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Detection of phishing websites using deep learning and machine learning

01 Jan 2020-Vol. 7, Iss: 8, pp 1027-1032
TL;DR: When a user opens a fake webpage and enters the username and protected password, the credentials of the user are acquired by the attacker which can be used for malicious purposes.
Abstract: Phishing can be described as a way by which someone may try to steal some personal and important information By appearing as a trusted body Many websites, which look perfectly legitimate to us, can be phishing and could well be the reason for various online frauds These phishing websites may try to obtain our important information through many ways, for example: phone, calls, messages, and pop up windows When a user opens a fake webpage and enters the username and protected password, the credentials of the user are acquired by the attacker which can be used for malicious purposes Phishing websites look very similar in appearance to their corresponding legitimate websites to attract large number of Internet users
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Proceedings ArticleDOI
01 Jan 2018
TL;DR: This work uses natural language processing techniques to analyze text and detect inappropriate statements which are indicative of phishing attacks, and focuses on the natural language text contained in the attack, performing semantic analysis of the text to detect malicious intent.
Abstract: Phishing attacks are one of the most common and least defended security threats today. We present an approach which uses natural language processing techniques to analyze text and detect inappropriate statements which are indicative of phishing attacks. Our approach is novel compared to previous work because it focuses on the natural language text contained in the attack, performing semantic analysis of the text to detect malicious intent. To demonstrate the effectiveness of our approach, we have evaluated it using a large benchmark set of phishing emails.

114 citations


"Detection of phishing websites usin..." refers background in this paper

  • ...[3] Authors presents a way to find phishing email attacks using natural language processing and machine learning....

    [...]

Proceedings ArticleDOI
27 Apr 2014
TL;DR: A new phishing detection approach based on the features of URL, which focuses on the similarity of phishing site's URL and legitimate site'sURL and shows that the technique can detect over 97% phishing sites.
Abstract: Together with the growth of e-commerce transaction, Phishing - the act of stealing personal information - rises in quantity and quality. The phishers try to make fake-sites look similar to legitimate sites in terms of interface and uniform resource locator (URL) address. Therefore, the numbers of victim have been increasing due to inefficient methods using blacklist to detect phishing. This paper proposes a new phishing detection approach based on the features of URL. Specifically, the proposed method focuses on the similarity of phishing site's URL and legitimate site's URL. In addition, the ranking of site is also considered as an important factor to decide whether the site is a phishing site. The proposed technique is evaluated with a dataset of 11,660 phishing sites and 5,000 legitimate sites. The results show that the technique can detect over 97% phishing sites.

57 citations

Proceedings ArticleDOI
22 Mar 2018
TL;DR: In this paper, phising website dataset taken from UCI was investigated, its dimension was reduced, and the performance comparison of classification algorithms is studied on reduced phishing website dataset.
Abstract: The Internet is becoming a necessary and important tool in everyday life. However, Internet users might have poor security for different kinds of web threats, which may lead to financial loss or clients lacking trust in online trading and banking. Phishing is described as a skill of impersonating a trusted website aiming to obtain private and secret information such as a username and password or social security and credit card number. In this paper, phising website dataset taken from UCI was investigated. Its dimension was reduced and the performance comparison of classification algorithms is studied on reduced phishing website dataset. Phishing website dataset was taken from UCI machine learning repository. This dataset consists of 11055 records and 31 features. Feature selection algorithms were applied to reduce the dimension of phishing website dataset and to obtain higher classification performance. Then, the performance of classification algorithms is compared to other data mining classification algorithms. Finally, a comparative classification performance on the reduced dataset by using the common classification algorithms is given.

48 citations


"Detection of phishing websites usin..." refers methods in this paper

  • ...[4] Another method by authors proposes feature selection...

    [...]

Proceedings ArticleDOI
20 Apr 2018
TL;DR: A model is put forth as a solution to detect phishing websites by using the URL detection method using Random Forest algorithm.
Abstract: Phishing is an unlawful activity wherein people are misled into the wrong sites by using various fraudulent methods. The aim of these phishing websites is to confiscate personal information or other financial details for personal benefits or misuse. As technology advances, the phishing approaches used need to get progressed and there is a dire need for better security and better mechanisms to prevent as well as detect these phishing approaches. The primary focus of this paper is to put forth a model as a solution to detect phishing websites by using the URL detection method using Random Forest algorithm. There are 3 major phases such as Parsing, Heuristic Classification of data, Performance Analysis in this model and each phase makes use of a different technique or algorithm for processing of data to give better results.

47 citations


"Detection of phishing websites usin..." refers methods in this paper

  • ...[5] Proposed model with answer for recognize phishing sites by using URL identification strategy utilizing Random Forest algorithm....

    [...]

Proceedings ArticleDOI
07 Dec 2015
TL;DR: In this study, websites' URL features are extracted and subset based feature selection methods and classification algorithms for phishing websites detection are analyzed.
Abstract: In this study we extracted websites' URL features and analyzed subset based feature selection methods and classification algorithms for phishing websites detection.

45 citations