Detection and Binary Classification of Spear-Phishing Emails in Organizations Using a Hybrid Machine Learning Approach

Akinwale, Popoola Favourite; Jahankhani, Hamid

doi:10.1007/978-3-030-88040-8_9

Cited by 1 publication

(1 citation statement)

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“…Additionally, spam email generation has shifted away from sending several messages over an extended period of time and toward sending massive amounts of spam emails at short intervals, increasing the difficulty of detecting spam emails [28]. Efficient email spam filtering methods should respond actively to the most recent spam threats, junk mail, and bulk emails [29]. Additionally, the expense of network infrastructure must be minimized, and the property rights of employees must be preserved.…”

Section: Introductionmentioning

confidence: 99%

Email Spam detection in IoT based on Hybrid deep learning model and multi-objective optimization by NSGA II

Dehghani

Ahmadinia

Ghafoori

2023

Preprint

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The Internet of Things (IoT) connects a range of things, including sensors, physical devices, controllers, and intelligent computer processors. Physical objects with the ability to organize and control independently are referred to as smart devices in the IoT architecture. These smart devices are becoming an integral aspect of human life, from smart homes to large industrial and organizational sectors. Despite the numerous benefits of email regarding information generation and reminders based on predefined regulations, spam emails sent by thingbots pose a potential concern in the Internet of Things. Recently, several studies have used machine learning systems and deep learning models to detect email spam in the internet of things. The presence of unbalanced data, which impacts classification accuracy, is one of the challenges associated with spam detection. In this study, bidirectional gated recurrent unit (BiGRU) and Convolution neural network (CNN) are combined with the Non-dominated Sorting Genetic Algorithm-II (NSGA II) multi-objective optimization method to effectively address imbalance problems. This solution utilizes the two classification criteria TPR and FPR, as NSGA II objective functions and is capable of resolving the imbalance problem in email spam data. The proposed technique is evaluated using the Enron dataset, and the results indicate that the proposed method is more accurate at detecting spam than other baseline methods.

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Section: Introductionmentioning

confidence: 99%