Results 111 to 120 of about 9,881 (133)

Command & Control: Understanding, Denying and Detecting - A review of malware C2 techniques, detection and defences

, 2014
In this survey, we first briefly review the current state of cyber attacks, highlighting significant recent changes in how and why such attacks are performed. We then investigate the mechanics of malware command and control (C2) establishment: we provide
Cova, Marco, Gardiner, Joseph, Nagaraja, Shishir   +2 more
core  

Detecting Phishing Websites Using Machine Learning

IJARCCE
Abstract—Phishing websites remain a dominant entry-point for cyber-attacks. This study proposes a light-weight machine learning pipeline that spots malicious URLs in real time. We collected 734 benign links from Alexa and 412 confirmed phishing links from PhishTank, extracted 30 lexical and host-based features, and trained five classifiers.
B. Sucharitha, B. Chandini, D. Satya Kumar, M. Surendra, Dr. G. Kishor Kumar   +4 more
openaire   +2 more sources

Evasion Attacks and Defense Mechanisms for Machine Learning-Based Web Phishing Classifiers

IEEE Access
Phishing is an electronic fraud through which an attacker can access user credentials. Phishing websites are the ones that mimic legitimate websites. Fraudsters can replace them within hours to evade their detection.
Manu J. Pillai, S. Remya, V. Devika, Somula Ramasubbareddy, Yongyun Cho   +4 more
doaj   +1 more source

A Novel Architecture Based on Weight Freezing and Random Forest for Website Phishing Detection

International Journal of Computational Intelligence Systems
Phishing is a severe cybersecurity threat that continues to cause significant economic losses and breaches of privacy globally. This paper presents a novel architecture for effective phishing website detection, integrating a unique weight freezing ...
Ali Jasim Khaleefah AL-JABERI, Sefer Kurnaz, Raghda Awad Shaban Naseri, Hameed Mutlag Farhan   +3 more
doaj   +1 more source

RSTHFS: A Rough Set Theory-Based Hybrid Feature Selection Method for Phishing Website Classification

IEEE Access
Phishing is a pervasive form of cybercrime where malicious websites deceive users into revealing sensitive information, e.g., passwords and credit card details. Despite advances in cybersecurity, accurately detecting phishing websites remains challenging
Jahanggir Hossain Setu, Nabarun Halder, Ashraful Islam, M. Ashraful Amin   +3 more
doaj   +1 more source

Phishing Website Detection using Machine Learning

IJARCCE, 2022
Gayathri V, Dr. Malatesh S H
openaire   +1 more source

Detecting Phishing Website Using Machine Learning

International Journal of Innovations in Engineering and Science, 2023
Prof. Monika Ingole, Achal Meshram, Swati Thengane, Asmita Raut, Prachi Pati   +4 more
openaire   +1 more source

Deep learning based phishing website detection

TELKOMNIKA (Telecommunication Computing Electronics and Control), 2023
N. Subhashini, Amogh Banerjee, Abhi Kumar, S. Muthulakshmi, S. Revathi   +4 more
openaire   +1 more source

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Detection of Phishing Websites

2023
Phishing attacks are one of the biggest security threats to personal and financial information on the internet. They are a type of cyber-attack where attackers pretend to be a trusted entity in order to trick people into revealing sensitive information, such as passwords and credit card numbers.
Lakshmipathi Gejjala, Muthukumar Arunachalam, Bala Manikanta Eswar Duggisetty, Jaswanth Kumar Reddy Vardireddy   +3 more
openaire   +1 more source

User behaviour based phishing websites detection

2008 International Multiconference on Computer Science and Information Technology, 2008
Phishing detection systems are principally based on the analysis of data moving from phishers to victims. In this paper we describe a novel approach to detect phishing websites based on analysis of userspsila online behaviours - i.e., the websites users have visited, and the data users have submitted to those websites.
null Xun Dong, John A. Clark, Jeremy L. Jacob   +2 more
openaire   +1 more source