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Understanding Deception

Understanding Deception

Deception is a pervasive aspect of human communication. Empirical studies have shown that most individuals engage in some form of deceit on a daily basis, with approximately 20% of social exchanges involving deceptive elements. Lying follows a developmental trajectory, peaking during adolescence and declining with age, possibly due to the maturation of cognitive control and social accountability.Cognitive and Social Factors in Deception DetectionDespite its prevalence, accurately detecting...

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Detecting phishing websites using machine learning technique.

Ashit Kumar Dutta¹

¹Department of Computer Science and Information System, College of Applied Sciences, Almaarefa University, Riyadh, Saudi Arabia.

|October 11, 2021

Summary

This summary is machine-generated.

This study introduces a machine learning technique using recurrent neural networks to detect phishing URLs, significantly improving upon existing methods for identifying malicious websites and protecting users online.

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Area of Science:

Cybersecurity
Machine Learning
Internet Technologies

Background:

The rise of electronic trading and cloud technologies has increased online security risks.
Phishing attacks, which mimic legitimate sites, exploit users and compromise sensitive data.
Current phishing detection methods are insufficient, leading to a growing number of victims.

Purpose of the Study:

To develop an intelligent technique for detecting phishing URLs.
To enhance user protection against cyber-attacks in the vulnerable online environment.

Main Methods:

A machine learning approach was employed for URL detection.
A recurrent neural network model was specifically utilized to identify phishing URLs.
The method was evaluated using a dataset of 7900 malicious and 5800 legitimate websites.

Main Results:

The proposed recurrent neural network method demonstrated superior performance in detecting malicious URLs.
Experimental outcomes indicate the technique outperforms recent approaches in phishing detection.

Conclusions:

The developed machine learning technique offers a more effective solution for identifying phishing URLs.
This advancement is crucial for improving online security and safeguarding users from cyber threats.