A Machine Learning Approach for Phishing Websites Prediction with Novel Feature Selection Framework

Bhowmik, Pankaj; Bhowmik, Pulak Chandra

doi:10.1007/978-981-19-2445-3_24

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…These insights can be used to bolster the security posture of organizations. For instance, our theory-guided feature selection can play a crucial role in offender behaviour analytics (OBA) by enabling analysts to predict the type of cybercriminal social engineering activities from emerging threat patterns that can better inform cyberthreat response (Bhowmik and Bhowmik, 2022). For instance, by prioritising the most prevalent features at a point in time, cybersecurity experts can develop more robust techniques to identify unusual activities and initiate timely incident responses.…”

Section: Theoretical Practical and Methodological Implicationsmentioning

confidence: 99%

“…Scam data involving social engineering techniques, where scammers manipulate individuals into divulging sensitive information, contains a significant amount of noisy content that does not contribute to successful scams but forms a substantial part of the overall dataset (Gupta and Gupta, 2019;Naidoo, 2020). As a subcomponent of TGDS, theory-guided feature selection (TGFS) is the process of incorporating theoretical advances from the problem domain to select relevant features for TGDS models (Bhowmik and Bhowmik, 2022;Miao and Niu, 2016;Slawski et al, 2010). By leveraging existing theories, concepts, principles, and expert insights, behavioural scientists and data scientists can work together to apply theory-guided feature selection to reduce noise in complex cybersecurity datasets (Mao et al, 2019).…”

Section: -Rutherford D Rogermentioning

confidence: 99%

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Theory-Guided Feature Selection in Cybercrime Data Science

Naidoo,

Naidoo

2024

iccws

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Cybercrime data science is being significantly hampered by the presence of 'noisy' features within vast and complex datasets. We draw from the theoretical insights of the behavioural sciences to propose a feature selection model to enrich and improve the value and interpretability of cybercrime intelligence datasets. We piloted our theory-guided feature selection approach on a subset of intelligence datafeeds provided by a global fraud and cybercrime tracking firm. The results of the proposed social influence feature selection model show significant improvement in the interpretability of the machine learning-based exploratory analysis and advanced visualization techniques in an experimental setting. The feature selection model yielded rich insights about cybercriminal psychological tactics from social engineering scam data and has potential applicability in the areas of cyberthreat response and cybersecurity awareness training. Our study shows the value of an interdisciplinary theory-guided approach to cybercrime data analytics that integrates scientific knowledge from the behavioural sciences and data science expertise. Our paper concludes by suggesting avenues for future research on theory-guided feature selection seeking to incorporate behavioural science knowledge in cybercrime data science. We intend to refine, automate, evaluate, and scale our model in future research to assess its effectiveness in producing insights about cybercriminal activities and informing decision-making in a naturalistic and real-time setting. In future research efforts, we aim to automate the encoding of features and apply a wider range of machine learning tools and evaluation metrics to extract more meaningful insights into cybercriminal psychological tactics. We also intend to refine our model on larger datasets to enhance its efficiency and responsiveness to real-time cybercrime data. We call on data scientists and cybercrime domain experts to work together to apply theory-guided feature selection to improve processes of knowledge discovery that enhance our cybersecurity capabilities.

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Section: Theoretical Practical and Methodological Implicationsmentioning

confidence: 99%

Section: -Rutherford D Rogermentioning

confidence: 99%

Theory-Guided Feature Selection in Cybercrime Data Science

Naidoo,

Naidoo

2024

iccws

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“…According to their empirical data, the optimized Random Forest (RFPT) classifier with feature selection by the FSFM outperforms all other strategies. Moreover, a framework for feature selection was described by the authors at [13] , [12]. They presented an empirical hybrid framework with two stages that takes into account the filter and wrapper method.…”

Section: Related Workmentioning

confidence: 99%

From Phishing Behavior Analysis and Feature Selection to Enhance Prediction Rate in Phishing Detection

Omar¹,

Taie²,

Shaheen³

2023

IJACSA

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Phishing incidents have captured the attention of security experts and end users in recent years as they have become more frequent, widespread, and sophisticated. The researchers offered a variety of strategies for detecting phishing attacks. Over time, these approaches suffer from insufficient performance and the inability to identify zero attacks. One of the limitations with these methods is that phishing techniques are constantly evolving, and the proposed methods are not keeping up, making it a hard nut to crack. The objective of this research is to develop a URL phishing detection model that can demonstrate its robustness against constantly changing attacks. One of the most significant contributions of this paper is the selection of a novel combination of features based on literal and recent phishing behavior analysis. This makes the model competent sufficient to recognize zero attacks and able to adjust to changes in phishing attacks. Furthermore, eleven machine learning classification techniques are utilized for classification tasks and comparative objectives. Moreover, three datasets with different instance distributions were constructed at different times for the model's initial construction and evaluation. Several experiments were carried out to investigate and evaluate the proposed model's performance, effectiveness, and robustness. The experiments' findings demonstrated that the GaussianNB method is the most durable, capable of maintaining performance even in the absence of retraining. Additionally, the LightGBM, Random Forest, and GradientBoost algorithms had the highest levels of performance, which they were able to maintain by routinely retraining the model with newer types of attacks. Models that employed these three suggested algorithms outperformed other current detection models with an average accuracy of about 99.7%, making them promising.

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Nature Inspired Metaheuristic Techniques of Firefly and Grey Wolf Algorithms Implemented in Phishing Intrusion Detection Systems

Kopecky,

Dwyer

2023

Lecture Notes in Networks and Systems

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A Machine Learning Approach for Phishing Websites Prediction with Novel Feature Selection Framework

Cited by 5 publications

References 18 publications

Theory-Guided Feature Selection in Cybercrime Data Science

Theory-Guided Feature Selection in Cybercrime Data Science

From Phishing Behavior Analysis and Feature Selection to Enhance Prediction Rate in Phishing Detection

Nature Inspired Metaheuristic Techniques of Firefly and Grey Wolf Algorithms Implemented in Phishing Intrusion Detection Systems

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