Fonte: “Unbalanced Web Phishing Classification through Deep Reinforcement Learning”, Paper,


Web phishing is a form of cybercrime aimed at tricking people into visiting malicious URLs to exfiltrate sensitive data.

Since the structure of a malicious URL evolves over time, phishing detection mechanisms that can adapt to such variations are paramount. Furthermore, web phishing detection is an unbalanced classification task, as legitimate URLs outnumber malicious ones in real-life cases.

Deep learning (DL) has emerged as a promising technique to minimize concept drift to enhance web phishing detection. Deep reinforcement learning (DRL) combines DL with reinforcement learning (RL); that is, a sequential decision-making paradigm in which the problem to be addressed is expressed as a Markov decision process (MDP). Recent studies have proposed an ad hoc MDP formulation to tackle unbalanced classification tasks called the imbalanced classification Markov decision process (ICMDP). In this paper, we exploit the ICMDP to present a double deep Q-Network (DDQN)-based classifier to address the unbalanced web phishing classification problem.

The proposed algorithm is evaluated on a Mendeley web phishing dataset, from which three different data imbalance scenarios are generated. Despite a significant training time, it results in better geometric mean, index of balanced accuracy, F1 score, and area under the ROC curve than other DL-based classifiers combined with data-level sampling techniques in all test cases.


Despite the proliferation of alternative communication tools, such as electronic messages, mobile applications, and social media channels, email remains a popular communication method. As business-critical email volumes grow, the need for automated malicious email recognition tools, such as phishing email detectors and filters, increases.

The aim of phishing is to fool users by posing as other subjects to steal confidential data. The concept drift identifies non-predictable and frequent time-dependent evolution of some streams of data, resulting in the absence of stationary data models.

This is a common scenario for web data, such as phishing URLs, since these are often ephemeral. Therefore, the detection techniques that are now effective may no longer be suitable in the future. machine learning (ML) has proven to be beneficial in addressing the phishing URL classification problem, since an ML-based system is able to generalize, minimizing concept drift, as observed in.

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