EKRV: Ensemble of kNN and Random Committee Using Voting for Efficient Classification of Phishing

Niranjan, A; Haripriya, D.; Pooja, R C; Sarah, Sfurti; Shenoy, P. Deepa; Venugopal, K R

doi:10.1007/978-981-13-1708-8_37

Cited by 11 publications

(8 citation statements)

References 6 publications

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“…It may be noticed from Fig. 4 that ERCRFS used by us perform better compared to EKRV [2] and HEFS [5]. Unfortunately, none of the Ensemble schemes behave uniformly on all the data sets.…”

Section: Investigation Methodologymentioning

confidence: 86%

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ERCRFS: Ensemble of Random Committee and Random Forest using StackingC for Phishing Classification

Niranjan¹

2020

IJETER

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The process of gathering sensitive information such as credit card/ debit card number, password, OTP etc., through a deceptive e-mail or SMS with links to Malicious site is called Phishing. An attacker during the Phishing attack masquerades as a trustworthy entity of some kind and lures the victim to provide his/her sensitive information. As per the latest data breach reports, the Phishing attacks make up almost a third of all data breaches. The attackers are using more sophisticated tools and are getting away with all the important data . Our work examines the application of Machine Learning Algorithms for the efficient classification of Phishing. The MMR, MSDMR and FSF Algorithms contribute towards the selection of significant Features from the data set such that there is no degradation in the performance of Classification. The hybrid framework ERCRFS, proposed by us ensures better Detection Accuracy values amongst all existing models.

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“…It may be noticed from Fig. 4 that ERCRFS used by us perform better compared to EKRV [2] and HEFS [5]. Unfortunately, none of the Ensemble schemes behave uniformly on all the data sets.…”

Section: Investigation Methodologymentioning

confidence: 86%

“…The top classifiers are then combined using various ensemble approaches such as StackingC, Stacking, Grading and Voting. The experimental results obtained on the data sets indicate that ERCRFS outperforms the existing Models [2] in terms of Prediction Accuracy.…”

Section: Introductionmentioning

confidence: 91%

ERCRFS: Ensemble of Random Committee and Random Forest using StackingC for Phishing Classification

Niranjan¹

2020

IJETER

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“…The Ensemble of KNN and Random committee using voting (EKRV) is proposed by Niranjan et al (2019) [41] to detect the phishing sites. The proposed work consists of two phases such as pre-processing and classification.…”

Section: A Ensemble Models To Detect Phishingmentioning

confidence: 99%

Multilayer Stacked Ensemble Learning Model to Detect Phishing Websites

Kalabarige¹,

Rao

Abraham

et al. 2022

IEEE Access

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Phishing is a cyber attack that tricks the online users into revealing sensitive information with a fake website imitating a legitimate website. The attackers with stolen credentials not only use them for the targeted website but also can be used for accessing the other popular legitimate websites. There exists many anti-phishing techniques, toolbars, extensions to counter the phishing sites but still the phishing attacks are major concern in the current digital world. In this paper, we propose a multilayered stacked ensemble learning technique which consists of estimators at different layers where the predictions of estimators from current layer are fed as input to the next layer. From the experimental results, it is observed that the proposed model achieved a significant performance when evaluated with different datasets with an accuracy of ranging from 96.79% to 98.90%. The proposed model is evaluated with datasets from UCI(D1), Mendeley 2018(D2) and Mendeley 2020(D3,D4). The proposed model achieved detection rate of 97.76% with D1 dataset, achieved an accuracy of 98.9% with D2 dataset. Finally, the technique is tested with D3 and D4 which resulted in accuracy of 96.79% and 98.43% respectively. Also, the proposed model outperformed baseline models corresponding to datasets with a significant difference in accuracy and F score metrics.

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“…The drawback of this system is detecting some minimal false-positive and false-negative results. Authors in Niranjan et al [48] used the UCI dataset on phishing containing 6157 legitimate and 4898 phishing instances out of a total of 11,055 instances. The EKRV model was used that involves a combination of KNN and random committee techniques.…”

Section: Hybrid Learning (Hl) Based Phishing Attack Detectionmentioning

confidence: 99%

“…Authors in Niranjan et al [48] proposed an ensemble technique through the voting and stacking method. They selected the UCI ML phishing dataset and take only 23 features out of 30 features for further attack detection.…”

Section: Hybrid Learning (Hl) Based Phishing Attack Detectionmentioning

confidence: 99%

A comprehensive survey of AI-enabled phishing attacks detection techniques

et al. 2020

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In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

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EKRV: Ensemble of kNN and Random Committee Using Voting for Efficient Classification of Phishing

Cited by 11 publications

References 6 publications

ERCRFS: Ensemble of Random Committee and Random Forest using StackingC for Phishing Classification

ERCRFS: Ensemble of Random Committee and Random Forest using StackingC for Phishing Classification

Multilayer Stacked Ensemble Learning Model to Detect Phishing Websites

A comprehensive survey of AI-enabled phishing attacks detection techniques

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