Methodology of wavelet analysis in research of dynamics of phishing attacks

Safety of transfer and reception of various data over the internet can be accompanied by a presence of harmful components in a passed content. The phishing attack is one of versions of such harmful components. Thus it is important to know the relationship between the phishes verified as valid and suspected phishes Submitted. This is necessary for the forecast. To solve this problem, we use the wavelet analysis of time series which represent phishes verified as valid and suspected phishes submitted. We are considering the change of Hurst indicator; we analyse of a spectrum of wavelet energy. This allows you to identify the features of the main characteristics of time series which are considered. Conducted researches have shown the presence of essential duration of long-term dependence of investigated data. We also identified presence of trend component in structure of investigated series of data. It allows you to investigate recurrence of occurrence of phishing attacks that allows concentrating forces and means during the periods of activisation of such harmful influences. The analysis is spent on real data that reflects the importance of the conclusions obtained.

[1]  Zhanna V. Deineko,et al.  Properties of wavelet coefficients of self-similar time series , 2015 .

[2]  Jason Hong,et al.  The state of phishing attacks , 2012, Commun. ACM.

[3]  Patrick Flandrin,et al.  Wavelet analysis and synthesis of fractional Brownian motion , 1992, IEEE Trans. Inf. Theory.

[4]  Gang Liu,et al.  Automatic Detection of Phishing Target from Phishing Webpage , 2010, 2010 20th International Conference on Pattern Recognition.

[5]  Michael Atighetchi,et al.  Attribute-Based Prevention of Phishing Attacks , 2009, 2009 Eighth IEEE International Symposium on Network Computing and Applications.

[6]  Chuanxiong Guo,et al.  Online Detection and Prevention of Phishing Attacks , 2006, 2006 First International Conference on Communications and Networking in China.

[7]  Mohd Mahmood Ali,et al.  APD: ARM Deceptive Phishing Detector System Phishing Detection in Instant Messengers Using Data Mining Approach , 2011 .

[8]  Murad S. Taqqu,et al.  Semi-parametric graphical estimation techniques for long-memory data. , 1996 .

[9]  Anurag Jain,et al.  A Preventive Anti-Phishing Technique using Code word , 2011 .

[10]  Tamara Radivilova,et al.  COMPARATIVE ANALYSIS FOR ESTIMATING OF THE HURST EXPONET FOR STATIONARY AND NONSTATIONARY TIME SERIES , 2011 .

[11]  Mehdi Dadkhah,et al.  Similar names in academic literature as a tools to deceive researchers , 2016, Int. J. Adv. Intell. Paradigms.

[12]  Faisal Alkhateeb,et al.  Bank Web Sites Phishing Detection and Notification System Based on Semantic Web technologies , 2012 .

[13]  Youssef Iraqi,et al.  A novel Phishing classification based on URL features , 2011, 2011 IEEE GCC Conference and Exhibition (GCC).

[14]  Xavier Perramon,et al.  Phishing Secrets: History, Effects, Countermeasures , 2010, Int. J. Netw. Secur..

[15]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[16]  Anjali Avinash Chandavale,et al.  Algorithm for Secured Online Authentication Using CAPTCHA , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[17]  Oleg Kobylin,et al.  General Methodology for Implementation of Image Normalization Procedure Using its Wavelet Transform , 2014 .

[18]  S. Havlin,et al.  Detecting long-range correlations with detrended fluctuation analysis , 2001, cond-mat/0102214.

[19]  Stephen Groat,et al.  GoldPhish: Using Images for Content-Based Phishing Analysis , 2010, 2010 Fifth International Conference on Internet Monitoring and Protection.

[20]  Shujun Li,et al.  A novel anti-phishing framework based on honeypots , 2009, 2009 eCrime Researchers Summit.

[21]  Arnon Rungsawang,et al.  Using Domain Top-page Similarity Feature in Machine Learning-Based Web Phishing Detection , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[22]  P. Shanthi,et al.  Anti-phishing detection of phishing attacks using genetic algorithm , 2010, 2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES.

[23]  Patrice Abry,et al.  Stochastic integral representation and properties of the wavelet coefficients of linear fractional stable motion , 2000 .

[24]  Richard Baraniuk,et al.  The Multiscale Nature of Network Traffic: Discovery, Analysis, and Modelling , 2003 .

[25]  Mehdi Dadkhah,et al.  An Introduction to Remote Installation Vulnerability in Content Management Systems , 2015, Int. J. Secur. Softw. Eng..

[26]  Rayford B. Vaughn,et al.  Phighting the Phisher: Using Web Bugs and Honeytokens to Investigate the Source of Phishing Attacks , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).