Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis

Akash, Nazmus Sakib; Rouf, Shakir; Jahan, Sigma; Chowdhury, Amlan; Uddin, Jia

doi:10.32890/jict2022.21.2.3

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…Due to the use of GPU-based feature selection and training models, the training and estimation times were significantly reduced. Akash et al [25] implemented ML methods. They made a model that takes botnet identification into account.…”

Section: Literature Surveymentioning

confidence: 99%

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Dasari,

Kaluri

2024

IEEE Access

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Data privacy is essential in the financial sector to protect client's sensitive information, prevent financial fraud, ensure regulatory compliance, and safeguard intellectual property. It has become a challenging task due to the increase in usage of the internet and digital transactions. In this scenario, DDoS attack is one of the major attacks that makes clients' privacy questionable. It requires effective and robust attack detection and prevention techniques. Machine Learning (ML) has the most effective approaches for employing cyber attack detection systems. It paves the way for a new era where human and scientific communities will benefit. This paper presents a hierarchical ML-based hyperparameteroptimization approach for classifying intrusions in a network. CICIDS 2017 standard dataset was considered for this work, Initially, data was preprocessed with the min-max scaling and SMOTE methods. The LASSO approach was used for feature selection, given as input to the hierarchical ML algorithms: XGboost, LGBM, CatBoost, random forest, and decision tree. All these algorithms are pretrained with hyperparameters to enhance the effectiveness of algorithms. Models performance was assessed in terms of recall, precision, accuracy, and F1-score metrics. Evaluated approaches have shown that the LGBM algorithm gives a proven performance in classifying DDoS attacks with 99.77% of classification accuracy.

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Section: Literature Surveymentioning

confidence: 99%

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Dasari,

Kaluri

2024

IEEE Access

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“…Based on the progressive discrete Fourier transforms, the authors of [11]suggested a data mining-based method to identify botnet that achieved a detection performance of 99% with a rate of false positives of 14%. In their study, we record network fows and transform them into feature streams containing attributes like the fow's duration and the number of packets exchanged.…”

Section: Related Workmentioning

confidence: 99%

Smart Approach for Botnet Detection Based on Network Traffic Analysis

Obeidat

Yaqbeh

2022

Journal of Electrical and Computer Engineering

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Today, botnets are the most common threat on the Internet and are used as the main attack vector against individuals and businesses. Cybercriminals have exploited botnets for many illegal activities, including click fraud, DDOS attacks, and spam production. In this article, we suggest a method for identifying the behavior of data traffic using machine learning classifiers including genetic algorithm to detect botnet activities. By categorizing behavior based on time slots, we investigate the viability of detecting botnet behavior without seeing a whole network data flow. We also evaluate the efficacy of two well-known classification methods with reference to this data. We demonstrate experimentally, using existing datasets, that it is possible to detect botnet activities with high precision.

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“…These improvements drastically increased the potency and usability of IoT across multiple sectors and businesses (Gubbi et al, 2013;Madakam et al, 2015). Today, the revolutionary influence of these combined technological breakthroughs attests to the IoT framework's continual evolution and adaptability (Akash et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Implementation of the IoT-Based Technology on Patient Medication Adherence: A Comprehensive Bibliometric and Systematic Review

Ghozali

2023

Journal of Information and Communication Technology

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The dynamic field of the Internet of Things (IoT) is constantly increasing, providing a plethora of potential integration across various sectors, most notably healthcare. The IoT represents a significant technological leap in healthcare management systems, coinciding with the rising preference for personalized, proactive, cost-effective treatment techniques. This review aimed to thoroughly assess the existing literature through a systematic review and bibliometric analysis, identifying untapped research routes and possible domains for further exploration. The overarching goal was to provide healthcare professionals with significant insights into the impact of IoT technology on Patient Medication Adherence (PMA) and related outcomes. An extensive review of 314 scientific articles on the deployment of IoT within pharmaceutical care services revealed a rising trend in publication volume, with a significant increase in recent years. Pertinently, from the 33 publications finally selected, substantial data support the potential of the IoT to improve PMA, particularly among senior patients with chronic conditions. This paper also comments on various regularly implemented IoT-based systems, noting their unique benefits and limitations. In conclusion, the critical relevance of PMA is highlighted, arguing for its emphasis in future discussions. Furthermore, the need for additional research endeavors is proposed to face and overcome existing constraints and establish the long-term effectiveness of IoT technologies in maximizing patient outcomes.

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Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis

Cited by 7 publications

References 30 publications

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Smart Approach for Botnet Detection Based on Network Traffic Analysis

Implementation of the IoT-Based Technology on Patient Medication Adherence: A Comprehensive Bibliometric and Systematic Review

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