A real-time IoT-based botnet detection method using a novel two-step feature selection technique and the support vector machine classifier

Masoudi-Sobhanzadeh, Yosef; Emami-Moghaddam, Shabnam

doi:10.1016/j.comnet.2022.109365

Cited by 18 publications

(6 citation statements)

References 40 publications

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“…They demonstrated that leveraging historical data of source and destination hosts, along with pertinent statistical metrics, can effectively differentiate between normal and anomalous traffic, achieving an accuracy of 97%. Despite its merits, the model displayed a True Positive Rate (TPR) that was below the average seen in other models 35 .…”

Section: Literature Reviewmentioning

confidence: 78%

A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection

Hossain,

Islam

2023

Sci Rep

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In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex security challenge. Existing detection systems are continually outmaneuvered by the relentless advancement of botnet strategies, necessitating a more dynamic and proactive approach. Our research introduces a ground-breaking solution to the persistent botnet problem through a strategic amalgamation of Hybrid Feature Selection methods—Categorical Analysis, Mutual Information, and Principal Component Analysis—and a robust ensemble of machine learning techniques. We uniquely combine these feature selection tools to refine the input space, enhancing the detection capabilities of the ensemble learners. Extra Trees, as the ensemble technique of choice, exhibits exemplary performance, culminating in a near-perfect 99.99% accuracy rate in botnet classification across varied datasets. Our model not only surpasses previous benchmarks but also demonstrates exceptional adaptability to new botnet phenomena, ensuring persistent accuracy in a landscape of evolving threats. Detailed comparative analyses manifest our model's superiority, consistently achieving over 99% True Positive Rates and an unprecedented False Positive Rate close to 0.00%, thereby setting a new precedent for reliability in botnet detection. This research signifies a transformative step in cybersecurity, offering unprecedented precision and resilience against botnet infiltrations, and providing an indispensable blueprint for the development of next-generation security frameworks.

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Section: Literature Reviewmentioning

confidence: 78%

A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection

Hossain,

Islam

2023

Sci Rep

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“…Sobhanzadeh et al 56 have introduced a real time botnet detection system using a two‐step FS approach. The initial stage employs the Pearson correlation, a filter‐based strategy for FS.…”

Section: Real Time Botnet Detection Using ML Algorithmsmentioning

confidence: 99%

Reviewing various feature selection techniques in machine learning‐based botnet detection

Baruah,

Borah,

Deka

2024

Concurrency and Computation

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SummaryMachine learning approaches are widely used for the detection and classification of emerging botnet variations due to their ability to yield more precise results compared to traditional methods. The relevancy of the features plays a major role in these detection algorithms' effectiveness. As such, the most distinctive characteristics must be extracted from a high‐dimensional dataset that is used to classify botnets. Nevertheless, we discovered that the majority of earlier studies lacked proper analysis and paid little attention to the various feature selection techniques. The main goal of this work is to investigate and assess the advantages and disadvantages of the different feature selection techniques used for botnet detection. Studies show that feature selection is a very efficient way to decrease the amount of storage and processing power required while simultaneously increasing classification accuracy. As a consequence, its application in many other fields has grown. The field of feature selection is recognized for its non‐deterministic polynomial‐time hardness; to mitigate this hardness, metaheuristic techniques have been applied. Metaheuristic algorithms are exceptionally good at performing a global search. In order to choose feature subsets optimally in the field of botnet detection, we additionally prioritize the use of metaheuristic methods. This study offers a more thorough insight of the feature selection strategies that are primarily employed by machine learning‐based botnet detection models. It also offers insights into how better feature selection approaches might be applied to strengthen botnet detection mechanisms. Additionally, it will help in understanding the limitations of existing approaches and identifying areas for improvement.

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“…∑ Total number of delivered packets ∑ Total packet transmitted (6) and botnet traffic delivers the same number of packets when the number of IoT nodes is six. Meanwhile, in other cases, normal traffic shows a higher packet delivery ratio as compared to botnet traffic.…”

Section: Pdr =mentioning

confidence: 99%

“…Due to its limited processing and power, botnet detection based on a host intrusion detection system (HIDS) achieved low reliability and accuracy [5]. The IoT domain's network-based botnet detection approach was proposed using a hierarchical classification [6]. Machine learning algorithms (MLAs) are the current trend for the identification and detection of IoT botnets that are working based on the network traffic [7].…”

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confidence: 99%

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

Garg,

Kumar,

Mahanti

2024

Future Internet

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The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency.

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A real-time IoT-based botnet detection method using a novel two-step feature selection technique and the support vector machine classifier

Cited by 18 publications

References 40 publications

A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection

A novel hybrid feature selection and ensemble-based machine learning approach for botnet detection

Reviewing various feature selection techniques in machine learning‐based botnet detection

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

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