Intelligent phishing url detection using association rule mining

Abstract

Phishing is an online criminal act that occurs when a malicious webpage impersonates as legitimate webpage so as to acquire sensitive information from the user. Phishing attack continues to pose a serious risk for web users and annoying threat within the field of electronic commerce. This paper focuses on discerning the significant features that discriminate between legitimate and phishing URLs. These features are then subjected to associative rule mining—apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. Analyzing the knowledge accessible on phishing URL and considering confidence as an indicator, the features like transport layer security, unavailability of the top level domain in the URL and keyword within the path portion of the URL were found to be sensible indicators for phishing URL. In addition to this number of slashes in the URL, dot in the host portion of the URL and length of the URL are also the key factors for phishing URL.

A novel lightweight URL phishing detection system using SVM and similarity index

“…To perform the phishing website detection their technique needs web pages HTML elements and JavaScript, a task that will be impossible if the attacker blocks the IP (internet protocol) of their Crawler from collecting the needed data. As well as the work of Jeeva et al [9] This phishing detection system acts within two phases, the first procedure leads to a research of the suspect URL in the white list called repository once this last is present in the list, the URL is deemed legitimate, however if the URL doesn't exist in the repository then its subjected to further examination during the second phase of the recognition which consists of an association rule mining algorithm. Finally the research of Ramesh et al [10] which reached an impressive recognition rate of 99.62%.…”

“…Nonetheless, in the literature, several works tackled the phishing attack detection challenge while using artificial intelligence and data mining techniques [5][6][7][8][9] achieving some satisfying recognition rate peaking at 99.62%. However those systems are not optimal to smartphones and other embed devices because of their complex computing and their high battery usage, since they require as entry complete HTML pages or at least HTML links, tags and webpage JavaScript elements some of those systems uses image processing to achieve the recognition.…”

A Study of Phishing URL Detection using Apriori and FP-Tree algorithm

“…Therefore, in this work the data mining technique is used for recognizing the phishing URL patterns. The concept is to analyses and extracts the features from the phishing URLs as training of the algorithm and then utilizes these extracted features to identify the phishing URLs [3]. The investigation of the work involves the study of phish tank datasets.…”

Prediction of Criminal Tendency of High-risk Personnel Based on Combination of Principal Component Analysis and Support Vector Machine

“…Almanie et al [8] presented an Apriori algorithm to obtain frequent crime patterns, and adopted decision trees and naive Bayes classifier methods to help predict crime events at specific time and location. S. Carolin Jeeva et al [9] pointed out the association rules mining (apriori and predictive apriori) for online crimes such as phishing, and the rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. Sujatha and Ezhilmaran [10] provided an effective stress intensity factor mining algorithm for predicting crime locations.…”