Resilient DDoS Detection in SDN-VANETs
Budget: ₹1,500 – ₹12,500 INR
The growing intricacy and ever-changing structure of Software-Defined Vehicular Networks
(SDN-VANETs) have brought about notable progress in intelligent transportation systems.
Nonetheless, this development has also widened the attack surface, rendering these networks
particularly vulnerable to advanced Distributed Denial-of-Service (DDoS) attacks. Within
SDN-enabled VANETs, the centralized SDN controller serves as the main decision-maker, making
it a key target for attackers. A successful DDoS assault on the controller can severely impair
network performance, disrupt real-time vehicular communication, and potentially jeopardize road
safety. Traditional DDoS detection methods in VANETs often depend on static rules, known
attack signatures, or threshold-based techniques, which fall short in addressing zero-day attacks
and dynamic attack patterns. Additionally, centralized detection systems face challenges with
latency, scalability, and robustness in non-IID and mobile vehicular settings. To address these
challenges, there is an urgent need for a scalable, intelligent, and adaptive security framework
that can ensure resilient DDoS detection and mitigation while meeting the low-latency demands of
vehicular communication. In this study, we introduce an innovative, intelligent multi-agent deep
learning framework for coordinated DDoS detection and mitigation in SDN-VANET environments.
This framework incorporates a multi-layered security architecture, utilizing the centralized control
of SDN and the distributed sensing capabilities of edge nodes and roadside units (RSUs). At
its core, the system features an intelligent Intrusion Detection and Mitigation System (IDMS)
that employs a hybrid approach, combining machine learning and deep learning models. These
models are trained on a diverse array of statistical and temporal features, such as packet rate, flow
entropy, inter-arrival times, and source diversity, enabling precise differentiation between normal
and malicious traffic behaviors. The deep learning models, particularly Long Short-Term Memory
(LSTM) networks, are designed to capture the sequential nature of traffic patterns, making them
highly effective in identifying low-rate and stealthy DDoS attacks. The system demonstrates
self-adaptive behavior, dynamically adjusting detection thresholds and mitigation strategies in
response to network context and mobility patterns. This ensures high detection accuracy while
maintaining false positive rates below 1%, which is crucial for uninterrupted vehicular services.
To mitigate detected attacks in real-time, the system leverages the programmability of SDN to
enforce dynamic traffic control rules, including rate limiting, traffic rerouting, flow isolation, and
node quarantine. These mitigation actions are coordinated at both the controller and edge levels
to ensure distributed, low-latency responses that do not overwhelm the controller. Comprehensive
experimental evaluation will be conducted using benchmark datasets such as CIC-DDoS2019 and
realistic VANET simulations with tools like Mininet-WiFi, SUMO, and the Ryu SDN controller.
Performance will be evaluated based on detection accuracy, latency, mitigation effectiveness,
computational overhead, and scalability under various traffic loads and mobility scenarios.
(SDN-VANETs) have brought about notable progress in intelligent transportation systems.
Nonetheless, this development has also widened the attack surface, rendering these networks
particularly vulnerable to advanced Distributed Denial-of-Service (DDoS) attacks. Within
SDN-enabled VANETs, the centralized SDN controller serves as the main decision-maker, making
it a key target for attackers. A successful DDoS assault on the controller can severely impair
network performance, disrupt real-time vehicular communication, and potentially jeopardize road
safety. Traditional DDoS detection methods in VANETs often depend on static rules, known
attack signatures, or threshold-based techniques, which fall short in addressing zero-day attacks
and dynamic attack patterns. Additionally, centralized detection systems face challenges with
latency, scalability, and robustness in non-IID and mobile vehicular settings. To address these
challenges, there is an urgent need for a scalable, intelligent, and adaptive security framework
that can ensure resilient DDoS detection and mitigation while meeting the low-latency demands of
vehicular communication. In this study, we introduce an innovative, intelligent multi-agent deep
learning framework for coordinated DDoS detection and mitigation in SDN-VANET environments.
This framework incorporates a multi-layered security architecture, utilizing the centralized control
of SDN and the distributed sensing capabilities of edge nodes and roadside units (RSUs). At
its core, the system features an intelligent Intrusion Detection and Mitigation System (IDMS)
that employs a hybrid approach, combining machine learning and deep learning models. These
models are trained on a diverse array of statistical and temporal features, such as packet rate, flow
entropy, inter-arrival times, and source diversity, enabling precise differentiation between normal
and malicious traffic behaviors. The deep learning models, particularly Long Short-Term Memory
(LSTM) networks, are designed to capture the sequential nature of traffic patterns, making them
highly effective in identifying low-rate and stealthy DDoS attacks. The system demonstrates
self-adaptive behavior, dynamically adjusting detection thresholds and mitigation strategies in
response to network context and mobility patterns. This ensures high detection accuracy while
maintaining false positive rates below 1%, which is crucial for uninterrupted vehicular services.
To mitigate detected attacks in real-time, the system leverages the programmability of SDN to
enforce dynamic traffic control rules, including rate limiting, traffic rerouting, flow isolation, and
node quarantine. These mitigation actions are coordinated at both the controller and edge levels
to ensure distributed, low-latency responses that do not overwhelm the controller. Comprehensive
experimental evaluation will be conducted using benchmark datasets such as CIC-DDoS2019 and
realistic VANET simulations with tools like Mininet-WiFi, SUMO, and the Ryu SDN controller.
Performance will be evaluated based on detection accuracy, latency, mitigation effectiveness,
computational overhead, and scalability under various traffic loads and mobility scenarios.