A Machine Learning Framework for Prevention of Software-Defined Networking controller from DDoS Attacks and dimensionality reduction of big data

Ali, Jehad; Roh, Byeong‐hee; Lee, Byungkyu; Oh, Jimyung; Adil, Muhammad

doi:10.1109/ictc49870.2020.9289504

Cited by 31 publications

(12 citation statements)

References 13 publications

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“…Security has always been an important research area, especially in the current era of Internet of Everything and big data [8–12]. As the core technology for security in the large‐scale multi‐node, mobile, dynamic wireless network environment, authentication plays a pivotal role.…”

Section: Related Workmentioning

confidence: 99%

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A PUF‐based lightweight authentication and key agreement protocol for smart UAV networks

Zhang

Obaidat

et al. 2021

IET Communications

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With the advancement of information technology and the reduction of costs, the application of unmanned aerial vehicle (UAV) has gradually expanded from the military field to the industrial field and civilian field. It brings great convenience to people in surveillance, detection, transportation, emergency rescue etc. However, UAVs usually work in harsh natural environments, and their communication security confronts various challenges. Due to UAVs' limited resources, such as computing capability, storage space, and energy, traditional security protection schemes based on complex cryptographic algorithms are not suitable for UAV systems directly. Therefore, a two‐stage lightweight identity authentication and key agreement protocol for UAV is proposed in this paper. The entire process only uses hash and XOR operations, which significantly improves the authentication efficiency. Simultaneously, the physical unclonable function (PUF) is introduced and embedded into the UAV hardware to ensure UAV network communication security when a UAV suffers a physical capture attack. In the paper, the security of the proposed protocol is proved with Burrows–Abadi–Needham (BAN) logic, Real‐or‐Random (ROR) model, and AVISPA simulation tools. An informal security analysis is also provided to illustrate that the protocol satisfies the security requirements of UAV networks. Finally, the protocol is compared with other existing protocols regarding function properties, computation cost, and communication cost, which shows that the proposed protocol has effectiveness and practicality.

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Section: Related Workmentioning

confidence: 99%

“…Security has always been an important research area, especially in the current era of Internet of Everything and big data [8][9][10][11][12].…”

Section: Related Workmentioning

confidence: 99%

A PUF‐based lightweight authentication and key agreement protocol for smart UAV networks

Zhang

Obaidat

et al. 2021

IET Communications

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“…Several mechanisms have been proposed in the literature to address the security and safety challenges of the control plane [17]. Te frst line of development has been to enforce the controller as in [18], with the aim of proposing a robust architecture that will enable it to be more robust to the considered threat, or in [19] which proposes to set up a security module in the controller to analyze its received trafc using the Support Vector Machine (SVM) classifer to detect a DDoS attack. However, with such a proposition, the control architecture does not provide a controller redundancy and so there is still a single point of failure.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a SDN Architecture Observer: Detection of Failure, Distributed Denial-of-Service and Unauthorized Intrusion

Desgeorges

Georges

Divoux

2023

Security and Communication Networks

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Software-defined networking was recently introduced and proposed to separate the control from the data plane. This architecture introduces new challenges, particularly with regard to security and safety. To address the safety challenges, it is necessary to set up a multi controller architecture to provide redundancy. In addition, the second controller can have a security benefit because it can be used to validate the decisions taken by the first controller. However, communication between the controllers is necessary in these architectures, which may be exploited by an attacker to spread across the controllers, resulting in a security issue. This study aims to develop a multi controller architecture without communication between controllers. The control is executed by the nominal controller, which performs the data plane computation, whereas the second controller is in charge of verifying the consistency of the controller’s decisions, i.e., the management traffic. We first formulated the activity of the command and then provided conditions to determine a consistent control. These conditions include a time boundary, which corresponds to the tolerance for a delay in the response time of the controller, and structural properties to verify the consistency of the path setup. Moreover, we proposed a detection algorithm that is divided into two parts: first, a learning phase that aims to learn the consistent path set up by the controller, and second, a running phase which aims to verify that the controller sets up paths that are similar to the learned path. This algorithm was evaluated in terms of its reactivity, precision, and recall. To evaluate this, we considered three use cases: a distributed denial of service (DDOS) attack, an attack to send malicious packets on the network, and a failure of the controller.

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“…In addition, there is a need for self-organization and demand-based networks to deal with huge amounts of data. SDN was therefore at the heart of the growing needs of such applications due to their programming, orchestration, and automation characteristics [11].…”

Section: Introductionmentioning

confidence: 99%

Management of Software-Defined Networking Powered by Artificial Intelligence

Ali¹,

Roh²

2022

Computer-Mediated Communication

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Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

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A Machine Learning Framework for Prevention of Software-Defined Networking controller from DDoS Attacks and dimensionality reduction of big data

Cited by 31 publications

References 13 publications

A PUF‐based lightweight authentication and key agreement protocol for smart UAV networks

A PUF‐based lightweight authentication and key agreement protocol for smart UAV networks

Implementation of a SDN Architecture Observer: Detection of Failure, Distributed Denial-of-Service and Unauthorized Intrusion

Management of Software-Defined Networking Powered by Artificial Intelligence

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