Botnet Detection with Deep Neural Networks Using Feature Fusion

Li, Chengjie; Zhang, Yunchun; Wang, Wangwang; Liao, Zikun; Feng, Fan

doi:10.1109/scset55041.2022.00066

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…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]. Because of the rapid expansion of threats as well as miscellany in attack strategies, IoT systems have considerable challenging issues in providing methods to detect threats and security attacks.…”

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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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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“…In the study conducted earlier [17], a feature extraction technique was first devised with the help of effective payload from every network packet. Then, a feature selection (FS) technique was projected on the basis of trade-off and by comparing the length of the packets extracted and the trained performance of models.…”

Section: Introductionmentioning

confidence: 99%

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with燚eep燣earning

Alshahrani¹,

Alrayes²,

Alqahtani³

et al. 2023

Computers, Materials &Amp; Continua

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Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning (ML) and Deep Learning (DL) techniques are developed based on heavy influence from Botnet detection methodology. In spite of this, it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset. The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizing Rat Swarm Optimizer with Deep Learning (BDC-RSODL) model. The presented BDC-RSODL model includes a series of processes like pre-processing, feature subset selection, classification, and parameter tuning. Initially, the network data is pre-processed to make it compatible for further processing. Besides, RSO algorithm is exploited for effective selection of subset of features. Additionally, Long Short Term Memory (LSTM) algorithm is utilized for both identification and classification of botnets. Finally, Sine Cosine Algorithm (SCA) is executed for fine-tuning the hyperparameters related to LSTM model. In order to validate the promising 3086 CMC, 2023, vol.74, no.2 performance of BDC-RSODL system, a comprehensive comparison analysis was conducted. The obtained results confirmed the supremacy of BDC-RSODL model over recent approaches.

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