Detecting Phishing Sites -- An Overview

Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal information. To minimize the damage caused by phishing must be detected as early as possible. There are various phishing attacks like spear phishing, whaling, vishing, smishing, pharming and so on. There are various phishing detection techniques based on whitelist, black-list, content-based, URL-based, visualsimilarity and machine-learning. In this paper, we discuss various kinds of phishing attacks, attack vectors and detection techniques for detecting the phishing sites. Performance comparison of 18 different models along with nine different sources of datasets are given. Challenges in phishing detection techniques are also given. KeywordsPhishing, Websites, Detection, Machine-learning

[1]  Ozgur Koray Sahingoz,et al.  Detection of Phishing Websites by Using Machine Learning-Based URL Analysis , 2020, 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[2]  Jack W. Stokes,et al.  Texception: A Character/Word-Level Deep Learning Model for Phishing URL Detection , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[4]  M. Sathish Kumar,et al.  Frequent rule reduction for phishing URL classification using fuzzy deep neural network model , 2020 .

[5]  Ozgur Koray Sahingoz,et al.  PHISHING DETECTION FROM URLS BY USING NEURAL NETWORKS , 2018, Computer Science & Information Technology (CS & IT).

[6]  Jian Feng,et al.  A Phishing Webpage Detection Method Based on Stacked Autoencoder and Correlation Coefficients , 2019, J. Comput. Inf. Technol..

[7]  Moitrayee Chatterjee,et al.  Detecting Phishing Websites through Deep Reinforcement Learning , 2019, 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC).

[8]  Gregor von Bochmann,et al.  Using URL shorteners to compare phishing and malware attacks , 2018, 2018 APWG Symposium on Electronic Crime Research (eCrime).

[9]  Yong Jiang,et al.  CNN-MHSA: A Convolutional Neural Network and multi-head self-attention combined approach for detecting phishing websites , 2020, Neural Networks.

[10]  Ehab Al-Shaer,et al.  PhishMon: A Machine Learning Framework for Detecting Phishing Webpages , 2018, 2018 IEEE International Conference on Intelligence and Security Informatics (ISI).

[11]  Jinghui Qin,et al.  Phishing URL Detection Via Capsule-Based Neural Network , 2019, 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID).

[12]  Saad A. Al-Ahmadi,et al.  Robust URL Phishing Detection Based on Deep Learning , 2020, KSII Trans. Internet Inf. Syst..

[13]  Prakhar Srivastava,et al.  Detection of Phishing Websites using an Efficient Feature-Based Machine Learning Framework , 2020 .

[14]  R. Leela Velusamy,et al.  Detection of Phishing Attacks using Radial Basis Function Network Trained for Categorical Attributes , 2020, 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[15]  Eduardo Feitosa,et al.  Heuristic-based strategy for Phishing prediction: A survey of URL-based approach , 2020, Comput. Secur..

[16]  Bassam H. Hammo,et al.  A Convolutional Neural Network Model to Detect Illegitimate URLs , 2020, 2020 11th International Conference on Information and Communication Systems (ICICS).

[17]  Happy Chapla,et al.  A Machine Learning Approach for URL Based Web Phishing Using Fuzzy Logic as Classifier , 2019, 2019 International Conference on Communication and Electronics Systems (ICCES).

[18]  Yingke Chen,et al.  HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis , 2019, 2020 International Joint Conference on Neural Networks (IJCNN).

[19]  Mario Fritz,et al.  VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity , 2020, CCS.

[20]  Abdulhamit Subasi,et al.  Comparison of Adaboost with MultiBoosting for Phishing Website Detection , 2020 .

[21]  Thikra M. Abed,et al.  Anti-Phishing System Using Intelligent Techniques , 2019, 2019 2nd Scientific Conference of Computer Sciences (SCCS).

[22]  B. Janet,et al.  Phishing Website Classification and Detection Using Machine Learning , 2020, 2020 International Conference on Computer Communication and Informatics (ICCCI).

[23]  Yang Su,et al.  Research on Website Phishing Detection Based on LSTM RNN , 2020, 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC).

[24]  M. Arivukarasi,et al.  Performance Analysis of Malicious URL Detection by using RNN and LSTM , 2020, 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC).

[25]  M. A. Akanbi,et al.  Deep Learning with Convolutional Neural Network and Long Short-Term Memory for Phishing Detection , 2019, 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA).

