IoT-based Cyber-security of Drones using the Naïve Bayes Algorithm

Majeed, Rizwan; Abdullah, Nurul Azma; Mushtaq, Muhammad Faheem

doi:10.14569/ijacsa.2021.0120748

“…K-nearest neighbours (KNN) method is the classification method used to classify the incoming entry as normal or malicious entry [50]. Naïve Bayes is a statistical method that uses a probabilistic method based on the Bayesian theory; the probability of a field prone to attack can be calculated [51]. Support vector machine (SVM) is a classification method that separates the intrusions and normal entries from the dataset.…”

Section: Artificial Intelligence For Cyber Securitymentioning

confidence: 99%

Cyber-attacks in Cyber Physical Microgrid Systems : A comprehensive Review

Koduru¹,

Machina²,

Madichetty³

2023

Preprint

0

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Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed.

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“…The k-nearest neighbours (KNN) method is used to classify the incoming entry as normal or malicious entry [50]. Naïve Bayes is a statistical method that uses a probabilistic method based on the Bayesian theory; the probability of a field prone to attack can be calculated [51]. Support vector machine (SVM) is a classification method that separates the intrusions and normal entries from the dataset.…”

Section: Artificial Intelligence For Cyber Securitymentioning

confidence: 99%

Cyber-attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

Koduru¹,

Machina²,

Madichetty³

2023

Preprint

3

0

View full text Add to dashboard Cite

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed. A real-time case study of stealthy local covert attacks with false data injection attacks is implemented on the DC-DC converter. A deep learning model is designed to mitigate cyber attacks, and its performance is evaluated.

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“…Data mining is the act of discovering patterns that have the potential to be beneficial for managing massive databases (Syafar et al, 2015;Windarto & Herawan, 2022). Data mining classification approaches, such as the C4.5 algorithm (Aziz & Lawi, 2022;Hasanah et al, 2020), Naive Bayes (Kewsuwun & Kajornkasirat, 2022;Majeed et al, 2021), Neural Network (Hardiyanto et al, 2021;Perdana et al, 2021;Windarto et al, 2018) and K-NN (Atalaya et al, 2022;Varzaneh et al, 2022), are widely utilized by researchers in the solution of problems. Classification approach is also among the most researched algorithms.…”

Section: Introductionmentioning

confidence: 99%

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2023

JOWUA

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The goal of this research is to use the boosting technique with the C4.5 algorithm to lower the number of wrong classifications. One of the techniques for boosting is called Adaboost. It can balance the class by giving more weight to the level of classification error, which can change how the data is spread out. The Online Shoppers Purchasing Intention dataset from the UCI Machine Learning Repository is used to test this method. It has 12330 records and 18 attributes, with a label being one of those attributes. In this study, the results were looked at with the help of software called RapidMiner 9.10. Before the processing stage, the dataset is split into two parts: training and testing, with a 90:10 and 80:20 split, respectively. The results of the experiments with the C4.5 algorithm and the boosting technique at a ratio of 90:10 (accuracy of 95.07 percent and AUC of 0.966) and a ratio of 80:20 (accuracy of 95.07 percent and AUC of 0.966). While the experimental results of the C4.5 algorithm without the boosting technique at a ratio of 90:10 (accuracy of 89.02 percent and AUC of 0.845) and a ratio of 80:20 (accuracy of 88.1 percent and AUC of 0.845). Based on the results of the comparison, boosting on the C4.5 algorithm is much better than the standard C4.5 algorithm, with an average increase of 5.76 percent. We can say that using the boosting technique with the C4.5 algorithm can improve high accuracy and lower the amount of classification error.

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IoT-based Cyber-security of Drones using the Naïve Bayes Algorithm

Cited by 15 publications

References 17 publications

Cyber-attacks in Cyber Physical Microgrid Systems : A comprehensive Review

Cyber-attacks in Cyber Physical Microgrid Systems : A comprehensive Review

Cyber-attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

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