An SDN-supported collaborative approach for DDoS flooding detection and containment

Chin, Tommy; Mountrouidou, Xenia; Li, Xiangyang; Xiong, Ke

doi:10.1109/milcom.2015.7357519

Cited by 43 publications

(35 citation statements)

References 9 publications

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“…Rodrigues et al [28] Javaid et al [27] Rodrigues et al [26] François et al [35] Chen et al [36] Chin et al [37] Patil et al [38] Sagirlar et al [39] Our approach…”

Section: Responsiveness Scalabilitymentioning

confidence: 99%

Collaborative Blockchain-Based Detection of Distributed Denial of Service Attacks Based on Internet of Things Botnets

et al. 2019

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Internet of Things is one of the most significant latest developments in computer science. It is common for modern computing infrastructures to partially consist of numerous low power devices that are characterized by high diversity in both hardware and software. Existing security models, approaches and solutions are not able to sufficiently protect such systems. In this paper we propose the use of lightweight agents installed at multiple internet of things (IoT) installations (e.g., smart-homes), in order to collaboratively detect distributed denial of service (DDoS) attacks conducted by the use of IoT devices botnets. Specifically, agents exchange outbound traffic information in order to identify possible victims of DDoS attacks. This information exchange is governed by a blockchain smart contract, that ensures the integrity of both the procedure and the information. A simulation of the operation of the proposed methodology has been conducted in order to evaluate both its detection efficiency and its resilience against malicious agents that aim to falsify results.

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“…Rodrigues et al [28] Javaid et al [27] Rodrigues et al [26] François et al [35] Chen et al [36] Chin et al [37] Patil et al [38] Sagirlar et al [39] Our approach…”

Section: Responsiveness Scalabilitymentioning

confidence: 99%

Collaborative Blockchain-Based Detection of Distributed Denial of Service Attacks Based on Internet of Things Botnets

et al. 2019

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“…23,24 Others deal more with the protection of communication end points (user terminals and application servers). 4,[25][26][27][28][29] Similarly, in this paper, we focus on the protection of the communication end points by taking advantage of SDN concepts.…”

Section: Related Workmentioning

confidence: 99%

DDoS protection with stateful software‐defined networking

et al. 2018

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Summary Distributed denial of service (DDoS) attacks represent one of the most critical security challenges facing network operators. Software‐defined networking (SDN) permits fast reactions to such threats by dynamically enforcing simple forwarding/blocking rules as countermeasures. However, the centralization of the control plane requires that the SDN controller, besides network management operations, should also collect information to identify and mitigate the security menaces. A major drawback of this approach is that it may overload the controller and the control channel. On the other hand, stateful SDN represents a new concept, developed to improve reactivity and offload the controller by delegating local treatments to the switches. In this article, we embrace this paradigm to protect end‐hosts from DDoS attacks. We propose StateSec, a novel approach based on in‐switch processing capabilities to detect and mitigate flooding threats. StateSec monitors packets matching configurable traffic features without resorting to the controller. By feeding an entropy‐based detection algorithm with such monitoring features, it detects and mitigates several threats such as (D)DoS with high accuracy. We implemented StateSec in an SDN platform comparing it with state‐of‐the‐art approaches. We show that StateSec is far more efficient: It achieves very accurate detection levels, reducing at the same time the control plane overhead. We have also evaluated the memory footprint of StateSec for a possible use in production. Finally, we deployed StateSec over a real network to tune its parameters and assess its suitability to real‐world deployments.

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“…Furthermore, the study of IDS has been an important aspect of network security. There have been numerous developments of IDS solutions to deter malicious traffic [7,8,14,15,30,33,37,42], but they heavily rely on user-space detection.…”

Section: Related Workmentioning

confidence: 99%

“…SDN can effectively help solve these real-world problems due to the two key features of SDN including its programmability and globalview of an environment. Researchers have leveraged these two features to minimize end-to-end delay [5], control network traffic [17], and security attacks [8].…”

Section: Introductionmentioning

confidence: 99%

Kernel-Space Intrusion Detection Using Software-Deﬁned Networking

Chin¹,

Xiong²,

Rahouti³

2018

ICST Transactions on Security and Safety

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Software-Defined Networking (SDN) has encountered serious Denial of Service (DoS) attacks. However, existing approaches cannot sufficiently address the serious attacks in the real world because they often present significant overhead and they require long detection and mitigation time. In this paper, we propose a lightweight kernel-level intrusion detection and prevention framework called KernelDetect, which leverages modular string searching and filtering mechanisms with SDN techniques. In KernelDetect, we sufficiently utilize the strengths of the Aho-Corasick and Bloom filter to design KernelDetect by using SDN. We further experimentally compare it with SNORT and BROS, two conventional and popular Intrusion Detection and Prevention System (IDPS) on the Global Environment for Networking Innovations (GENI), a real-world testbed. Our comprehensive studies through experimental data and analysis show that KernelDetect is more efficient and effective than SNORT and BROS.

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An SDN-supported collaborative approach for DDoS flooding detection and containment

Cited by 43 publications

References 9 publications

Collaborative Blockchain-Based Detection of Distributed Denial of Service Attacks Based on Internet of Things Botnets

Collaborative Blockchain-Based Detection of Distributed Denial of Service Attacks Based on Internet of Things Botnets

DDoS protection with stateful software‐defined networking

Kernel-Space Intrusion Detection Using Software-Deﬁned Networking

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