Machine LearningTechniquesfor Detection of Website Phishing: A Review for Promises and Challenges

Odeh, Ammar; Keshta, Ismail; Abdelfattah, Eman

doi:10.1109/ccwc51732.2021.9375997

Cited by 41 publications

(21 citation statements)

References 32 publications

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“…Deep learning (DL): DL architecture is built based on neural networks that can discover hidden information in complex data through level-by-level learning [36]. The DL approach has become increasingly popular in the phishing detection domain with the recent development of DL technologies [2]. Although DL requires a more significant dataset and longer training time than the traditional ML method, it can automatically extract the features from raw data without prior knowledge [22].…”

Section: ) Classification By Methodsmentioning

confidence: 99%

“…Taxonomy Current challenges Future directions Remark [1] Reviewed only conventional ML techniques Did not include DL approaches in the literature Did not discuss the existing issues or suggest the future research directions [4] Did not examine the most recent DL techniques for phishing detection Limited discussion on open challenges and future directions [5] Lacked an exhaustive analysis on DL-based phishing detection approach Did not discuss the current issues or future research directions [6] Did not investigate the most recent DL algorithms Did not discuss the open challenges or recommend future research directions [2] Lacked an extensive review on different types of phishing attacks and DL algorithms [3] Lacked an in-depth classification of phishing detection methods Emphasized more on traditional ML techniques [7] Focused only on the role and influences of features used for learning Did not analyze DL for phishing detection in detail Contained limited discussion on future research directions [8] Concentrated more on conventional ML approaches [9] Did not provide an in-depth analysis of DL for phishing detection [10] Lacked a discussion on DL classifier to detect phishing attacks…”

Section: Referencementioning

confidence: 99%

“…It is observed that among these studies, some authors provided taxonomies of the related topics but did not discuss the open issues and future research areas [1], [4]- [6]. In contrast, other authors lacked an exhaustive review and classification of phishing detection; yet, they included current challenges and future directions in their studies [2], [3]. The authors in [7] conducted an in-depth benchmarking and evaluation on phishing detection but primarily focused on the importance of features used for learning.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Selamat

Krejcar

et al. 2022

IEEE Access

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Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Despite decades of development and improvement, existing phishing detection techniques still suffer from the deficiency in performance accuracy and the inability to detect unknown attacks. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. In recent years, deep learning has emerged as a branch of machine learning that has become a promising solution for phishing detection. As a result, this study proposes a taxonomy of deep learning algorithms for phishing detection by examining 81 selected papers using a systematic literature review approach. The paper first introduces the concept of phishing and deep learning in the context of cybersecurity. Then, phishing detection and deep learning algorithm taxonomies are provided to classify the existing literature into various categories. Next, taking the proposed taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues deep learning faces in phishing detection and proposes future research directions to overcome these challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context and highlight the related issues that motivate researchers in their future works. The results obtained from the empirical experiment showed that the common issues among most of the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and deficient detection accuracy.

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Section: ) Classification By Methodsmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Selamat

Krejcar

et al. 2022

IEEE Access

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“…Similarly, Odeh et al presented in [28] a survey on recent protection techniques that were used to detect phishing attacks on websites. They are deep learning, automated techniques, heuristic, and machine learning-based techniques.…”

Section: Safae Et Al Inmentioning

confidence: 99%

Web Tracking Domain and Possible Privacy Defending Tools: A燣iterature燫eview

Bubukayr¹,

Frikha²

2022

Journal of Cyber Security

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Personal data are strongly linked to web browsing history. By visiting a certain website, a user can share her favorite items, location, employment status, financial information, preferences, gender, medical status, news, etc. Therefore, web tracking is considered as one of the most significant internet privacy threats that can have a serious impact on end-users. Usually, it is used by most websites to track visitors through the internet in order to enhance their services and improve search customization. Moreover, selling users' data to the advertising companies without their permission. Although there are more research efforts focused on third-party tracking to protect user privacy, there are still no comprehensive approaches to develop an efficient and accessible privacy protection method, even if more attention is paid to the topic. The main goal of this paper is to conduct a literature review on the web-tracking domain and possible privacy defending methods by presenting an overview of privacy issues, determining the possible tracking mechanisms that might be exploited, discussing the available privacy defense tools that could be utilized for improvement, and presenting the strength and weaknesses of each method.

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“…As a result, researching and improving certified encryption algorithms is crucial in modern medicine [3]. Medical image encryption techniques attempt to convert a digital image to a different image data format that is difficult to recognize [4]- [7].…”

Section: Introductionmentioning

confidence: 99%

Techniques of medical image encryption taxonomy

2022

Self Cite

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Medical images are one of the most significant and sensitive data types in computer systems. Sending medical images over the internet necessitates using a robust encryption scheme that is resistant to cryptographic attacks. Confidentiality is the most critical part of the three security objectives for information systems security, namely confidentiality, integrity, and availability. Confidentiality is the most critical aspect for the secure storage and transfer of medical images. In this study, we attempt to classify various encryption methods in order to assist researchers in selecting the optimal strategy for protecting sensitive patient information while transferring medical images without alteration and outline the measures that should be adopted to address challenges and concerns relevant to techniques of medical image encryption.

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Machine LearningTechniquesfor Detection of Website Phishing: A Review for Promises and Challenges

Cited by 41 publications

References 32 publications

Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Web Tracking Domain and Possible Privacy Defending Tools: A燣iterature燫eview

Techniques of medical image encryption taxonomy

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