A URL with Image-based Feature Extraction for Preventing Phishing Attacks
محتوى المقال
الملخص
Currently, Phishing is a type of attack in which cyber criminals tricks the victims to steal their personal and financial data. It has become an organized criminal activity. Spoofed emails claiming to be from legitimate source are crafted in a way to lead victims to reveal their personal, financial data by misdirecting them to the counterfeit website. We compared previous password protection solutions, some of the presented solutions use specialized equipment or additional servers to protect passwords. Other solutions are prone to spoofing and phishing attacks as well as introduce usability issues. Also these solutions do not address the challenge of protecting passwords against the adversary who can, for instance, exploit server-side software vulnerabilities. Our goal is enhancing the best solution to prevent phishing by alerting the users from phishing websites when detected based on URL with image-based feature extraction method.
الكلمات المفتاحية
Phishing attacks، Feature extraction، Image processing، Speedup
التنزيلات
المراجع
C. Herley, P. C. Van Oorschot and A. S. Patrick, “Passwords: If
We’re So Smart, Why Are We Still Using Them?” in Int. Conf.
Financ. Cryptogr. Data Secur., in Financial Cryptography and
Data Security, in Lecture Note in Computer Science, vol. 5628,
pp. 230-237, 2009.
C. Herley and P. Van Oorschot, "A Research Agenda Acknowledging
the Persistence of Passwords," in IEEE Secur. Priv., vol.
, no. 1, pp. 28-36, Jan.-Feb. 2012, doi: 10.1109/MSP.2011.150.
J. Bonneau, C. Herley, P. C. v. Oorschot and F. Stajano, "The
Quest to Replace Passwords: A Framework for Comparative
Evaluation of Web Authentication Schemes," 2012 IEEE Symp.
Secur. Priv., San Francisco, CA, 2012, pp. 553-567, doi: 10.1109/
SP.2012.44.
J. Yan, A. Blackwell, R. Anderson and A. Grant, “The memorability
and security of passwords – some empirical results,” Univ.
Cambridge, United Kingdom, Rep. UCAN-CL-TR-500, Sept.
B. Menkus, “Understanding the use of passwords,” Comput. Secur.,
vol. 7, no. 2, pp. 132-136, Apr. 1988, doi: 10.1016/0167-
(88)90325-2.
S. Gaw and E. W. Felten, “Password management strategies for
online accounts,” in Proc. Second Symp. Usable Priv. Secur., July
, pp. 44-55, doi: 10.1145/1143120.1143127.
E. F. Gehringer, "Choosing passwords: security and human
factors," IEEE 2002 Int. Symp. Technol. Soci. (ISTAS'02). Soci.
Implic. Inf. Commun. Technol. Proc. (Cat. No.02CH37293),
Raleigh, NC, USA, 2002, pp. 369-373, doi: 10.1109/ISTAS.
1013839.
A. K. Jain and B. B. Gupta, “A novel approach to protect against
phishing attacks at client side using auto-updated white-list,”
EURASIP J. Inf. Secur., 2016, no. 9, May 2016, doi: 10.1186/
s13635-016-0034-3.
M. He et al, “An efficient phishing webpage detector,” Expert
Syst. Appl., vol. 38, no. 10, pp. 12018-12027, Sept. 2011, doi:
1016/j.eswa.2011.01.046.
Y. Pan and X. Ding, "Anomaly Based Web Phishing Page Detection,"
22nd Annu. Comput. Secur. Appl. Conf. (ACSAC'
, Miami Beach, FL, 2006, pp. 381-392, doi: 10.1109/
ACSAC.2006.13.
R. Islam and J. Abawajy, “A multi-tier phishing detection and
filtering approach,” J. Netw. Comput. Appl., vol. 36, no. 1, pp.
-335, Jan. 2013, doi: 10.1016/j.jnca.2012.05.009.
S. C. Jeeva and E. B. Rajsingh, “Intelligent phishing url detection
using association rule mining,” Human-Centric Comput. Inf.
Sci., vol. 6, no. 10, July 2016, doi: 10.1186/s13673-016-0064-3.
Y. Li, R. Xiao, J. Feng and L. Zhao, “A semi-supervised learning
approach for detection of phishing webpages,” Optik, vol. 124, no.
, pp. 6027-6033, Dec. 2013, doi; 10.1016/j.ijleo.2013.04.078.
S. Roopak and T. Thomas, "A Novel Phishing Page Detection
Mechanism Using HTML Source Code Comparison and Cosine
Similarity," 2014 Fourth Int. Conf. Adv. Comput. Commun., Cochin,
, pp. 167-170, doi: 10.1109/ICACC.2014.47.
D. Abraham and N. S. Raj, "Approximate string matching algorithm
for phishing detection," 2014 Int. Conf. Adv. Comput.
Commun. Inf. (ICACCI), New Delhi, 2014, pp. 2285-2290, doi:
1109/ICACCI.2014.6968578.
S. Garera, N. Provos, M. Chew and A. D. Rubin, “A framework
for detection and measurement of phishing attacks,” in Proc.
ACM Workshop Recurr. Malcode, Nov. 2007, pp. 1-8, doi:
1145/1314389.1314391.
A. Le, A. Markopoulou and M. Faloutsos, "PhishDef: URL
names say it all," 2011 Proc. IEEE INFOCOM, Shanghai, 2011,
pp. 191-195, doi: 10.1109/INFCOM.2011.5934995.
E. H. Chang, K. L. Chiew, S. N. Sze and W. K. Tiong, "Phishing
Detection via Identification of Website Identity," 2013 Int. Conf.
IT Converg. Secur. (ICITCS), Macao, 2013, pp. 1-4, doi: 10.1109/
ICITCS.2013.6717870.
M. G. Alkhozae and O. A. Batarfi, “Phishing Websites Detection
based on Phishing Characteristics in the Webpage Source Code,”
Int. J. Inf. Commun. Technol. Res., vol. 1, no. 6, pp. 283-291, Oct.
C. L. Tan, K. L. Chiew and S. N. Sze, “Phishing Webpage Detection
Using Weighted URL Tokens for Identity Keywords Retrieval,”
in 9th Int. Conf. Robot. Vis. Signal Process. Power Appl., in
Lecture Notes in Electrical Engineering, vol. 398, pp. 133-139,
R. B. Basnet and T. Doleck, "Towards Developing a Tool to Detect
Phishing URLs: A Machine Learning Approach," 2015 IEEE
Int. Conf. Comput. Intell. Commun. Technol., Ghaziabad, 2015,
pp. 220-223, doi: 10.1109/CICT.2015.63.
G. Sonowal and K. S. Kuppusamy, “MASPHID: A Model to
Assist Screen Reader Users for Detecting Phishing Sites Using
Aural and Visual Similarity Measures,” in Proc. Int. Conf. Inf.
Anal., Aug. 2016, pp. 1-6, doi: 10.1145/2980258.2980443.
J. Lee, D. Kim and C. Lee, “Heuristic-based Approach for Phishing
Site Detection Using URL Features,” in Proc. Third Int. Conf.
Adv. Comput. Electron. Electr. Technol., Apr. 2015, pp. 131-135.
C. Dhinakaran, D. Nagamalai and J. K. Lee, “Multilayer Approach
to Defend Phishing Attacks,” arXiv: 1108.1593, 2011,
[Online] Available: https://arxiv.org/abs/1108.1593
تفاصيل المقال
هذا العمل مرخص بموجب Creative Commons Attribution-NonCommercial 4.0 International License.