Detection of phishing attacks using machine learning
Akkireddy Chandramouli
, K Pavankumar , G Manikanta , DN Shivaganesh reddy , G Jayakumar
Machine learning, Scikit-learn, Phishing attack detection
The expanding use of cloud services, the expanding number of users of web applications, changes in network architecture that connects devices running mobile operating systems, and continually improving network technologies all provide new cyber security challenges. As a result, network security methods, sensors, and protection methods must change to accommodate the needs and issues of users in order to counter emerging threats. Because growing application layer cyber-attacks are recognized as top risks and the key problem for network and cyber security in Phishing is a one type of social engineering attack and has become a threat for cyber world. Phishing is a form of identity theft that occur when a malicious website impersonates a legitimate one in order to acquire sensitive information such as passwords Phishing attack employ a variety of techniques such as link manipulation, filter evasion, website forgery that mimics a legitimate website. The most common approach is to set up a spoofing web page that imitates a legitimate website. Anti-phishing working groups emphasizes that phishing attacks have grown in recent years. One of the most successful methods for detecting these malicious activates is using the concept of machine learning. This is because most of phishing attacks have some common characteristics which can be identified by machine learning methods. In which machine learning model takes input as URL and helps in detecting the website whether it is legitimate or fake website.
"Detection of phishing attacks using machine learning ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.6, Issue 8, page no.77 - 83, August-2021, Available :https://ijsdr.org/papers/IJSDR2108013.pdf
Volume 6
Issue 8,
August-2021
Pages : 77 - 83
Paper Reg. ID: IJSDR_193592
Published Paper Id: IJSDR2108013
Downloads: 000347244
Research Area: Engineering
Country: Visakhapatnam, Andhra Pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
