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A Framework for Agent-Based Detection and Prevention of DDoS Attacks in Distributed P2P Networks

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Abstract

Peer-to-peer networks have become popular as they are able to deliver useful services. They are distributed in nature and utilize distributed resources to perform intended activities. Especially they are widely used for file sharing. Distributed peer-to-peer networks are vulnerable to attacks. An attack that can disrupt services to all users across the globe is Distributed Denial-of-Service (DDoS) attack. DDoS attacks are considered major threat to distributed P2P networks as they are hard to detect. Many techniques came into existence to defeat DDoS attacks in such networks. However, it is still hard to respond quickly to flooding-based DDoS attacks. The rationale behind this is that DDoS attacks are made by adversaries who make use of large number of attacking machines by means of source address spoofing. In this paper, we proposed an architecture that can detect and defend DDoS attacks. The solution is based on two important parameters. They include Time-To-Live (TTL) value which is in IP header and the distance between source and destination (victim). The proposed methodology takes care of traffic control, agent-based trace back and detection of DDoS attack. The agent-based approach can keep track of details of all nodes. We have made extensive simulations in NS2 to demonstrate the proof of concept. The results revealed that the proposed methodology is capable of detecting and preventing DDoS attacks and thus ensures Quality of Service (QoS) for genuine traffic.

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Correspondence to Gera Jaideep .

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Jaideep, G., Battula, B.P. (2019). A Framework for Agent-Based Detection and Prevention of DDoS Attacks in Distributed P2P Networks. In: Bhargava, D., Vyas, S. (eds) Pervasive Computing: A Networking Perspective and Future Directions. Springer, Singapore. https://doi.org/10.1007/978-981-13-3462-7_2

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  • DOI: https://doi.org/10.1007/978-981-13-3462-7_2

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  • Publisher Name: Springer, Singapore

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  • Online ISBN: 978-981-13-3462-7

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