SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)

Wani, Azka; Revathi, S.; Khaliq, Rubeena

doi:10.1049/cit2.12003

Cited by 95 publications

(52 citation statements)

References 30 publications

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“…Rather than relying on hand-engineered or traditional technique filters, CNN may be trained to learn these filter characteristics over time. The arrangement of the Visual Cortex served as inspiration for the design of the CNN architecture, which is similar to the neuronal connection pattern found in the human brain [28]. A visual field area known as the Receptive Field is where neurons respond to visual input.…”

Section: ) Support Vector Machines (Svm)mentioning

confidence: 89%

SDN Architecture for Smart Homes Security with Machine Learning and Deep Learning

Alonazi¹,

HAMDI²,

Azim³

et al. 2022

IJACSA

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In recent decades, Intelligent home systems are popular because they improve comfort and quality of life. A growing number of homes are becoming "smarter" by incorporating Internet of Things (IoT) technology to improve comfort, energy efficiency, and safety. Increases in resourceconstrained IoT devices heighten security threats and vulnerabilities connected with them. Using SDN and virtualization, the IoT's size and adaptability can be managed at a lower cost than ever before. Using these intelligent security solutions, we can achieve real-time detection and automation for attack detection and prevention using artificial intelligence. Consequently, a large variety of solutions utilizing machine learning and deep learning have been developed to mitigate attacks on the IoT. Thus, the goal of this work is to use machine learning and deep learning to defend smart homes with SDNbased. We have designed smart home environments using Software-Defined Networking and Mininet that provide Instant Virtual networks for IoT in smart homes. Two datasets were used in this work: the first SDN dataset, which we acquired from smart homes by launching real attacks and creating normal traffic, and the second IoTID20 dataset, which is publicly available online. On both datasets, conducted ML and DL experiments. The best accuracy on SDN Dataset was 99.9% using Xgboost classifier, and on IoTID20 was 98.9% LSTM in binary classification, and ANN 85.7% on multiclass.

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Section: ) Support Vector Machines (Svm)mentioning

confidence: 89%

SDN Architecture for Smart Homes Security with Machine Learning and Deep Learning

Alonazi¹,

HAMDI²,

Azim³

et al. 2022

IJACSA

View full text Add to dashboard Cite

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“…The SVD algorithm is a very important model compression method. Its connotation is to represent the original matrix by extracting key features; that is, a complex matrix is approximated by multiplying several small matrices representing key features in the original matrix [ 23 ]. SVD is an algorithm widely used in the field of machine learning.…”

Section: Research Theories and Methodsmentioning

confidence: 99%

Feature Recognition and Style Transfer of Painting Image Using Lightweight Deep Learning

Tan

2022

Computational Intelligence and Neuroscience

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This work aims to improve the feature recognition efficiency of painting images, optimize the style transfer effect of painting images, and save the cost of computer work. First, the theoretical knowledge of painting image recognition and painting style transfer is discussed. Then, lightweight deep learning techniques and their application principles are introduced. Finally, faster convolutional neural network (Faster-CNN) image feature recognition and style transfer models are designed based on a lightweight deep learning model. The model performance is comprehensively evaluated. The research results show that the designed Faster-CNN model has the highest average recognition efficiency of about 28 ms and the lowest of 17.5 ms in terms of feature recognition of painting images. The accuracy of the Faster-CNN model for image feature recognition is about 97% at the highest and 95% at the lowest. Finally, the designed Faster-CNN model can perform style recognition transfer on a variety of painting images. In terms of style recognition transfer efficiency, the highest recognition transfer rate of the designed Faster-CNN model is about 79%, and the lowest is about 77%. This work not only provides an important technical reference for feature recognition and style transfer of painting images but also contributes to the development of lightweight deep learning techniques.

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“…Not defined [22] LSTM CSE-CIC-IDS2018 A security architecture is designed to locate Brute-Force, SYN flooding, and ICMP flooding attacks…”

Section: Dos Attack Detection Scheme Is Designedmentioning

confidence: 99%

“…Authors have addressed a vast range of cyber-attacks, and their designed framework has validated this claim by successfully detecting these attacks with remarkable accuracy. [22] discussed a relevantly admissible approach to an encounter Brute-Force attack, SYN flooding, and ICMP flooding attacks. The designed security framework is aggregated with CSE-CIC-IDS2018 data set with a training pattern on LSTM and is capable of detecting premonition threats in IoT communications.…”

Section: Cicids2017mentioning

confidence: 99%

Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT)

et al. 2022

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The Internet of Things (IoT) has established itself as a multibillion-dollar business in recent years. Despite its obvious advantages, the widespread nature of IoT renders it insecure and a potential target for cyber-attacks. Furthermore, these devices' broad connectivity and dynamic heterogeneous nature can open up a new surface of attack for refined malware attacks. There is a critical need of protecting the IoT environment from such attacks and malwares. Therefore this research aims to propose an intelligent, SDN-enabled hybrid framework leveraging Cuda Long Short Term Memory Gated Recurrent Unit (cuLSTMGRU) for efficient threat detection in IoT environments. To properly assess the proposed system, a state-of-the-art IoT-based dataset and standard evaluation metrics were used. The proposed model achieved 99.23 % detection accuracy with a low false-positive rate. For further verification, we compare the proposed model results with two of our constructed models (i.e., cuBLSTM and cuGRUDNN) and current benchmark algorithms. The proposed model outclassed the other models regarding speed efficiency, detection accuracy, precision, and other standard evaluation metrics. Finally, the proposed work employed 10-fold cross-validation to ensure that the results were completely unbiased.

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SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)

Cited by 95 publications

References 30 publications

SDN Architecture for Smart Homes Security with Machine Learning and Deep Learning

SDN Architecture for Smart Homes Security with Machine Learning and Deep Learning

Feature Recognition and Style Transfer of Painting Image Using Lightweight Deep Learning

Towards SDN-Enabled, Intelligent Intrusion Detection System for Internet of Things (IoT)

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