The application of a novel neural network in the detection of phishing websites

Feng, Fang; Zhou, Qing; Shen, Zebang; Yang, Xuhui; Wang, Jinqiang

doi:10.1007/s12652-018-0786-3

Cited by 68 publications

(26 citation statements)

References 36 publications

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“…Another approach by Feng et al [41], applied a neural network for identification the phishing web sites by adopting the Monte Carlo algorithm and risk minimization principle. Empirical results showed that their model reached a 97.71% precise detection rate and a 1.7% false alarm rate.…”

Section: Phishing and Spam Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.

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Section: Phishing and Spam Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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“…The GVM algorithm is a new classification algorithm proposed by Zhao in 2016 [3]. Since it contains the design risk minimization and Monte Carlo (MC) algorithm, it has strong generalization ability and has been successfully applied in phishing detection [29], Android malware detection [30], groundwater status forecasting [31], electricity demand prediction [32]. Yong et al proposed a derivative-based Monte Carlo algorithm to accelerate the training of GVM based on the GVM [33].…”

Section: B Alo and Gvmmentioning

confidence: 99%

Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification

Feng

Shen

et al. 2020

IEEE Access

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Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and BALO algorithms to tackle the imbalanced classification problem, delivering different cost weights to different classes of samples. In our method, the BALO algorithm determines the cost weights and extract more significant features to improve the classification performance. Experiments conducted on eleven imbalanced data sets have shown that the CFGVM algorithm significantly improves the classification performance of minority class samples. By comparing with similar algorithms and stateof-the-art algorithms, the proposed algorithm significantly outperforms in performance and produces better classification results. INDEX TERMS Imbalanced data, cost-sensitive, general vector machine, binary ant lion optimizer.

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“…ML-based approaches to detect phishing websites is an active research area that employs a wide range of supervised classification techniques to segregate phishing class. Feng et al propose a novel neural network for phishing detection [10]. They improve the generalization ability of the network by designing risk minimization principle.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection

Zabihimayvan

Doran

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.

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The application of a novel neural network in the detection of phishing websites

Cited by 68 publications

References 36 publications

Artificial Intelligence in the Cyber Domain: Offense and Defense

Artificial Intelligence in the Cyber Domain: Offense and Defense

Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification

Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection

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