Efficient Feature Selection for Detecting Botnets based on Network Traffic and Behavior Analysis

Hossain, Md. Imtiyaz; Eshrak, Sakib; Auvik, Minhaj Jami; Nasim, Syed Faiz; Rab, Raqeebir; Rahman, Ashikur

doi:10.1145/3428363.3428378

Cited by 7 publications

(8 citation statements)

References 6 publications

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“…For evaluation of the model performance, the "ISCX-Bot-2014 dataset" has been utilized. Hossain et al 2 have a detection model using a wrapper method for FS and several ML algorithms such as Naïve Bayes, LR, AdaBoost, Random Forest, and MLP.…”

Section: Fs Methods In Botnet Detectionmentioning

confidence: 99%

“…1 Nearly 40% of computers linked to the Internet have malware on them, and botnets are used to commit most cybercrimes, according to reports. 2 One of the most advanced instruments for committing crimes online is the botnet. Mostly for financial gain, cybercriminals utilize botnets to remotely carry out a variety of illegal operations, including phishing, identity theft, extortion, Distributed Denial of Service (DDoS) attacks, and spam distribution.…”

Section: Introductionmentioning

confidence: 99%

“…A report claims that due to the increase in remote working during the COVID‐19 pandemic is partially responsible for the 600% spike in cybercrimes 1 . Nearly 40% of computers linked to the Internet have malware on them, and botnets are used to commit most cybercrimes, according to reports 2 . One of the most advanced instruments for committing crimes online is the botnet.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Fs Methods In Botnet Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Baruah,

Borah,

Deka

2024

Concurrency and Computation

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“…al. [7] use a different set of features and increase the detection rate to 91% with high accuracy. They trained a model using Multi-layer Feed Forward Network (ANN).…”

Section: Literature Reviewmentioning

confidence: 99%

“…This heuristic method performs backward feature elimination technique. This method is presented in [7]. At first, all the features are included in the available feature set.…”

Section: Feature Exclusion Algorithmmentioning

confidence: 99%

On Feature Selection Algorithms for Effective Botnet Detection

Afroz

Ibnath²,

Rahman³

et al. 2023

Ubiquitous Networking

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On Feature Selection Algorithms for Effective Botnet Detection the features based on their similarity in patterns and checking all combinations within the groups of small number of features which improves the time complexity by a large margin. Through experiments we show the efficacy of the proposed feature selection heuristics. The result shows that some heuristics outperform state-of-the-art feature selection algorithms.

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Leveraging Graph Neural Networks for Botnet Detection

Saad

2023

Communications in Computer and Information Science

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Efficient Feature Selection for Detecting Botnets based on Network Traffic and Behavior Analysis

Cited by 7 publications

References 6 publications

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

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

On Feature Selection Algorithms for Effective Botnet Detection

Leveraging Graph Neural Networks for Botnet Detection

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