight cryptography; machine learning approaches; mobile communication technologies; predictive algorithms
Application summary
Cybersecurity has become one of the top concerns for businesses, governments, and organizations worldwide due to the drastic increase in cyber-attacks in recent years. One emergent technology that has attracted cyber-attackers is Internet of Things (IoT). This technology refers to a collection of autonomous, smart, connected, and uniquely identifiable objects with embedded processors that have sensing, computing, and communicating capabilities. IoT promises enormous application benefits in the areas of mobile commerce, energy and environment, agriculture, military, security, surveillance, and critical infrastructure such as smart grids, water supply, nuclear plants, connected health, and telecommunications. However, resource constraints, lack of security protocols, and the heterogeneous nature of IoT devices have presented new attack surfaces. In particular, IoT systems are vulnerable to one major type of cyber-attack, namely, Distributed Denial of Service or DDoS, in which access to services are blocked, thus leading to catastrophic consequences, especially in critical infrastructure and mission critical applications. The primary objective of this proposal is to innovate, design, and implement a suite of algorithms for the detection and mitigation of DDoS attacks on IoT systems. The novelty of the proposal is the design and development of an end-to-end security architecture that uses a context-aware framework for the prediction, detection, and mitigation of DDoS attacks. This proposal is supported by industry partner Calian Ltd., one of Canada's leading solution providers and industry leaders in cybersecurity. Calian will work closely with the researchers and train highly qualified personnel (HQP) in the areas of IoT, cyber-security, data analytics, machine learning, and predictive algorithms. The outcome of this research will generate innovative solutions for DDoS attack mitigation and lead to knowledge transfer and knowledge mobilization with the supporting partner. In the long term, investing in this program will give Canada a driving role in cybersecurity research and training, ensuring an economic payoff and social benefit to citizens well into the future.