An Effective Phishing Site Prediction using Machine Learning

Pandey, Aman Raj; Sharma, Tushar; Basnet, Subarna; Kumar, Ankesh; Setia, Sonia

doi:10.1109/icccis56430.2022.10037744

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…The goal of these simulations is to provide organizations with a more comprehensive understanding of the potential risks posed by social engineering attacks so that they can take appropriate steps to mitigate or prevent them. This may involve improving their security awareness training programs, enhancing their technical security controls, or developing more effective incident response plans (Pandey et al, 2022).…”

Section: Mathematical Modelmentioning

confidence: 99%

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A comprehensive survey on social engineering-based attacks on social networks

Naz,

Sarwar,

Kaleem

et al. 2024

Int. j. adv. appl. sci.

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Threats based on social engineering in social networks are becoming a more common problem. Social engineering is a type of attack that relies on trickery and exploiting human psychology to gain access to confidential information or resources. It involves deceptive techniques like phishing, pretexting, and baiting, tricking individuals into revealing sensitive information or performing specific actions. These tactics can lead to unauthorized access to user accounts, identity theft, or the distribution of harmful content. This study offers a detailed review of threats related to social engineering on social networks. It explores various social engineering attacks, the methods used to execute these threats, and measures that can be adopted to minimize the risk of becoming a victim. The research aimed to develop a new, broad classification of social engineering attacks and strategies for responding to them. It also examines the challenges that social engineering poses to algorithms on social media platforms and highlights the need for more research. The study concludes by pointing out the shortcomings of current approaches and suggesting future research directions, stressing the importance of standardized protective measures and increasing awareness among potential victims. This thorough examination improves our understanding of social engineering attacks and encourages the development of innovative solutions and ethical practices, contributing to a more secure digital environment.

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Section: Mathematical Modelmentioning

confidence: 99%

“…Classification metrics SEAM (Sánchez-Paniagua et al, 2022) Preparation, delivery, and exploitation, set of questions SEAMv2 (Abroshan et al, 2021a) Preparation, delivery, and exploitation, set of Questions, vector attack, the flow of actions Mathematical model (Pandey et al, 2022) Analyzing risk vectors through the link…”

Section: Modelmentioning

confidence: 99%