Mitigating attacks in software defined networks

Karmakar, Kallol Krishna; Varadharajan, Vijay; Tupakula, Udaya

doi:10.1007/s10586-018-02900-2

Cited by 22 publications

(14 citation statements)

References 33 publications

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“…The centralized view of the SDN network and separation of the data plane from the control plane creates a new opportunity for the attacker to carry out various types of attacks compared to the conventional network. The nature of these attacks in SDN is different from those commonly affecting the conventional network [52]. For example, the attacker can generate new malicious traffic to attack the SDN controller or even the communication links between the SDN controller and OpenFlow switches.…”

Section: Methodology For Data Generation a Dataset Attack Scenariosmentioning

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 The majority of the published studies use non-compatible and outdated datasets, such as the KDD'99 dataset.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: Methodology For Data Generation a Dataset Attack Scenariosmentioning

confidence: 99%

“…This manuscript does not emphasize all attack types in the SDN context; instead, some attacks, which are relevant to the InSDN dataset are reported in the following paragraphs. Rather, interested readers may refer to previous studies [52], [57]- [60] for more detailed information.…”

Section: B Sdn Specific Attacksmentioning

confidence: 99%

InSDN: A Novel SDN Intrusion Dataset

2020

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“…It is definitely not a top secret for specialists who organize the framework system in dissimilar associations and affiliations. Once created, it is important to keep up perfect working and check for issues such as the development of contraptions and interoperability, among others [3]. Instead, there has been a necessity to ensure the system wherever key and crucial material is taken care of for associations and affiliations, principally in game plans with sophisticated datasets of clients and suppliers.…”

Section: Introductionmentioning

confidence: 99%

“…The idea of an software defined network (SDN) depends on the model made out of the three layers-application, control, and information plane-that ideally repeat and robotize the system framework [3]. Engineering expects to integrate the control and information planes in organized gadgets, for example, in routers and switches [1].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the application program interface (API) that conveys these three planes is the northbound API and the southbound API, which are contingent upon the correspondence discourse regarding the SDN-based controller. Inside the SDN condition, the advancement of the open source APIs is advanced, which provides greater adaptability and dynamism [3].…”

Section: Introductionmentioning

confidence: 99%

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SDN-based VANETs, Security Attacks, Applications, and Challenges

et al. 2020

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Vehicular ad-hoc networks (VANETs) are the specific sort of ad-hoc networks that are utilized in intelligent transportation systems (ITS). VANETs have become one of the most reassuring, promising, and quickest developing subsets of the mobile ad-hoc networks (MANETs). They include smart vehicles, roadside units (RSUs), and on-board units (OBUs) which correspond through inconsistent wireless network. The current research in the vehicles industry and media transmission innovations alongside the remarkable multimodal portability administrations expedited center-wise ITS, of which VANETs increase considerably more attention. The particular characteristics of the software defined networks (SDNs) use the vehicular systems by its condition of the centralized art having a complete understanding of the network. Security is an important issue in the SDN-based VANETs, as a result of the effect the threats and vulnerabilities can have on driver’s conduct and personal satisfaction. This paper opens a discourse on the security attacks that future SDN-based VANETs should confront and examines how SDNs could be advantageous in building new countermeasures. SDN-based VANETs encourage us to dispose of the confinement and difficulties that are available in the traditional VANETs. It helps us to diminish the general burden on the system by dealing with the general system through a single wireless controller. While SDN-based VANETs provide us some benefits in terms of applications and services, they also have some important challenges which need to be solved. In this study we discuss and elaborate the challenges, along with the applications, and the future directions of SDN-based VANETs. At the end we provide the conclusion of the whole study.

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