VARMAN: Multi-plane security framework for software defined networks

Krishnan, Prabhakar; Duttagupta, Subhasri; Achuthan, Krishnashree

doi:10.1016/j.comcom.2019.09.014

Cited by 79 publications

(47 citation statements)

References 33 publications

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“…In SDN, only statistical features can be extracted from the SDN controller through OpenFlow calls to the SDN switches, (eg., flow duration, number of packets, number of bytes). In this manuscript, the same framework method of [75] is used to obtain the SDN specific features. These features can be directly extracted from the SDN controller through API queries or by the manual computation based on flow statistics information.…”

Section: B Sdn Specific Featuresmentioning

confidence: 99%

“…The new features include the maximum, minimum, mean, and standard deviation of these values as well as the directionspecific features. These features are essential to define some particular attacks like botnet [75]. We selected a subset of 48 features from our dataset.…”

Section: B Sdn Specific Featuresmentioning

confidence: 99%

“…We selected a subset of 48 features from our dataset. While the previous method [75] used a subset of 50 features to train their learning model. However, they used the source IP, destination IP in their computation.…”

Section: B Sdn Specific Featuresmentioning

confidence: 99%

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InSDN: A Novel SDN Intrusion Dataset

2020

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Software-Defined Network (SDN) has been developed to reduce network complexity through control and manage the whole network from a centralized location. Today, SDN is widely implemented in many data center's network environments. Nevertheless, emerging technology itself can lead to many vulnerabilities and threats which are still challenging for manufacturers to address it. Therefore, deploying Intrusion Detection Systems (IDSs) to monitor malicious activities is a crucial part of the network architecture. Although the centralized view of the SDN network creates new opportunities for the implementation of IDSs, the performance of these detection techniques relies on the quality of the training datasets. Unfortunately, there are no publicly available datasets that can be used directly for anomaly detection systems applied in SDN networks. The majority of the published studies use non-compatible and outdated datasets, such as the KDD'99 dataset. This manuscript aims to generate an attack-specific SDN dataset and it is publicly available to the researchers. To the best of our knowledge, our work is one of the first solutions to produce a comprehensive SDN dataset to verify the performance of intrusion detection systems. The new dataset includes the benign and various attack categories that can occur in the different elements of the SDN platform. Further, we demonstrate the use of our proposed dataset by performing an experimental evaluation using eight popular machine-learning-based techniques for IDSs.

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Section: B Sdn Specific Featuresmentioning

confidence: 99%

Section: B Sdn Specific Featuresmentioning

confidence: 99%

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InSDN: A Novel SDN Intrusion Dataset

2020

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“…Krishnan et al [63] set out a multi-layer architecture called Advanced MultiplAne SecuRity Framework for Software Defined Networks (VARMAN), which is designed for the detection and mitigation of DDoS attacks in SDN-based IoT data centers. The detection of malicious flows is aided by several machine-learning algorithms and takes place during the selection and classification stages.…”

Section: Flow Filteringmentioning

confidence: 99%

“…Cosine similarity Yin et al [54] Flow filtering Özçelik et al [56] Krishnan et al [63] Hybrid SDN-Fog-Cloud…”

Section: Flow Filteringmentioning

confidence: 99%

A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios

Silva

Neto

et al. 2020

Sensors

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The Internet of Things (IoT) has attracted much attention from the Information and Communication Technology (ICT) community in recent years. One of the main reasons for this is the availability of techniques provided by this paradigm, such as environmental monitoring employing user data and everyday objects. The facilities provided by the IoT infrastructure allow the development of a wide range of new business models and applications (e.g., smart homes, smart cities, or e-health). However, there are still concerns over the security measures which need to be addressed to ensure a suitable deployment. Distributed Denial of Service (DDoS) attacks are among the most severe virtual threats at present and occur prominently in this scenario, which can be mainly owed to their ease of execution. In light of this, several research studies have been conducted to find new strategies as well as improve existing techniques and solutions. The use of emerging technologies such as those based on the Software-Defined Networking (SDN) paradigm has proved to be a promising alternative as a means of mitigating DDoS attacks. However, the high granularity that characterizes the IoT scenarios and the wide range of techniques explored during the DDoS attacks make the task of finding and implementing new solutions quite challenging. This problem is exacerbated by the lack of benchmarks that can assist developers when designing new solutions for mitigating DDoS attacks for increasingly complex IoT scenarios. To fill this knowledge gap, in this study we carry out an in-depth investigation of the state-of-the-art and create a taxonomy that describes and characterizes existing solutions and highlights their main limitations. Our taxonomy provides a comprehensive view of the reasons for the deployment of the solutions, and the scenario in which they operate. The results of this study demonstrate the main benefits and drawbacks of each solution set when applied to specific scenarios by examining current trends and future perspectives, for example, the adoption of emerging technologies based on Cloud and Edge (or Fog) Computing.

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A comprehensive survey on DDoS attacks on various intelligent systems and it's defense techniques

Gaurav

Gupta

Alhalabi

et al. 2022

Int J of Intelligent Sys

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The purpose of this study is to provide an overview of distributed denial of service (DDoS) attack detection in intelligent systems. In recent times, due to the endemic COVID‐19, the use of intelligent systems has increased. However, these systems are easily affected by DDoS attacks. A DDoS attack is a reliable tool for cyber‐attackers because there is no efficient method which can detect or filter it properly. In this context, we analyze different types of DDoS attacks and defense techniques for intelligent systems. For the analysis, we used Scopus databases to collect relevant papers in English between 2014 and 2022. This study makes an important contribution to the field of DDoS attack detection for intelligent systems, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of various perspectives, definitions, and trends in the field.

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VARMAN: Multi-plane security framework for software defined networks

Cited by 79 publications

References 33 publications

InSDN: A Novel SDN Intrusion Dataset

InSDN: A Novel SDN Intrusion Dataset

A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios

A comprehensive survey on DDoS attacks on various intelligent systems and it's defense techniques

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