International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 08 | Aug 2025
p-ISSN: 2395-0072
www.irjet.net
A Modular Real-Time System for DDoS Detection and Mitigation in Hybrid Cloud and IoT Environments Alby Alphonsa Joseph 1, Dr. Venifa Mini G 2 1Research Scholar, Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education,
Tamil Nadu, India
2Assistant Professor, Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education,
Tamil Nadu, India ------------------------------------------------------------------------***-------------------------------------------------------------------------
Abstract - The integration of IoT and cloud systems offers
DDoS attacks are especially difficult to defend against because of their distributed and coordinated nature. The general mechanism of a DDoS attack is illustrated in Fig -1 In such attacks, the perpetrator uses a network of compromised devices—commonly referred to as "zombies"—to flood the target server with malicious traffic. The primary objective is to exhaust network resources and deny service to legitimate users, potentially causing severe long-term disruptions to online services.
advanced data services but also increases the risk of DDoS attacks. Existing methods like MLP, Random Forest, and Naive Bayes often fail to detect attacks accurately, especially in large datasets, leading to high false positives and poor service quality. This paper presents a real-time DDoS detection algorithm using a dedicated dataset. It achieves high accuracy, low false positives, and maintains strong network connectivity, even during attacks. The algorithm handles large, fast data streams efficiently, ensuring continuous service and meeting SLA requirements.
Real-world DDoS attack networks often involve a very large number of devices and are strategically designed to degrade service availability. These attacks can significantly impair network performance and accessibility. DDoS is a systematic method that uses multiple servers to bring down a target system.
MATLAB simulations show that the proposed method outperforms existing techniques in accuracy, reliability, and classification performance. Key Words: DDoS Detection, Cloud Security, Machine Learning, IoT Networks, Hybrid Cloud, Traffic Analysis, Anomaly Detection, Real-Time Mitigation.
1.INTRODUCTION Most organizations are increasingly adopting cloud computing, driven by the growth of e-commerce and internet-based trading. To optimize costs and efficiently utilize computing resources, many firms opt for a hybrid cloud model—a combination of both public and private cloud infrastructures. However, hybrid and distributed cloud environments are frequently targeted by Distributed Denial of Service (DDoS) attacks [1].
Fig -1: DDoS Attack Mechanism
DDoS attacks disrupt services by overwhelming the network with excessive traffic, rendering them inaccessible to legitimate users and often resulting in significant financial losses. Before launching an attack, cybercriminals typically gain control over a large number of compromised devices. Due to its vast internet footprint and growing digital economy, India has become an attractive target for financial espionage and cybercrime. Cloud-related threats can lead to the loss of critical assets and data.
Existing DDoS mitigation strategies generally rely on centralized detection mechanisms hosted in cloud environments or use traditional network-based techniques. However, these approaches often prove inadequate in hybrid cloud setups. Centralized systems may introduce latency and become single points of failure if targeted, while traditional network-based solutions may lack the flexibility to adapt to dynamic and distributed cloud workloads [2].
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