Waste Flooding: A Phishing Retaliation Tool

Leite, Cristoffer; Gondim, João José Costa; Barreto, Priscila Solís; Alchieri, Eduardo

doi:10.1109/nca.2019.8935018

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…This form contains a tricky link for the website, which the attacker hosts to collect the user's data. When the victim uses this link and interacts with the form by entering the required data, the data becomes under the control of the attacker [33] as clarified in Fig. 2.…”

Section: The Ecosystem Of Phishing Attack Processmentioning

confidence: 99%

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Machine Learning Techniques for Detecting Phishing URL Attacks

Mosa¹,

Shams²,

Abohany³

et al. 2023

Computers, Materials &Amp; Continua

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Cyber Attacks are critical and destructive to all industry sectors. They affect social engineering by allowing unapproved access to a Personal Computer (PC) that breaks the corrupted system and threatens humans. The defense of security requires understanding the nature of Cyber Attacks, so prevention becomes easy and accurate by acquiring sufficient knowledge about various features of Cyber Attacks. Cyber-Security proposes appropriate actions that can handle and block attacks. A phishing attack is one of the cybercrimes in which users follow a link to illegal websites that will persuade them to divulge their private information. One of the online security challenges is the enormous number of daily transactions done via phishing sites. As Cyber-Security have a priority for all organizations, Cyber-Security risks are considered part of an organization's risk management process. This paper presents a survey of different modern machine-learning approaches that handle phishing problems and detect with high-quality accuracy different phishing attacks. A dataset consisting of more than 11000 websites from the Kaggle dataset was utilized and studying the effect of 30 website features and the resulting class label indicating whether or not it is a phishing website (1 or −1). Furthermore, we determined the confusion matrices of Machine Learning models: Neural Networks (NN), Naïve Bayes, and Adaboost, and the results indicated that the accuracies achieved were 90.23%, 92.97%, and 95.43%, respectively.

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Section: The Ecosystem Of Phishing Attack Processmentioning

confidence: 99%

“…The preventive technique introduced by Well-Built authentication. A corrective technique introduced by like site removal [33].…”

Section: Phishing Attacks Typesmentioning

confidence: 99%

Machine Learning Techniques for Detecting Phishing URL Attacks

Mosa¹,

Shams²,

Abohany³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…It is common among other security threats, use as the initial step to gain access to an electronic device for further exploitation without the user awareness. Phishers deceive people and obtain secret information [1], such as usernames, passwords, credit card numbers, and IDs from a victim [36]. It targets the human element of cyber-securitywhich [37]account for 95% of cyber incidents and is used as the initial stages usedin cyber-security breaches [38], [21], [39].According to the UK Cyber-security and Strategy 2016-2021 and world statistics, almost all the successful cyber-attacks have a contributing human influence [40] which is to say that cyber-security is not just about the technology as human knowledge on security is also required for cyber-security stability.…”

Section: Introductionmentioning

confidence: 99%

Phishing Mitigation Techniques: A Literature Survey

Wosah¹,

Win²

2021

IJNSA

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Email is a channel of communication which isconsideredto be a confidential medium of communication for exchange of information among individuals and organisations. The confidentiality consideration about email is no longer the case as attackers send malicious emails to users to deceive them into disclosing their private personal information such as username, password, and bank card details, etc. In search of a solution to combat phishing cybercrime attacks, different approaches have been developed. However, the traditional exiting solutions have beenlimited in assisting email users to identify phishing emails from legitimate ones. This paper reveals the different email and website phishing solutions in phishing attack detection. It first provides a literature analysis of different existing phishing mitigation approaches. It then provides a discussion on the limitations of the techniques, before concluding with anexplorationintohow phishing detection can be improved.

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Developing a Webpage Phishing Attack Detection Tool

Almutairi

Al-Shoshan

2022

Lecture Notes in Networks and Systems

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Waste Flooding: A Phishing Retaliation Tool

Cited by 5 publications

References 9 publications

Machine Learning Techniques for Detecting Phishing URL Attacks

Machine Learning Techniques for Detecting Phishing URL Attacks

Phishing Mitigation Techniques: A Literature Survey

Developing a Webpage Phishing Attack Detection Tool

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