Multi-Layer Perceptron Artificial Neural Network Based IoT Botnet Traffic Classification

Javed, Yousra; Rajabi, Navid

doi:10.1007/978-3-030-32520-6_69

Cited by 24 publications

(12 citation statements)

References 8 publications

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“…The weight values are updated internally by MLP as the model is being developed via the backpropagation process. Such MLP network is used to build an intrusion detection model utilizing NSL-KDD dataset [45], malware analysis [66], to generate explanation in IoT environments [47], detecting malicious botnet traffic from IoT devices [63]. To perform a security threat analysis of the IoT, MLP based network is used in [59], where the model classifies the network data as normal or as under attack.…”

Section: Deep Neural Network Learning-based Approachesmentioning

confidence: 99%

Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions

Sarker¹,

Khan²,

Abushark³

et al. 2022

Preprint

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The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and comfort for consumers across a wide range of application areas, from education to smart cities, the present and future IoT technologies hold great promise for improving the overall quality of human life. However, cyber-attacks and threats greatly affect smart applications in the environment of IoT. The traditional IoT security techniques are insufficient with the recent security challenges considering the advanced booming of different kinds of attacks and threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning solutions, is the key to delivering a dynamically enhanced and up-to-date security system for the next-generation IoT system. Throughout this article, we present a comprehensive picture on IoT security intelligence, which is built on machine and deep learning technologies that extract insights from raw data to intelligently protect IoT devices against a variety of cyber-attacks. Finally, based on our study, we highlight the associated research issues and future directions within the scope of our study. Overall, this article aspires to serve as a reference point and guide, particularly from a technical standpoint, for cybersecurity experts and researchers working in the context of IoT.

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Section: Deep Neural Network Learning-based Approachesmentioning

confidence: 99%

Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions

Sarker¹,

Khan²,

Abushark³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Such neural networks can be used to solve various issues in the domain of cybersecurity. For instance, building an intrusion detection model [36], malware analysis [57], security threat analysis [50], detecting malicious botnet traffic [54] as well as for building trustworthy IoT systems [39] MLP based network are used. MLP is sensitive to feature scaling and needs a range of hyperparameters such as the number of hidden layers, neurons and iterations to be tuned, which may lead the model computationally expensive to solve a complex security model.…”

Section: Multi-layer Perceptron (Mlp)mentioning

confidence: 99%

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

Sarker¹

2021

Preprint

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Deep learning (DL), which is originated from an artificial neural network (ANN), is one of the major technologies of today's smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN or ConvNet), Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM), Self-organizing Map (SOM), Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBN), Generative Adversarial Network (GAN), Deep Transfer Learning (DTL or Deep TL), Deep Reinforcement Learning (DRL or Deep RL), or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today's diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyber-attacks, e.g. denial of service (DoS), fraud detection or cyber-anomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

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“…Reference [99] uses supervised learning approach to obtain high TPR and can further be used in Transfer Learning projects.…”

Section: Neural Network Detection Mechanismsmentioning

confidence: 99%

“…Other detection methods, such as the one proposed by [93], also use similar neural network methods for detecting IoT-based botnets using PSI-Graph generation with potentially fewer resources. Reference [99] uses a model based neural network approach to classify IoT botnets; the paper compares the MLP-ANN mode with the N-BaIoT model. MLP-ANN requires a supervised learning approach, meaning it can become even more effective by training with more data and can run on very limited computing resources.…”

Section: Neural Network Detection Mechanismsmentioning

confidence: 99%

“…Detecting botnets using machine learning and neural networks has gained prominence amongst researchers and developers. Section 6 shows a clear majority of recent papers focusing on these techniques in order to achieve a high true positive rate of detection of botnet behaviour [24,92,99,109,124,125,127,136]. The additional advantages of potential real-time detection and mitigation and cloud-offloading for training/learning has allowed these techniques to establish themselves as the solution to botnet detection for the foreseeable future.…”

Section: Machine Learning and Neural Network For Botnet Detectionmentioning

confidence: 99%

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A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Stege

El-Habr

et al. 2021

Future Internet

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Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.

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Multi-Layer Perceptron Artificial Neural Network Based IoT Botnet Traffic Classification

Cited by 24 publications

References 8 publications

Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions

Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions

Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

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