A Review: Software Defined Networks Management

Kas, Kashif Nisar; Hijazi, Mohd Hanafi Ahmed; Chen, Gang; Sarrafzadeh, Abdolhossein

doi:10.7125/apan.39.2

Cited by 5 publications

(3 citation statements)

References 6 publications

(6 reference statements)

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“…Different security mechanisms such as firewalls, authentication schemes, different encryption methods, antiviruses, etc., are currently used to protect sensitive data from possible security attacks [12][13][14] on vulnerable devices [15][16][17] like the distributed denial of service (DDoS) attacks [18][19][20][21] as the first line of defense. IoT can implement using software-defined networking [SDN] [22][23][24], future network architecture [25][26][27][28], named data networking (NDN) [29][30][31] and cloud computing network [32] with voice over IP (VoIP) [33][34][35][36] fiber optics [37][38][39], worldwide interoperability for microwave access (WiMAX) [40][41][42], deep learning (DL) [43], artificial intelligence (AI), and machine learning (ML) [44].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Using Deep Neural Network for IoT Architecture

et al. 2021

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The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices generate and exchange sensitive data over the traditional internet, security has become a prime concern due to the generation of zero-day cyberattacks. A network-based intrusion detection system (NIDS) can provide the much-needed efficient security solution to the IoT network by protecting the network entry points through constant network traffic monitoring. Recent NIDS have a high false alarm rate (FAR) in detecting the anomalies, including the novel and zero-day anomalies. This paper proposes an efficient anomaly detection mechanism using mutual information (MI), considering a deep neural network (DNN) for an IoT network. A comparative analysis of different deep-learning models such as DNN, Convolutional Neural Network, Recurrent Neural Network, and its different variants, such as Gated Recurrent Unit and Long Short-term Memory is performed considering the IoT-Botnet 2020 dataset. Experimental results show the improvement of 0.57–2.6% in terms of the model’s accuracy, while at the same time reducing the FAR by 0.23–7.98% to show the effectiveness of the DNN-based NIDS model compared to the well-known deep learning models. It was also observed that using only the 16–35 best numerical features selected using MI instead of 80 features of the dataset result in almost negligible degradation in the model’s performance but helped in decreasing the overall model’s complexity. In addition, the overall accuracy of the DL-based models is further improved by almost 0.99–3.45% in terms of the detection accuracy considering only the top five categorical and numerical features.

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Section: Introductionmentioning

confidence: 99%

Anomaly Detection Using Deep Neural Network for IoT Architecture

et al. 2021

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“…Thus, utilizing SDN is an undeniable answer for improving the presentation of IoT networking and beating existing complexity. IoT can implement using softwaredefined networking [37][38][39][40][41][42][43][44], named data networking (NDN) [45][46][47] and cloud computing network [48] with future applications such as voice over IP (VoIP) [49][50][51][52] fiber optic [53][54][55], worldwide interoperability for microwave access (WiMAX) [56][57][58], and artificial intelligence (AI) and machine learning (ML) [59]. The embedded sensing devices are employed in IoT-based systems to efficiently and economically gauge real-time environmental parameters [60][61][62][63][64][65][66].…”

Section: Introductionmentioning

confidence: 99%

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

Sabir

Nisar²,

Raja

et al. 2021

IEEE Access

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The utmost advancements of artificial neural networks (ANNs), software-defined networks (SDNs) and internet of things (IoT) technologies find beneficial in different applications of the smart healthcare sector. Aiming at modern technology's use in the future development of healthcare, this paper presents an advanced heuristic based on Morlet wavelet neural network for solving the mosquito release ecosystem in a heterogeneous atmosphere. The mosquito release ecosystem is dependent of six classes, eggs density, larvae density, pupae density, mosquitoes searching for hosts density, resting mosquito's density and mosquitoes searching for ovipositional site density. An artificial neural network with the layer structure of Morlet wavelet (MWNN) kernel is presented using the global and local search optimization schemes of genetic algorithm (GA) and active-set algorithm (ASA), i.e., MWNN-GA-ASA. The accurateness, reliability and constancy of the proposed MWNN-GA-ASA is established through comparative examinations with Adams method based numerical results to solve the proposed nonlinear system with matching of order 10 -06 to 10 -09 . The accuracy and convergence of the proposed MWNN-GA-ASA is certified using the statistical operators based on root mean square error (RMSE), Theil's inequality coefficient (T.I.C) and mean absolute deviation (MAD) operators.INDEX TERMS Mosquito release ecosystem, IoT, SDN, artificial neural networks, heuristic algorithm, Adams's method.

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“…The abilities of SDN, including programming-based [9][10][11][12][13][14] activity, unified control, centralized view of the system, dynamic refreshing of sending rules, make it less demanding to distinguish and respond to Distributed Denial of Service (DDoS) attack. But the potential DDoS vulnerabilities exist over the SDN platform [15].…”

Section: Introductionmentioning

confidence: 99%

A Novel DDoS Attack-aware Smart Backup Controller Placement in SDN Design

Haque¹,

Tan²,

Yusoff³

et al. 2020

AETiC

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Security issues like Distributed Denial of Service (DDoS) attacks are becoming the main threat for Software-Defined Networking (SDN). Controller placement is a fundamental factor in the design and planning of SDN infrastructure. The controller could be seen as a single dot of failure for the whole SDN and it's the alluring point for DDoS attack. Single controller placement implies a single point of SDN control. So, there is a very high chance to fail the entire network topology as the controller associated with all switches. As a result, legitimate clients won't have the capacity to use SDN services. This is the reason why the controller is the suitable center dot of attack for the aggressor. To protect SDN from this type of single purpose of failure, it is essential to place multiple smart backup controllers to guarantee the SDN operation. In this paper, we propose a novel Integer Linear Programming (ILP) model to optimize the security issue by placing powerful smart backup controller. Result obtained from the simulation shows that our proposed novel ILP model can suggest single or multiple smart backup controller placement to support several ordinary victim controllers which has the capacity to save the cost of multiple ordinary controllers by sharing link, maximum new flows per second of controller and port, etc.

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A Review: Software Defined Networks Management

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Cited by 5 publications

References 6 publications

Anomaly Detection Using Deep Neural Network for IoT Architecture

Anomaly Detection Using Deep Neural Network for IoT Architecture

IoT Technology Enabled Heuristic Model With Morlet Wavelet Neural Network for Numerical Treatment of Heterogeneous Mosquito Release Ecosystem

A Novel DDoS Attack-aware Smart Backup Controller Placement in SDN Design

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