[26]  Alwyn Roshan Pais,et al.  Detection of phishing websites using an efficient feature-based machine learning framework , 2018, Neural Computing and Applications.

[27]  Jinghui Qin,et al.  Phishing URL Detection via CNN and Attention-Based Hierarchical RNN , 2019, 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).

[28]  Hossein Gharaee,et al.  An Adaptive Machine Learning Based Approach for Phishing Detection Using Hybrid Features , 2019, 2019 5th International Conference on Web Research (ICWR).

[29]  Indrakshi Ray,et al.  Fresh-Phish: A Framework for Auto-Detection of Phishing Websites , 2017, 2017 IEEE International Conference on Information Reuse and Integration (IRI).

[30]  Khin T. Lwin,et al.  Intelligent phishing detection scheme using deep learning algorithms , 2020, J. Enterp. Inf. Manag..

[31]  RYAN HEARTFIELD,et al.  A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks , 2015, ACM Comput. Surv..

[32]  G. Jaspher Willsie Kathrine,et al.  Variants of phishing attacks and their detection techniques , 2019, 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI).

[33]  Meenu,et al.  Phishing Website Detection Based on Machine Learning: A Survey , 2020, 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS).

[34]  Akihito Nakamura,et al.  Proactive Phishing Sites Detection , 2019, 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[35]  Anggit Ferdita Nugraha,et al.  Meta-Algorithms for Improving Classification Performance in the Web-phishing Detection Process , 2019, 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE).

[36]  Ankit Kumar Jain,et al.  A machine learning based approach for phishing detection using hyperlinks information , 2018, Journal of Ambient Intelligence and Humanized Computing.

[37]  A. A,et al.  Towards the Detection of Phishing Attacks , 2020, 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184).

[38]  Alwyn R. Pais,et al.  A heuristic technique to detect phishing websites using TWSVM classifier , 2020, Neural Computing and Applications.

[39]  Alwyn Roshan Pais,et al.  Efficient deep learning techniques for the detection of phishing websites , 2020, Sādhanā.

[40]  Sanjay Jha,et al.  PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites , 2020, 2020 IEEE Conference on Communications and Network Security (CNS).

[41]  Noah Ndakotsu Gana,et al.  Machine Learning Classification Algorithms for Phishing Detection: A Comparative Appraisal and Analysis , 2019, 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf).

[42]  Kholoud Althobaiti,et al.  A Review of Human- and Computer-Facing URL Phishing Features , 2019, 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW).

[43]  Sohrab Hossain,et al.  Phishing Attacks Detection using Deep Learning Approach , 2020, 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT).

[44]  B. Janet,et al.  Natural language processing and Machine learning based phishing website detection system , 2019, 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).

[45]  Iwao Sasase,et al.  Visual Similarity-Based Phishing Detection Scheme Using Image and CSS with Target Website Finder , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[46]  Ahmed Iqbal Pritom,et al.  Phishing Website Detection Using Effective Classifiers and Feature Selection Techniques , 2019, 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET).

[47]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[48]  Erzhou Zhu,et al.  OFS-NN: An Effective Phishing Websites Detection Model Based on Optimal Feature Selection and Neural Network , 2019, IEEE Access.

[49]  Sohrab Hossain,et al.  Phishing Attacks Detection using Machine Learning Approach , 2020, 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT).

[50]  Suleiman Y. Yerima,et al.  High Accuracy Phishing Detection Based on Convolutional Neural Networks , 2020, 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS).

[51]  Bakshi Rohit Prasad,et al.  Phishing Website Detection Using Neural Network and Deep Belief Network , 2019 .

[52]  Saad Al-Ahmadi,et al.  PDMLP: Phishing Detection using Multilayer Perceptron , 2020, International Journal of Network Security & Its Applications.

[53]  Derek Doran,et al.  Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection , 2019, 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[54]  Ozgur Koray Sahingoz,et al.  Feature Selections for the Classification of Webpages to Detect Phishing Attacks: A Survey , 2020, 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA).

[55]  Alwyn R. Pais,et al.  CatchPhish: detection of phishing websites by inspecting URLs , 2020, J. Ambient Intell. Humaniz. Comput..

[56]  Qingshan Jiang,et al.  An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL , 2020, Electronics.

[57]  Steven C. H. Hoi,et al.  Malicious URL Detection using Machine Learning: A Survey , 2017, ArXiv.

[58]  Tony Thomas,et al.  On Effectiveness of Source Code and SSL Based Features for Phishing Website Detection , 2019, 2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE).