Edge Computing and DDoS: Can Decentralized Infrastructure Resist Attacks?

By: Gonipalli Bharath Vel Tech University, Chennai, India International Center for AI and Cyber Security Research and Innovations, Asia University, Taiwan, Gmail: gonipallibharath@gmail.com

Abstract:

Edge computing technology revolutionized data processing by establishing decentralized facilities which lower transmission delays. This transition creates new problems in cybersecurity especially because Distributed Denial-of-Service (DDoS) attacks become more likely to happen. The load distribution feature of edge computing through multiple nodes functions as a mitigation strategy when attacks happen. This piece examines how distributed network systems fight back against Distributed Denial-of-Service attacks through analysis of benefits and implementation obstacles. The paper examines protection measures for defeating cyber-attacks through anomaly detection as well as AI threat intelligence and blockchain authentication protocols. The article presents a security comparison between edge and cloud systems through an accompanying table before displaying a diagram showing standard DDoS mitigation practices in edge environments.

Introduction:

Edge computing emerged as an effective solution because of the fast-growing connected device numbers and expanding need for real-time data processing. Edge processing differs from cloud infrastructure through its distribution of servers because it handles data close to its original location which lowers processing delays and minimizes bandwidth requirements[1]. The transition to edge computing introduces security liabilities for networks because Distributed Denial-of-Service (DDoS) attacks have become one of its major threats[2]. A DDoS attack attacks networks to make all services unavailable for legitimate users. Decentralized edge computing setups depend on individual node vulnerabilities because the network needs strong protection measures[3]. The effectiveness of edge computing against Distributed Denial-of-Service attacks makes up the focus of this analysis. This short article analyzes security problems at decentralized systems while presenting protection solutions and exploring how AI and blockchain strengthening create safer decentralized networks.

Security Challenges in Edge Computing:

• Expanded Attack Surface: Distributed data hosting in edge computing produces greater exposure because hackers can strike several nodes that make up the system[4].
• Resource Constraints: The small processing capabilities of edge devices create challenges for implementing standard security protection systems[5].
• Lack of Centralized Control: The administration of safety across dispersed architectures becomes harder than traditional cloud infrastructure because it operates without centralized control functions[6].
• Network Reliability: Stable connectivity remains essential for edge nodes since network disruptions will affect their ability to maintain security measure operations[7].

DDoS Mitigation in Edge Computing:

DDoS Mitigation Strategies in Edge Computing:

• AI-Powered Anomaly Detection: Machines use anomaly detection algorithms to study traffic patterns for detecting the abnormal activity spikes which indicate DDoS attacks.
• Traffic Filtering at Edge Nodes: The examination of incoming traffic at edge devices leads to suspicious request blocking before core network access happens.
• Blockchain- Based Authentication: The authentication process based on Blockchain technology uses decentralized capability that grants access only to valid users.
• Load Balancing and Redundancy: When traffic is distributed across multiple edge nodes it stops a single node from reaching its operational limits.
• Honeypots of Attack Diversion: Attack diversion through simulation nodes draws hackers into false systems where cyber analysis can study their tactics.

Aspect	Edge Computing	Cloud Computing
Attack surface	Larger, due to distributed nodes.	Centralized, single- entry points.
Latency in Mitigation	Faster response due to local processing.	Slower due to centralized control.
Scalability	Limited to edge nodes.	High scalability through cloud infrastructure.
Anomaly Detection	Distributed, node-specific detection.	Centralized, more data for analysis.
Data Privacy	Enhanced, data processed locally.	Dependent on cloud provider security policies.
Dependency on Network	Can function offline for some applications.	Requires constant internet connectivity.

Conclusion:

The edge computing approach becomes an effective solution for stopping DDoS attacks because it reduces database processing and security tasks to dispersed locations. The major hurdles related to scalability and expanded attack surfaces can be effectively countered by recent AI innovations and blockchain implementations together with real-time attack detection capabilities. Organizations that adopt edge infrastructure systems require security frameworks to develop so they can create an attack-proof digital environment.

References:

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2. M. Ouhssini, K. Afdel, M. Akouhar, E. Agherrabi, and A. Abarda, “Advancements in detecting, preventing, and mitigating DDoS attacks in cloud environments: A comprehensive systematic review of state-of-the-art approaches,” Egypt. Inform. J., vol. 27, p. 100517, Sep. 2024, doi: 10.1016/j.eij.2024.100517.
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6. N. A. Angel, D. Ravindran, P. M. D. R. Vincent, K. Srinivasan, and Y.-C. Hu, “Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies,” Sensors, vol. 22, no. 1, Art. no. 1, Jan. 2022, doi: 10.3390/s22010196.
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Cite As

Bharath G. (2025) Edge Computing and DDoS: Can Decentralized Infrastructure Resist Attacks?, Insights2Techinfo, pp.1

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