An Ensemble Model for Detecting Phishing Attack with Proposed Remove-Replace Feature Selection Technique

Hota, H. S.; Shrivas, Akhilesh Kumar; Hota, Rahul

doi:10.1016/j.procs.2018.05.103

Cited by 35 publications

(14 citation statements)

References 14 publications

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“…Each earlier phishing detection approaches showed an acceptable detection accuracy while using specific feature patterns with selected detection algorithms in their specific application domain [7,8]. Currently, the usage of individual classification algorithm in phishing detection is developing to a combination of multiple classifiers in the form of ensemble methods to produce a better accuracy with more efficiency [9][10][11]. Unfortunately, most existing ensemble classification techniques in phishing detection could not afford to adapt automatically on the variation of input feature patterns, and it remains as a challenging issue in the phishing detection works [12].…”

Section: Introductionmentioning

confidence: 99%

A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection

Zaw

Vasupongayya

2019

Journal of Computer Networks and Communications

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Currently, the smartphone contains lots of sensitive information. The increasing number of smartphone usage makes it more interesting for phishers. Existing phishing detection techniques are performed on their specific features with selected classifiers to get their best accuracy. An effective phishing detection approach is required to adapt the concept drift of mobile phishing and prevent degradation in accuracy. In this work, an adaptive phishing detection approach based on case-based reasoning technique is proposed to handle the concept drift challenge in phishing apps. Several experiments are conducted in order to demonstrate the design decision of our proposed model. The proposed model is evaluated with a large feature set containing 1,065 features from 10 different categories. These features are extracted from more than 10,000 android applications. Five combinations of features are created in order to mimic new real-world Android apps to evaluate our experiments. Moreover, a reduced feature set is also studied in this work in order to improve the efficiency of the proposed model. Both accuracy and efficiency of the proposed model are evaluated. The experimental results show that our proposed model achieves acceptable accuracy and efficiency for the phishing detection.

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Section: Introductionmentioning

confidence: 99%

A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection

Zaw

Vasupongayya

2019

Journal of Computer Networks and Communications

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“…The results show that they have the highest accuracies of 93.5% in comparison with other studies. The research [ 29 ] proposed a feature selection technique named as Remove Replace Feature Selection Technique (RRFST). They claim that they got the phishing email dataset from the khoonji’s anti-phishing website containing 47 features.…”

Section: Literature Surveymentioning

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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“…Esto ocasionó que las antiguas técnicas de detección de phishing no funcionen contra los ataques más recientes (Bulakh, 2016, p. 1). Como consecuencia de esta amenaza evolutiva, se ha decidido usar técnicas de machine learning que permitan encontrar nuevas páginas fraudulentas en un periodo de tiempo largo o indefinido (Hota, 2018;Medvet, 2008;Chen, 2010;Bulakh, 2016;Rajab, 2018).…”

Section: Estado Del Arteunclassified

“…Lamentablemente, los métodos propuestos no son capaces de detectar nuevas variantes de estos ataques, debido a que las métricas más importantes para detectar páginas phishing derivan de experiencias humanas (Mao, Bian, Tian, Zhhu, Wei, Li y Liang, 2018, p. 2). Por dicho motivo, en la actualidad se recurre a la inteligencia artificial para poder identificar páginas phishing de manera dinámica y automática utilizando para ello diferentes métricas (Abu-Nimeh, 2007;Al-Janabi, 2017;Bulakh, 2016;Chen, 2010;Hota, 2018;Jain, 2016;Mao, 2018;Medvet, 2008;Mourtaji, 2017;Rajab, 2018;Sanglerdsinlapachai, 2010).…”

Section: Introductionunclassified

Comparación de técnicas de machine learning para detección de sitios web de phishing

Moncada-Vargas

2020

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El phishing es el robo de datos personales a través de páginas web falsas. La víctima de este robo es dirigida a esta página falsa, donde se le solicita ingresar sus datos para validar su identidad. Es en ese momento que se realiza el robo, ya que al ingresar sus datos, estos son almacenados y usados por el hacker responsable de dicho ataque para venderlos o ingresar a las entidades y realizar robos o estafas. Para este trabajo se ha investigado sobre distintos métodos de detección de páginas web phishing utilizando técnicas de machine learning. Así, el propósito de este trabajo es realizar una comparación de dichas técnicas que han demostrado ser las más efectivas en la detección de los sitios web phishing. Los resultados obtenidos demuestran que los clasificadores de árboles, denominados Árbol de Decisión y Bosque Aleatorio, han alcanzado las mayores tasas de precisión y efectividad, con valores de entre 97 % y 99 % en la detección de este tipo de páginas.

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An Ensemble Model for Detecting Phishing Attack with Proposed Remove-Replace Feature Selection Technique

Cited by 35 publications

References 14 publications

A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection

A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection

A comprehensive survey of AI-enabled phishing attacks detection techniques

Comparación de técnicas de machine learning para detección de sitios web de phishing

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