Phishing Websites Classification using Hybrid SVM and KNN Approach

Altaher, Altyeb

doi:10.14569/ijacsa.2017.080611

Cited by 40 publications

(23 citation statements)

References 12 publications

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“…In another study [14], Naïve Bayes and logistic regression algorithm is employed to implement a model for phishing attack detection. The proposed model detects phishing attack ased on the prior experience provided to learning algorithm during training phase.…”

Section: Tsehay Admassu Assegiementioning

confidence: 99%

K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection

Assegie

2021

IJAINN

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Phishing causes many problems in business industry. The electronic commerce and electronic banking such as mobile banking involves a number of online transaction. In such online transactions, we have to discriminate features related to legitimate and phishing websites in order to ensure security of the online transaction. In this study, we have collected data form phish tank public data repository and proposed K-Nearest Neighbors (KNN) based model for phishing attack detection. The proposed model detects phishing attack through URL classification. The performance of the proposed model is tested empirically and result is analyzed. Experimental result on test set reveals that the model is efficient on phishing attack detection. Furthermore, the K value that gives better accuracy is determined to achieve better performance on phishing attack detection. Overall, the average accuracy of the proposed model is 85.08%.

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Section: Tsehay Admassu Assegiementioning

confidence: 99%

K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection

Assegie

2021

IJAINN

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“…In their research, A. Altaher [10] proposed a combination approach where they combined two algorithms namely: K-NN and SVM respectively for detecting phishing websites. In the initial stage, a hybrid model of KNN and SVM is employed.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Feature Selection Techniques for Malicious Website Detection in SMOTE Balanced Data

Bhoj¹,

Tripathi²,

Bisht³

et al. 2021

RS Open Journal on Innovative Communication Technologies

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The advancement in network technology has led to an exponential rise in the number of internet users across the globe. The increase in internet usage has resulted in an increase in both the number of malicious websites and cybercrimes reported over the years. Therefore, it has become critical to devise an intelligent solution that can detect malicious websites and be used in real-time systems. In our paper, we perform a comparative analysis of various feature selection techniques to build a time-efficient and accurate predictive model. To build our predictive model, a set of features are selected by feature selection methods. The selected features consist of at least 70% of the categorical features in all feature selection techniques examined in this paper. Keeping the end goal of real-time deployment of models in context the cost of processing or storing these features is far cheaper when compared to text or image-based features. We started out with a class imbalance in our data which was later dealt with using Synthetic Minority Oversampling Technique. Our proposed model also bested the existing work in the literature when compared over various evaluation metrics. The result indicated that Embedded feature selection was the best technique considering the accuracy of the model. The Filter-based technique may also be used in the context of developing a low latency system at the cost of the accuracy of the model.

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“…The experiment results exhibit that the accuracy of the proposed system in detecting phishing web sites is 97.16%. Therefore, in [14] the author demonstrates hybrid machine learning approaches that get a benefit from the strengthens of each algorithm and neglect its weaknesses. These algorithms are K-nearest neighbors (KNN) algorithm which is an effective approach against unwanted data, and the Support Vector Machine (SVM) algorithm which is a robust classifier.…”

Section: B Heuristic-based Approachmentioning

confidence: 99%

Intelligent Methods for Accurately Detecting Phishing Websites

Abuzuraiq

Alkasassbeh

Almseidin

2020

2020 11th International Conference on Information and Communication Systems (ICICS)

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With increasing technology developments, there is a massive number of websites with varying purposes. But a particular type exists within this large collection, the so-called phishing sites which aim to deceive their users. The main challenge in detecting phishing websites is discovering the techniques that have been used. Where phishers are continually improving their strategies and creating web pages that can protect themselves against many forms of detection methods. Therefore, it is very necessary to develop reliable, active and contemporary methods of phishing detection to combat the adaptive techniques used by phishers. In this paper, different phishing detection approaches are reviewed by classifying them into three main groups. Then, the proposed model is presented in two stages. In the first stage, different machine learning algorithms are applied to validate the chosen dataset and applying features selection methods on it. Thus, the best accuracy was achieved by utilizing only 20 features out of 48 features combined with Random Forest is 98.11%. While in the second stage, the same dataset is applied to various fuzzy logic algorithms. As well the experimental results from the application of Fuzzy logic algorithms were incredible. Where in applying the FURIA algorithm with only five features the accuracy rate was 99.98%. Finally, comparison and discussion of the results between applying machine learning algorithms and fuzzy logic algorithms is done. Where the performance of using fuzzy logic algorithms exceeds the use of machine learning algorithms.

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Phishing Websites Classification using Hybrid SVM and KNN Approach

Cited by 40 publications

References 12 publications

K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection

K-Nearest Neighbor Based URL Identification Model for Phishing Attack Detection

Comparative Analysis of Feature Selection Techniques for Malicious Website Detection in SMOTE Balanced Data

Intelligent Methods for Accurately Detecting Phishing Websites

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