URLdeepDetect: A Deep Learning Approach for Detecting Malicious URLs Using Semantic Vector Models

Afzal, Sara; Asim, Muhammad; Javed, Abdul Rehman; Beg, Mirza Omer; Baker, Thar

doi:10.1007/s10922-021-09587-8

Cited by 50 publications

(32 citation statements)

References 32 publications

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“…Similar to DNN and CNN, LSTM can be implemented individually [20,[41][42][43][44][45], incorporated with traditional machine learning techniques [46,47], or combined with other DL algorithms in a hybrid model for an improved performance in detecting malicious websites [10,11,31,33,35,36]. Among the studies of LSTM-based phishing detection models, a majority of them specified the parameter settings for neural network architecture, number of epochs, and learning rate; but ignored the dropout rate and batch size [31,41,42,44,47].…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

“…Moreover, only certain parameters were optimized during the fine-tuning process [32,42]. To evaluate the overall performance of LSTM models, four popular metrics were used, being accuracy, precision, recall, and F1-score [31,35,41,44,45]. Other measures training time, detection time, error rate, detection cost, number of epochs per second, etc.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

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Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

Selamat

Krejcar

et al. 2021

Applied Sciences

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Phishing detection with high-performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications’ requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behaviors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. In addition, various performance metrics were measured to evaluate the effectiveness and feasibility of DL models in detecting phishing activities. The results obtained from the experiments showed that no single DL algorithm achieved the best measures across all performance metrics. The empirical findings from this paper also manifest several issues and suggest future research directions related to deep learning in the phishing detection domain.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

Selamat

Krejcar

et al. 2021

Applied Sciences

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“…In the same way, the facial images are compared with criminal face images available in the database, and in case of anomaly, an alert is generated. KNN and SVM in association with face detection classifier were used to achieve the proposed objective [13].…”

Section: Related Workmentioning

confidence: 99%

An Optimized Machine Learning and Big Data Approach to Crime Detection

Palanivinayagam

Shankar

Bhattacharya

et al. 2021

Wireless Communications and Mobile Computing

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Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning-based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset.

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“…Chen et al [8] proposed a new ensemble learning method, which is Extreme Gradient Boosting (XGB). Compared with Gradient Boosted Decision Tree (GBDT), XGB adds a regular term to the objective function to prevent overfitting, and the speed of parallel processing of data is faster.…”

Section: Introductionmentioning

confidence: 99%

An Effective Cost-Sensitive XGBoost Method for Malicious URLs Detection in Imbalanced Dataset

Shen

Peng

et al. 2021

IEEE Access

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Imbalanced class has been a common problem encountered in the modeling process, and has attracted more and more attention from scholars. Biased classifiers, which limit the classifiers' performance for minority classes, will be produced if the imbalanced ratio between the number of positive labels and negative labels is ignored. The synthetic minority over-sampling technique (SMOTE) is a very classic and popular over-sampling method, which is widely used to address this problem. However, SMOTE increases label noise and the training time during the over-sampling process. To improve the detection rate of minority classes while ensuring efficiency, we propose a cost-sensitive XGBoost (CS-XGB) for the imbalanced data problem. The CS-XGB method can reduce the classifiers' preference for most classes without changing the distribution of the original data. 600000 Uniform Resource Locators (URLs) were collected to validate the CS-XGB method. We compare XGBoost (XGB), SMOTE+XGB and CS-XGB, and the experimental results confirm that the CS-XGB is robust and efficient for imbalanced cases.

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URLdeepDetect: A Deep Learning Approach for Detecting Malicious URLs Using Semantic Vector Models

Cited by 50 publications

References 32 publications

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

An Optimized Machine Learning and Big Data Approach to Crime Detection

An Effective Cost-Sensitive XGBoost Method for Malicious URLs Detection in Imbalanced Dataset

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