ACNN-BOT: An Ant Colony Inspired Feature Selection Approach for ANN Based Botnet Detection

Joshi, Chirag; Ranjan, Ranjeet K.; Bharti, Vishal

doi:10.1007/s11277-023-10695-8

Cited by 3 publications

(2 citation statements)

References 44 publications

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“…Additionally, they created IRC, which is not a brand-new variant of the botnet. Joshi et al 55 have proposed a botnet detection using the Ant Colony Optimization (ACO) algorithm and Artificial Neural network (ANN). ACO is a swarm-based metaheuristic algorithm that is used for FS, and for classification purposes, ANN is used.…”

Section: Role Of Metaheuristic Algorithms In Fs For Botnet Detectionmentioning

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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Section: Role Of Metaheuristic Algorithms In Fs For Botnet Detectionmentioning

confidence: 99%

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

Baruah,

Borah,

Deka

2024

Concurrency and Computation

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“…The increase in the Internet of Things (IoT) devices has caused a consistent rise in the quantity of IoT-based attacks. The IoT botnet attack has been considered the major IoT threat, which attempts to commit actual, useful, and gainful cyber threats [1]. IoT botnets have been groups of Internetlinked IoT devices to are corrupted with malware and directed remotely by attackers [2].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

Maghrabi,

Shabanah,

Althaqafi

et al. 2024

IEEE Access

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Nowadays, the Internet of Things (IoT) has become a rapid development; it can be employed by cyber threats in IoT devices. A correct system to recognize malicious attacks at IoT platforms became of major importance to minimize security threats in IoT devices. Botnet attacks have more severe and common attacks and it is threaten IoT devices. These threats interrupt IoT alteration by interrupting networks and services for IoT devices. Several existing methods present themselves to determine unknown patterns in IoT networks for improving security. Recent analysis presents DL and ML methods for classifying and detecting botnet attacks from the IoT environment. Consequently, this paper develops a Bald Eagle Search Optimization with a Hybrid Deep Learning based botnet detection (BESO-HDLBD) algorithm in an IoT platform. The presented BESO-HDLBD approach aims to resolve the security issue by identifying the botnets in the IoT environment. To reduce the high dimensionality problem, the BESO-HDLBD method uses the BESO system for the feature selection process. For botnet detection purposes, the BESO-HDLBD algorithm uses HDL, which is an integration of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and attention concept. The desire for the HDL technique in botnet detection utilises the intricate nature of botnet attacks that frequently contain difficult and developing patterns. Combining CNNs permits for effectual feature extraction from spatial data, BiLSTM networks capture temporal dependencies, and attention mechanisms improve the model's capability to concentrate on fundamental patterns. The selection of hyperparameters of the HDL approach takes place using the dragonfly algorithm (DFA). The experimental analysis of the BESO-HDLBD system could be examined under a benchmark botnet dataset. The obtained outcome infers a better outcome of the BESO-HDLBD technique compared to the recent detection system with respect to distinct estimation measures.

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Artificial intelligence-based approach to detect malicious users using deep learning and optimization techniques

Terumalasetti,

S R

2024

Multimed Tools Appl

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ACNN-BOT: An Ant Colony Inspired Feature Selection Approach for ANN Based Botnet Detection

Cited by 3 publications

References 44 publications

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

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

Enhancing Cybersecurity in the Internet of Things Environment Using Bald Eagle Search Optimization With Hybrid Deep Learning

Artificial intelligence-based approach to detect malicious users using deep learning and optimization techniques

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