Detecting malicious URLs using machine learning techniques

Vanhoenshoven, Frank; Nápoles, Gonzalo; Falcón, Rafael; Vanhoof, Koen; Köppen, Mario

doi:10.1109/ssci.2016.7850079

Cited by 100 publications

(34 citation statements)

References 19 publications

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“…5 4 24,520 138,925 99.78% (Zhao and Hoi, 2013) Classic Perceptron 990,000 10,000 99.49% (Patil and Patil, 2018) Random Forest 26,041 26,041 99.44% (Zhao and Hoi, 2013) Label Efficient Perceptron 990,000 10,000 99.41% (Chen et al, 2014) Logistic Regression 1,945 404 99.40% (Cui et al, 2018) SVM 24,520 138,925 99.39% (Patil and Patil, 2018) Fast Decision Tree Learner REPTree26,041 26,041 99.19% (Zhao and Hoi, 2013) Cost-sensitive Perceptron 990,000 10,000 99.18% (Patil and Patil, 2018) C A R T 5 26,041 26,041 99.15% (Jain and Gupta, 2018b) Random Forest 2,141 1,918 99.09% (Patil and Patil, 2018) J 4 8 6 26,041 26,041 99.03% (Verma and Dyer, 2015) J48 11,271 13,274 99.01% (Verma and Dyer, 2015) P A R T 7 11,271 13,274 98.98% (Verma and Dyer, 2015) Random Forest 11,271 13,274 98.88% (Shirazi et al, 2018) Gradient Boosting 1,000 1,000 98,78% (Cui et al, 2018) Naïve-Bayes 24,520 138,925 98,72% (Cui et al, 2018) C4.5 356,215 2,953,700 98.70% (Patil and Patil, 2018) Alternating Decision Tree 26,041 26,041 98.48% (Shirazi et al, 2018) SVM (Linear) 1,000 1,000 98,46% (Shirazi et al, 2018) CART 1,000 1,000 98,42% (Adebowale et al, 2019) Adaptive Neuro-Fuzzy Inference System 6,843 6,157 98.30% (Vanhoenshoven et al, 2016) Random Forest 1,541,000 759,000 98.26% (Jain and Gupta, 2018b) Logistic Regression 2,141 1,918 98.25% (Patil and Patil, 2018) Random Tree 26,041 26,041 98.18% (Shirazi et al, 2018) k-Nearest Neighbuors 1,000 1,000 98,05% (Vanhoenshoven et al, 2016) Multi Layer Perceptron 1,541,000 759,000 97.97% (Verma and Dyer, 2015) Logistic Regression 11,271 13,274 97.70% (Jain and Gupta, 2018b) Naïve-Bayes 2,141 1,918 97.59% (Vanhoenshoven et al, 2016) k-Nearest Neighbours 1,541,000 759,000 97.54% (Shirazi et al, 2018) SVM (Gaussian) 1,000 1,000 97,42% (Vanhoenshoven et al, 2016) C 5 . 0 8 1,541,000 759,000 97.40%…”

Section: Referencementioning

confidence: 99%

Comparison of Classification Algorithms for Detection of Phishing Websites

Vaitkevicius¹,

Marcinkevičius²

2020

Informatica

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Phishing activities remain a persistent security threat, with global losses exceeding 2.7 billion USD in 2018, according to the FBI's Internet Crime Complaint Center. In literature, different generations of phishing websites detection methods have been observed. The oldest methods include manual blacklisting of known phishing websites' URLs in the centralized database, but they have not been able to detect newly launched phishing websites. More recent studies have attempted to solve phishing websites detection as a supervised machine learning problem on phishing datasets, designed on features extracted from phishing websites' URLs. These studies have shown some classification algorithms performing better than others on differently designed datasets but have not distinguished the best classification algorithm for the phishing websites detection problem in general. The purpose of this research is to compare classic supervised machine learning algorithms on all publicly available phishing datasets with predefined features and to distinguish the best performing algorithm for solving the problem of phishing websites detection, regardless of a specific dataset design. Eight widely used classification algorithms were configured in Python using the Scikit Learn library and tested for classification accuracy on all publicly available phishing datasets. Later, classification algorithms were ranked by accuracy on different datasets using three different ranking techniques while testing the results for a statistically significant difference using Welch's T-Test. The comparison results are presented in this paper, showing ensembles and neural networks outperforming other classical algorithms.

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Section: Referencementioning

confidence: 99%

Comparison of Classification Algorithms for Detection of Phishing Websites

Vaitkevicius¹,

Marcinkevičius²

2020

Informatica

View full text Add to dashboard Cite

show abstract

“…The presence of malicious URL in phishing emails is a key characteristics of spam emails and Vanhoenshoven et al [185] tested the effectiveness of RF in detecting such URLs within spam emails using a publicly available database. The authors came into conclusion that with an accuracy of 97.69%, RF actually performed better than few other classification techniques such as MLP, C4.5 Decision Tree, SVM and NB.…”

Section: K: Supervised Systems Discussing Performance Of Different Almentioning

confidence: 99%

A Comprehensive Survey for Intelligent Spam Email Detection

et al. 2019

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The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject-appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection. INDEX TERMS Machine learning, phishing attack, spear phishing, spam detection, spam email, spam filtering.

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“…In this framework, the traditional tools and infrastructures are not useful because we deal with big data created with a high velocity, and the solutions and predictions must be faster than the threats. Artificial Intelligence and ML analytics have turned out in one of the most powerful tools against the cyberattackers (see [35][36][37][38][39][40][41]), but obtaining actionable knowledge from a database of Cybersecurity events by applying ML algorithms usually is a computationally expensive task for several reasons. A database of Cybersecurity contains, in general, a huge amount of dynamical, and unstructured but highly correlated and connected data, so we need to deal with some costly aspects of the quality of the data such as noise, trustworthiness, security, privacy, heterogeneity, scaling, or timeliness [42][43][44].…”

Section: Complexitymentioning

confidence: 99%

Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm

et al. 2019

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Selecting the best configuration of hyperparameter values for a Machine Learning model yields directly in the performance of the model on the dataset. It is a laborious task that usually requires deep knowledge of the hyperparameter optimizations methods and the Machine Learning algorithms. Although there exist several automatic optimization techniques, these usually take significant resources, increasing the dynamic complexity in order to obtain a great accuracy. Since one of the most critical aspects in this computational consume is the available dataset, among others, in this paper we perform a study of the effect of using different partitions of a dataset in the hyperparameter optimization phase over the efficiency of a Machine Learning algorithm. Nonparametric inference has been used to measure the rate of different behaviors of the accuracy, time, and spatial complexity that are obtained among the partitions and the whole dataset. Also, a level of gain is assigned to each partition allowing us to study patterns and allocate whose samples are more profitable. Since Cybersecurity is a discipline in which the efficiency of Artificial Intelligence techniques is a key aspect in order to extract actionable knowledge, the statistical analyses have been carried out over five Cybersecurity datasets.

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Detecting malicious URLs using machine learning techniques

Cited by 100 publications

References 19 publications

Comparison of Classification Algorithms for Detection of Phishing Websites

Comparison of Classification Algorithms for Detection of Phishing Websites

A Comprehensive Survey for Intelligent Spam Email Detection

Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm

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