Detecting Phishing Domains Using Machine Learning

Alnemari, Shouq Mohsen; Alshammari, Majid

doi:10.3390/app13084649

Cited by 36 publications

(9 citation statements)

References 56 publications

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“…Academics are interested in the RF algorithm due to its speed and accuracy in categorization. In predictive modeling and machine learning approaches, the RF involves a collection of supervised learning procedures for regression and classification [27]. It aggregates the results and predictions from multiple decision trees [26] to select the optimal output, as illustrated in Figure 1.…”

Section: • Random Forestmentioning

confidence: 99%

Web Attack Intrusion Detection System Using Machine Learning Techniques

Baklizi,

Atoum,

Alkhazaleh

et al. 2024

Int. J. Onl. Eng.

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Web attacks often target web applications because they can be accessed over a network and often have vulnerabilities. The success of an intrusion detection system (IDS) in detecting web attacks depends on an effective traffic classification system. Several previous studies have utilized machine learning classification methods to create an efficient IDS with various datasets for different types of attacks. This paper utilizes the Canadian Institute for Cyber Security’s (CIC-IDS2017) IDS dataset to assess web attacks. Importantly, the dataset contains 80 attributes of recent assaults, as reported in the 2016 McAfee report. Three machine learning algorithms have been evaluated in this research, namely random forests (RF), k-nearest neighbor (KNN), and naive bayes (NB). The primary goal of this research is to propose an effective machine learning algorithm for the IDS web attacks model. The evaluation compares the performance of three algorithms (RF, KNN, and NB) based on their accuracy and precision in detecting anomalous traffic. The results indicate that the RF outperformed the NB and KNN in terms of average accuracy achieved during the training phase. During the testing phase, the KNN algorithm outperformed others, achieving an average accuracy of 99.4916%. However, RF and KNN achieved 100% average precision and recall rates compared to other algorithms. Finally, the RF and KNN algorithms have been identified as the most effective for detecting IDS web attacks.

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Section: • Random Forestmentioning

confidence: 99%

Web Attack Intrusion Detection System Using Machine Learning Techniques

Baklizi,

Atoum,

Alkhazaleh

et al. 2024

Int. J. Onl. Eng.

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“…In our study, our main focus was to boost the precision of our proposed models by introducing MinMax normalization as a critical preprocessing measure. This technique, widely acknowledged in the realm of machine learning, significantly enhances model accuracy, particularly for specific models that rely on it [49]. By employing MinMax normalization in our suggested model, we effectively rescaled the data to a domain of [0, 1], leading to notable improvements in the input quality during model training (see Eq.…”

Section: The Minmax Normalizationmentioning

confidence: 99%

“…This result vividly illustrates the effectiveness of deep learning methodologies in tackling phishing attacks, showcasing the model's ability to discern malicious websites with a remarkable degree of accuracy. Furthermore, Alnemari et al [49] adopted the Random Forest model, achieving an exceptional accuracy of 97.3% on the UCI dataset. The success of this approach showcases the formidable strength of Random Forest in detecting phishing attacks, operating at a high level of accuracy and outperforming several alternative methods.…”

Section: Findings and Analysismentioning

confidence: 99%

Comparative Study of Machine Learning Algorithms for Phishing Website Detection

Omari

2023

IJACSA

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Phishing, a prevalent online threat where attackers impersonate legitimate organizations to obtain sensitive information from victims, poses a significant cybersecurity challenge. Recent advancements in phishing detection, particularly machine learning-based methods, have shown promising results in countering these malicious attacks. In this study, we developed and compared seven machine learning models, namely Logistic Regression (LR), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Gradient Boosting, to assess their efficiency in detecting phishing domains. Employing the UCI phishing domains dataset as a benchmark, we rigorously evaluated the performance of these models. Our findings indicate that the Gradient Boosting-based model, in conjunction with the Random Forest, exhibits superior performance compared to the other techniques and aligns with existing solutions in the literature. Consequently, it emerges as the most accurate and effective approach for detecting phishing domains.

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“…We took the architecture of deep learning models from [1]- [4] and implemented them. Each of these models is capable of learning from URLs and detecting phishing URLs with high accuracy.…”

Section: Model Evaluation and Comparisonmentioning

confidence: 99%

“…In a phishing attack, an attacker on the internet sets up a fake website or email that looks legitimate with the intention of tricking unsuspecting victims into divulging sensitive information. Because the fraudulent website may look remarkably similar to the real ones, this kind of phishing attack can be challenging to detect [1]. Users can safeguard themselves by being watchful to ensure that the uniform resource locator (URL) of the website they are visiting is correct and by never clicking on links in shady emails.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based phishing website detection

Subhashini,

Banerjee,

Kumar

et al. 2023

TELKOMNIKA

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Phishing attacks use fraudulent websites that trick people into disclosing sensitive information. More effective and precise methods are required to identify phishing websites so that people and organisations can be protected from the damaging effects of these online threats. The aim of this work is to develop a model that can identify phishing uniform resource locator (URLs) more accurately than current approaches while requiring less training time, testing time, and storage space. This research work proposes a novel method for identifying phishing websites using a long short-term memory (LSTM) gated recurrent unit (GRU) algorithm to detect phishing URLs. The accuracy of the suggested method is 98.89%, which is significantly better than the findings of earlier studies. The model also showed a need for shorter training and testing time, and a reduced amount of storage space.

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Detecting Phishing Domains Using Machine Learning

Cited by 36 publications

References 56 publications

Web Attack Intrusion Detection System Using Machine Learning Techniques

Web Attack Intrusion Detection System Using Machine Learning Techniques

Comparative Study of Machine Learning Algorithms for Phishing Website Detection

Deep learning based phishing website detection

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