An Empirical Analysis of Machine Learning Techniques in Phishing E-mail detection

Livara, Arriane; Hernandez, Rowell M.

doi:10.1109/iconat53423.2022.9725434

Cited by 15 publications

(6 citation statements)

References 10 publications

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“…The researchers used SVM with the parameters: a kernel function is RBF, Regression Loss is 0.10, One hundred for Iteration Limit, and 0.0010 for Numerical Tolerance. The SVM accuracy of this article is 16.85% which is very poor [2]. The dataset has predefined attributes.…”

Section: Related Workmentioning

confidence: 90%

“…With the ubiquity of the Internet today, society uses Internet products for various things, such as sharing knowledge, socializing, and conducting multiple financial activities, including purchases, advertising, and sending money [1] [2]. This state has led to the emergence of cybercrime; Cybercrime is using computers, communication devices, or networks as a tool for illicit purposes.…”

Section: Introductionmentioning

confidence: 99%

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Phishing Attacks Detection by Using Support Vector Machine

Nabet,

George

2023

J. Al-Qadisiyah Comp. Sci. Math.

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Today's world is heading towards complete digital transformation, and with all its advantages, this transformation involves many risks, the most important of which is phishing. This article proposes a system that extracts features from all parts of the email, initially brought from different data sets, and uses one of the machine learning algorithms (K-means algorithm) to extract the valuable features, as used four methods to calculate the distance in the K-means algorithm. This work used SVM as a classifier to classify emails into phishing and legitimate and tuned its parameters to obtain a high percentage of accuracy. The proposed model gave accuracy equal to 98.8 %.

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Section: Related Workmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Phishing Attacks Detection by Using Support Vector Machine

Nabet,

George

2023

J. Al-Qadisiyah Comp. Sci. Math.

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“…In [39], the unbalanced dataset is divided into phishing and legitimate categories. Then, the training dataset is obtained using 90% of the phishing samples and the same quantity of legitimate samples.…”

Section: Handle Class Imbalance In Web Phishing Classificationmentioning

confidence: 99%

Unbalanced Web Phishing Classification through Deep Reinforcement Learning

Maci,

Santorsola,

Coscia

et al. 2023

Computers

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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.

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“…Several studies [11] represent a pioneering effort in the domain of using natural language processing (NLP) and machine learning (ML) methodologies to identify and recognize phishing emails. In the realm of phishing email detection, it is common to employ machine learning (ML) techniques, specifically clustering and classification methods [12]. Consequently, these techniques rely on the utilization of machine learning-based methodologies, along with machine learning-based assessment criteria.…”

Section: Phishing Email Classification With Classical Machine Learnin...mentioning

confidence: 99%

Lambda Architecture-Based Big Data System for Large-Scale Targeted Social Engineering Email Detection

DEMİREZEN,

SELCEN NAVRUZ

2023

International Journal of Information Security Science

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In this research, we delve deep into the realm of Targeted Social Engineering Email Detection, presenting a novel approach that harnesses the power of Lambda Architecture (LA). Our innovative methodology strategically segments the BERT model into two distinct components: the embedding generator and the classification segment. This segmentation not only optimizes resource consumption but also improves system efficiency, making it a pioneering step in the field. Our empirical findings, derived from a rigorous comparison between the fastText and BERT models, underscore the superior performance of the latter. Specifically, The BERT model has high precision rates for identifying malicious and benign emails, with impressive recall values and F1 scores. Its overall accuracy rate was 0.9988, with a Matthews Correlation Coefficient value of 0.9978. In comparison, the fastText model showed lower precision rates. Leveraging principles reminiscent of the Lambda architecture, our study delves into the performance dynamics of data processing models. The Separated-BERT (Sep-BERT) model emerges as a robust contender, adept at managing both real-time (stream) and large-scale (batch) data processing. Compared to the traditional BERT, Sep-BERT showcased superior efficiency, with reduced memory and CPU consumption across diverse email sizes and ingestion rates. This efficiency, combined with rapid inference times, positions Sep-BERT as a scalable and cost-effective solution, aligning well with the demands of Lambda- inspired architectures. This study marks a significant step forward in the fields of big data and cybersecurity. By introducing a novel methodology and demonstrating its efficacy in detecting targeted social engineering emails, we not only advance the state of knowledge in these domains but also lay a robust foundation for future research endeavors, emphasizing the transformative potential of integrating advanced big data frameworks with machine learning models.

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An Empirical Analysis of Machine Learning Techniques in Phishing E-mail detection

Cited by 15 publications

References 10 publications

Phishing Attacks Detection by Using Support Vector Machine

Phishing Attacks Detection by Using Support Vector Machine

Unbalanced Web Phishing Classification through Deep Reinforcement Learning

Lambda Architecture-Based Big Data System for Large-Scale Targeted Social Engineering Email Detection

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