DI-ADS: A Deep Intelligent Distributed Denial of Service Attack Detection Scheme for Fog-Based IoT Applications

Gudla, Surya Pavan Kumar; Bhoi, Sourav Kumar; Nayak, Soumya Ranjan; Verma, Amit

doi:10.1155/2022/3747302

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…In another study, [29] presented a two-stage approach. Traffic is analyzed with an additional binary tree classifier in order to detect and identify the intrusion during the first stage.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-layer Intrusion Detection System based on Fog and Cloud using Improved KNN and MPNN

Kaffash,

Kamel,

Kheirabadi

2023

Preprint

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The concept of Internet of Things (IoT) and its countless applications are considered as an inseparable part of modern technology era. The placement of IoT- based devices and their limitations make the environment more vulnerable due to its openness. Security plays a critical role in IoT applications due to pervasiveness of the IoT in all of the aspects in daily life. from the other hand final devices such as their limited computing power, large number of devices connected to each other, and communication between devices and users do not allow for using traditional methods to solve security issues. Intrusion detection systems (IDSs) which can separate malicious traffic from normal mode are among the effective solutions in this field. the installed IDS should be highly accurate and light weight to affect accuracy. In order to bring services closer to electronic devices, a concept called "fog" has emerged. A large number of studies have been conducted to make the light IDS for IoT network utilizing various methods. The present study aims to propose two-layer hierarchical IDS based on machine learning, which detects attacks by considering the limitations of IoT resources. In order to create an efficient and accurate IDS, the combination of two improved K-nearest neighbor (KNN) algorithms and multi-layer perceptron (MLP) neural network applied in the fog and cloud to separate the attacks from normal traffic, respectively. we evaluated our proposed method using IOT23 dataset. The results prove the improvement in accuracy, compared to the previous methods.

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“…In another study, [29] presented a two-stage approach. Traffic is analyzed with an additional binary tree classifier in order to detect and identify the intrusion during the first stage.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-layer Intrusion Detection System based on Fog and Cloud using Improved KNN and MPNN

Kaffash,

Kamel,

Kheirabadi

2023

Preprint

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“…Physical devices like sensors and actuators give on-demand administration over the cloud, but its centralization is hazardous. All with this, on providing services by cloud to IoT faces high challenges in data abeyance, data security, data obtrusion, and data shielding [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Deep Intelligent Attack Detection Framework for Fog-Based IoT Systems

Gudla

Bhoi

Nayak

et al. 2022

Computational Intelligence and Neuroscience

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Fog computing provides a multitude of end-based IoT system services. End IoT devices exchange information with fog nodes and the cloud to handle client undertakings. During the process of data collection between the layer of fog and the cloud, there are more chances of crucial attacks or assaults like DDoS and many more security attacks being compromised by IoT end devices. These network (NW) threats must be spotted early. Deep learning (DL) assumes an unmistakable part in foreseeing the end client behavior by extricating highlights and grouping the foe in the network. Yet, because of IoT devices’ compelled nature in calculation and storage spaces, DL cannot be managed on those. Here, a framework for fog-based attack detection is proffered, and different attacks are prognosticated utilizing long short-term memory (LSTM). The end IoT gadget behaviour can be prognosticated by installing a trained LSTMDL model at the fog node computation module. The simulations are performed using Python by comparing LSTMDL model with deep neural multilayer perceptron (DNMLP), bidirectional LSTM (Bi-LSTM), gated recurrent units (GRU), hybrid ensemble model (HEM), and hybrid deep learning model (CNN + LSTM) comprising convolutional neural network (CNN) and LSTM on DDoS-SDN (Mendeley Dataset), NSLKDD, UNSW-NB15, and IoTID20 datasets. To evaluate the performance of the binary classifier, metrics like accuracy, precision, recall, f1-score, and ROC-AUC curves are considered on these datasets. The LSTMDL model shows outperforming nature in binary classification with 99.70%, 99.12%, 94.11%, and 99.88% performance accuracies on experimentation with respective datasets. The network simulation further shows how different DL models present fog layer communication behaviour detection time (CBDT). DNMLP detects communication behaviour (CB) faster than other models, but LSTMDL predicts assaults better.

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A Study on Effect of Learning Rates Using Adam Optimizer in LSTM Deep Intelligent Model for Detection of DDoS Attack to Support Fog Based IoT Systems

Gudla

Bhoi

2022

Communications in Computer and Information Science

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DI-ADS: A Deep Intelligent Distributed Denial of Service Attack Detection Scheme for Fog-Based IoT Applications

Cited by 9 publications

References 28 publications

A Two-layer Intrusion Detection System based on Fog and Cloud using Improved KNN and MPNN

A Two-layer Intrusion Detection System based on Fog and Cloud using Improved KNN and MPNN

A Deep Intelligent Attack Detection Framework for Fog-Based IoT Systems

A Study on Effect of Learning Rates Using Adam Optimizer in LSTM Deep Intelligent Model for Detection of DDoS Attack to Support Fog Based IoT Systems

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