IoT-based botnet attacks systematic mapping study of literature

Hamid, Habiba; Noor, Rafidah Md; Omar, Syaril Nizam; Ahmedy, Ismail; Anjum, Shaik Shabana; Shah, Syed Adeel Ali; Kaur, Sheena; Othman, Fazidah; Tamil, Emran Mohd

doi:10.1007/s11192-020-03819-5

Cited by 9 publications

(5 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These models learn the discriminating features of benign traffic and malicious traffic using different architectures such as Random Forest (RF) [ 18 ], Support Vector Machine (SVM) [ 19 ], Deep Neural Network (DNN) [ 20 ], Recurrent Neural Network (RNN) [ 21 ], Long Short-Term Memory (LSTM) [ 22 ] and Gated Recurrent Unit (GRU) [ 23 ]. For an in-depth understanding, comprehensive reviews and surveys on the application of ML and DL in intrusion detection are presented in [ 24 , 25 , 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

View full text Add to dashboard Cite

Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

show abstract

Section: Introductionmentioning

confidence: 99%

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Popoola

Adebisi

Ande

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…On top of that, some literature review papers [ 13 , 14 , 16 , 17 ] provide a well-defined search strategy, which contributes to a more systematic way of analyzing literature. These sections give a road map for other researchers to analyze literature effectively.…”

Section: Related Workmentioning

confidence: 99%

Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques

Gelgi,

Guan,

Arunachala

et al. 2024

Sensors

View full text Add to dashboard Cite

Internet of Things (IoT) technology has become an inevitable part of our daily lives. With the increase in usage of IoT Devices, manufacturers continuously develop IoT technology. However, the security of IoT devices is left behind in those developments due to cost, size, and computational power limitations. Since these IoT devices are connected to the Internet and have low security levels, one of the main risks of these devices is being compromised by malicious malware and becoming part of IoT botnets. IoT botnets are used for launching different types of large-scale attacks including Distributed Denial-of-Service (DDoS) attacks. These attacks are continuously evolving, and researchers have conducted numerous analyses and studies in this area to narrow security vulnerabilities. This paper systematically reviews the prominent literature on IoT botnet DDoS attacks and detection techniques. Architecture IoT botnet DDoS attacks, evaluations of those attacks, and systematically categorized detection techniques are discussed in detail. The paper presents current threats and detection techniques, and some open research questions are recommended for future studies in this field.

show abstract

“…However, the inclusion of all publications of this journal, regardless of whether the keywords occur in the search fields, is a bias in favour of JoPP . As a result, we believe that starting with a collection of a priori and appropriate keywords is the foundation of every systematic search strategy (Agrawal et al , 2022; Hamid et al , 2021). Similarly, we think that we were able to reliably infer trends in PP research because our sample size was large enough to be broadly representative.…”

Section: Conclusion Implications and Limitationsmentioning

confidence: 99%

The landscape of public procurement research: a bibliometric analysis and topic modelling based on Scopus

Rejeb

Appolloni

et al. 2023

JOPP

View full text Add to dashboard Cite

Purpose The purpose of this study is to investigate the structure and dynamics of academic articles relating to public procurement (PP) in the period 1984–2022 (up to May). The researchers also intend to analyse how this knowledge domain has grown since 1984. Design/methodology/approach A bibliometric analysis was carried out to examine the existing state of PP research. Based on 640 journal articles indexed in the Scopus database and written by 1,247 authors over nearly four decades, a bibliometric analysis was conducted to reveal the intellectual structure of academic works pertaining to PP. Findings Findings reveal that PP research from Scopus has significantly increased in the past decade. Major journals publishing PP research are International Journal of Procurement Management, Journal of Cleaner Production, Journal of Purchasing and Supply Management and Public Money and Management. Results also indicate that authors’ cooperation network is fragmented, showing limited collaboration among PP researchers. In addition, results suggest that the institutional collaboration network in PP research mirrors what is commonly referred to as the North–South divide, signifying insufficient research collaboration between developed and developing countries’ institutions. According to the co-occurrence keyword network and topic modelling, PP revolves around five main themes, including innovation, corruption, sustainable and green PP, PP contracts and small and medium enterprises. Based on these results, several directions for future research are suggested. Social implications This paper provides an increased understanding of the entire PP field and the potential research directions. Originality/value To the best of the authors’ knowledge, this study is the first-ever application of bibliometric techniques and topic modelling to examine the development of PP research since 1984 based on scholarly publications extracted from the Scopus database.

show abstract

IoT-based botnet attacks systematic mapping study of literature

Cited by 9 publications

References 66 publications

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks

Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques

The landscape of public procurement research: a bibliometric analysis and topic modelling based on Scopus

Contact Info

Product

Resources

About