ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

Al‐Haija, Qasem Abu; Al-Dala’ien, Mu’awya

doi:10.3390/jsan11010018

Cited by 62 publications

(7 citation statements)

References 42 publications

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“…The outcome of the entire process is the average of two categorization systems' results. For multiclass recognition and classification, the J48 method and random forest are utilized [ 34 ]. The mean of the anticipated values is used to determine the classiﬁcation accuracy and the idea of bagging as depicted in Figure 6 .…”

Section: System Designmentioning

confidence: 99%

Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique

Rayan

2022

Computational Intelligence and Neuroscience

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e-mail service providers and consumers find it challenging to distinguish between spam and nonspam e-mails. The purpose of spammers is to spread false information by sending annoying messages that catch the attention of the public. Various spam identification techniques have been suggested and evaluated in the past, but the results show that the more research in this regard is required to enhance accuracy and to reduce training time and error rate. Thus, this research proposes a novel machine learning-based hybrid bagging method for e-mail spam identification by combining two machine learning methods: random forest and J48 (decision tree). The proposed framework categorizes the e-mail into ham and spam. The database is split into multiple sets and provided as input to each method in this procedure. Moreover, tokenization, stemming, and stop word removal are performed in the preprocessing stage. Further, correlation feature selection (CFS) is employed in this research to select the required features from the preprocessed data. The effectiveness of the presented method is evaluated in terms of true-negative rates, accuracy, recall, precision, false-positive rate, f-measure, and false-negative rate; the outcomes of three studies are compared. According to the results, the presented hybrid bagged model-based SMD technology achieved 98 percent accuracy.

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Section: System Designmentioning

confidence: 99%

Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique

Rayan

2022

Computational Intelligence and Neuroscience

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“…The study also examines the NIDS design's publicly available statistics, assessment criteria, and implementation procedures. Jeans et al [42] proposed an IoT-based botnet detection technique that details the evaluation of three distinct machine learning approaches from the decision tree family the same year (AdaBoosted, RUSBoosted, and bagged). They used the N-BaIoT-2021 dataset, which comprises 99.6% accurate records of both routine IoT network tra c and botnet assault tra c from infected IoT devices.…”

Section: Related Workmentioning

confidence: 99%

DEIGASe: Deep Extraction and Information Gain for an Optimal Anomaly Detection in IoT-based Smart Cities

Hazman

Guezzaz

Benkirane

et al. 2022

Preprint

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A smart city architecture involves the integration of information and communication technology with gadgets across a system in order to boost connectivity for residents. As a result of ongoing data collection to improve service to customers. With the availability of multiple devices and remote flow through channels, the probability of cyber-attacks and intrusion detection has increased. As a consequence, numerous solutions for securing IoT have been implemented, including authentication, availability, encryption, and data integrity. Intrusion detection systems (IDSs) are an effective cyber solution that could be expanded by utilizing machine learning (ML) and deep learning (DP) techniques. This study presents an enhanced IDS that makes use of This study provides an optimal anomaly detection model, called DEIGASe which combines deep extraction based on the stacked autoencoder and feature selection utilizing Information gain (IG) and Genetic algorithms (GA) for select best features. The proposed model was evaluated on the upgraded IoT-23, BoT-IoT, and Edge-IIoT datasets using the GPU. When compared to existing IDS, our approach provides good ACC, recall, and precision rating performance features, with over 99.9% on record detection and calculation times around 17s for learning and 0.613s for detection.

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“…Abundant PDF readers and software are affected incessantly, for example, CVE-2018-14442, CVE-2017-10994 in Foxit Reader, and CVE-2018-8350 in Microsoft Windows PDF Library [46]. Recent intelligent detection systems are developed via machine/deep learning techniques [47][48] and blockchain/cryptocurrency techniques [49]. In this section, we present the proposed detection system used to analyze the PDF files to provide insights into the detection model, which classifies the PFD files into either benign or malware.…”

Section: Proposed Classification Systemmentioning

confidence: 99%

PDF Malware Detection Based on Optimizable Decision Trees

Al‐Haija¹,

Odeh²,

Qattous³

2022

Preprint

Self Cite

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Portable Document Format (PDF) files are one of the most universally used file types. This has fascinated hackers to develop methods to use these normally innocent PDF files to create security threats via infection vectors PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF Malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine-learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PFD files from malware PFD files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern-inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight-accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at high detection performance and low detection overhead.

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ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks

Cited by 62 publications

References 42 publications

Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique

Analysis of e-Mail Spam Detection Using a Novel Machine Learning-Based Hybrid Bagging Technique

DEIGASe: Deep Extraction and Information Gain for an Optimal Anomaly Detection in IoT-based Smart Cities

PDF Malware Detection Based on Optimizable Decision Trees

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