Phishing Dynamic Evolving Neural Fuzzy Framework for Online Detection “Zero-day” Phishing Email

Almomani, Ammar

doi:10.17485/ijst/2013/v6i1.18

Cited by 49 publications

(10 citation statements)

References 18 publications

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“…Social engineering based phishing attacks are further classified as: spoofed email phishing and fake websites. Furthermore, spoofed email phishing could be spear phishing, if the attack is aimed at a particular person or group of persons or whaling if aimed at a highranking person or employee (Almomani, Gupta and Wan, 2013). Fake websites that have similar visual appearance like a genuine one are equally used to phish a user.…”

Section: Classification Of Phishing Attacksmentioning

confidence: 99%

A Mobile Anti-Phishing System Using Linkguard Algorithm

Amusan¹,

Adedeji²,

Alade³

et al. 2021

FUOYEJET

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Phishing is a type of cyber-attack where the attacker deploys a combination of social engineering and technological skills to trick users into revealing private information like bank account details, usernames and passwords by creating an imitation of an existing web page. This research developed an Android-based anti-phishing system leveraging on the ubiquity of mobile devices and their increasing applications for business and personal purposes. The system was developed by implementing an end-host based algorithm called the Linkguard algorithm which is effective in detecting and preventing both known and unknown phishing attacks. A total of over 500 links which was a blend of both phishing and non-phishing links were collected from Phishtank and Alexa to validate the proposed system which achieved an accuracy of 96%. It is recommended that concerted efforts should be geared towards continuously sensitizing users to improve their phishing awareness as this cybercrime keeps evolving and users play a very crucial role in mitigating it. Keywords— android, cybercrime, Linkguard algorithm, mobile, phishing

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Section: Classification Of Phishing Attacksmentioning

confidence: 99%

A Mobile Anti-Phishing System Using Linkguard Algorithm

Amusan¹,

Adedeji²,

Alade³

et al. 2021

FUOYEJET

View full text Add to dashboard Cite

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“…Several related defensive mechanism can be found in the literature. 39,[43][44][45] In particular, Khonji et al 43 and Almomani et al 44 proposed two defensive mechanisms against user and root access attacks. Another defensive mechanism is proposed by Almomani et al, 45 where the authors presented an email-based filtering method as a defensive mechanism for phishing attacks on both the server side and the client side.…”

Section: Defense Against User and Root Compromise Attacksmentioning

confidence: 99%

Machine learning in cybersecurity: a comprehensive survey

Dasgupta

Akhtar

Sen

2020

Journal of Defense Modeling & Simulation

110

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Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minute millions of data bytes are being generated, processed, exchanged, shared, and utilized to yield outcomes in specific applications. Thus, securing the data, machines (devices), and user’s privacy in cyberspace has become an utmost concern for individuals, business organizations, and national governments. In recent years, machine learning (ML) has been widely employed in cybersecurity, for example, intrusion or malware detection and biometric-based user authentication. However, ML algorithms are vulnerable to attacks both in the training and testing phases, which usually leads to remarkable performance decreases and security breaches. Comparatively, limited studies have been conducted to understand the essence and degree of the vulnerabilities of ML techniques against security threats and their defensive mechanisms. It is imperative to systematize recent works related to cybersecurity using ML to seek the attention of researchers, scientists, and engineers. Therefore, in this paper, we provide a comprehensive survey of the works that have been carried out most recently (from 2013 to 2018) on ML in cybersecurity, describing the basics of cyber-attacks and corresponding defenses, the basics of the most commonly used ML algorithms, and proposed ML and data mining schemes for cybersecurity in terms of features, dimensionality reduction, and classification/detection techniques. In this context, this article also provides an overview of adversarial ML, including the security characteristics of deep learning methods. Finally, open issues and challenges in cybersecurity are highlighted and potential future research directions are discussed.

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“…"Social engineering schemes use spoofed e-mails purporting to be from legitimate businesses and agencies, designed to lead consumers to counterfeit websites that trick recipients into divulging financial data such as usernames and passwords." The phishing based on social engineering is further classified based on [14][15][16][17][18][19]:…”

Section: Taxonomy Of Phishing Attacksmentioning

confidence: 99%

Defending against phishing attacks: taxonomy of methods, current issues and future directions

2017

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Internet technology is so pervasive today, for example, from online social networking to online banking, it has made people's lives more comfortable. Due the growth of Internet technology, security threats to systems and networks are relentlessly inventive. One such a serious threat is "phishing", in which, attackers attempt to steal the user's credentials using fake emails or websites or both. It is true that both industry and academia are working hard to develop solutions to combat against phishing threats. It is therefore very important that organisations to pay attention to end-user awareness in phishing threat prevention.Therefore, aim of our paper is twofold. First, we will discuss the history of phishing attacks and the attackers' motivation in details. Then, we will provide taxonomy of various types of phishing attacks. Second, we will provide taxonomy of various solutions proposed in literature to protect users from phishing based on the attacks identified in our taxonomy. We conclude our paper discussing various issues and challenges that still exist in the literature, which are important to fight against with phishing threats.

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Phishing Dynamic Evolving Neural Fuzzy Framework for Online Detection “Zero-day” Phishing Email

Cited by 49 publications

References 18 publications

A Mobile Anti-Phishing System Using Linkguard Algorithm

A Mobile Anti-Phishing System Using Linkguard Algorithm

Machine learning in cybersecurity: a comprehensive survey

Defending against phishing attacks: taxonomy of methods, current issues and future directions

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